### PNNL

- 20792

Prepared for the U.S. Department of Energy u nder Contract DE

- AC05

- 76RL01830



# Evaluation of Representative Smart Grid Investment Grant Project Technologies: Distributed Generation

R Singh B Vyakaranam

February 2012DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty , express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights . Referen ce herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency there of, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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### P NNL -20792



# Evaluation of Smart Grid Investment Grant P roject Technologies: Distributed Generation

R Singh B Vyakaranam

F ebruary 2012

P repared for the U.S. Department of Energy under Contract DE- AC05 -76RL01830

P acific Northwest National Laboratory Richland, Washington 99352

### iii 

## Table of Contents

TABLE OF FIGURES

................................ ................................ ................................ ................................ .......... VIII

TABLE OF TABLES

................................ ................................ ................................ ................................ ........ XVIII

SUMMARY

................................ ................................ ................................ ................................ ............................... 1 1 INTRODUCTION

................................ ................................ ................................ ................................ .................. 2 1.1 REPORT SCOPE

................................ ................................ ................................ ................................ ................. 3 1.1.1 DIRECT REPRESENTATIVE TECHN OLOGIES

................................ ................................ .......................... 3 1.1.2 ENABLING TECHN OLOGIES

................................ ................................ ................................ ....................... 4 1.1.2.1 SMART METERS

................................ ................................ ................................ ................................ ......... 4 1.1.2.2 COMMUNICATIO NS INFRASTRUCTURE

................................ ................................ .............................. 5 1.1.2.3 HUMAN MACHIN E INTERFACE

................................ ................................ ................................ .............. 5 1.2 REPORT STRUCTURE

................................ ................................ ................................ ................................ ...... 5

2 DISTR IBUTED GENERATION TE CHNOLOGY AREAS

................................ ................................ .................. 6 2.1 PHOTOVOLTAIC (PV ) MODEL IMPLEMENTATI ON

................................ ................................ .................. 7 2.1.1 RESIDENTIAL PV S

................................ ................................ ................................ ................................ ........ 8 2.1.1.1 SGIG IMPACT METRICS AFFECTED BY RESIDENTIAL PVS

................................ ............................. 9 2.1.1.2 SPECIFIC IMP LEMENTATION OF RESID ENTIAL PVS

................................ ................................ ....... 10 2.1.1.3 OVERALL RESI DENTIAL PVS SIMULATI ON RESULTS

................................ ................................ .... 11 2.1.1.4 ANNUAL PEAK LOAD

................................ ................................ ................................ ............................. 11 2.1.1.5 ANNUAL ENERG Y CONSUMPTION

................................ ................................ ................................ ...... 14 2.1.1.6 ANNUAL SYSTE M LOSSES

................................ ................................ ................................ ..................... 17 2.1.1.7 POWER FACTOR IMPACT

................................ ................................ ................................ ....................... 19 2.1.1.8 ANNUAL RESID ENTIAL PV OUTPUT

................................ ................................ ................................ ... 21 2.1.1.9 ANNUAL CO 2 EMISSIONS

................................ ................................ ................................ ....................... 22 2.1.2 COMMERCIAL PVS

................................ ................................ ................................ ................................ ..... 25 2.1.2.1 SGIG METRICS AFFECTED BY COMMERCIAL PVS

................................ ................................ .......... 25 2.1.2.2 SPECIFIC IMP LEMENTATION OF COMME RCIAL PV

................................ ................................ ........ 26 2.1.2.3 HIGH LEVEL C OMMERCIAL PV SIMULAT ION RESULTS

................................ ................................ 26 2.1.2.4 ANNUAL PEAK LOAD

................................ ................................ ................................ ............................. 26 2.1.2.5 ANNUAL ENERG Y CONSUMPTION

................................ ................................ ................................ ...... 29 2.1.2.6 ANNUAL SYSTE M LOSSES

................................ ................................ ................................ ..................... 31 2.1.2.7 POWER FACTOR IMPACT

................................ ................................ ................................ ....................... 33 2.1.2.8 ANNUAL COMME RCIAL PV OUTPUT

................................ ................................ ................................ .. 34 2.1.2.9 ANNUAL CO 2 EMISSIONS

................................ ................................ ................................ ....................... 35 2.1.3 ADDITION OF BO TH COMMERCIAL AND RE SIDENTIAL (COMBINED) PVS

................................ .. 37 2.1.3.1 SGIG METRICS AFFECTED BY COMBINED PVS

................................ ................................ ................ 37 2.1.3.2 SPECIFIC IMP LEMENTATION OF COMBI NED PV

................................ ................................ .............. 38 2.1.3.3 HIGH LEVEL C OMBINED PV SIMULATIO N RESULT S

................................ ................................ ...... 39 2.1.3.4 ANNUAL PEAK LOAD

................................ ................................ ................................ ............................. 39 2.1.3.5 ANNUAL ENERG Y CONSUMPTION

................................ ................................ ................................ ...... 42 2.1.3.6 ANNUAL SYSTE M LOSSES

................................ ................................ ................................ ..................... 44 2.1.3.7 POWER FACTOR IMPACT

................................ ................................ ................................ ....................... 46 2.1.3.8 ANNUAL COMBI NED PV OUTPUT

................................ ................................ ................................ ........ 47

### iv 2.1.3.9 ANNUAL CO 2 EMISSIONS

................................ ................................ ................................ ....................... 48 2.2 ADDITION OF COMM ERCIAL WTG

................................ ................................ ................................ ............ 50 2.2.1 SGIG METRICS A FFECTED BY COMMERCIA L WTG

................................ ................................ ........... 51 2.2.2 SPECIFIC IMPLEMENTAT ION OF COMMERCIAL WT G

................................ ................................ ....... 51 2.2.3 HIGH LEVEL COM MERCIAL WTG RESULTS

................................ ................................ ......................... 52 2.2.3.1 ANNUAL PEAK LOAD

................................ ................................ ................................ ............................. 53 2.2.3.2 ANNUAL E NERGY CONSUMPTION

................................ ................................ ................................ ...... 55 2.2.3.3 ANNUAL SYSTE M LOSSES

................................ ................................ ................................ ..................... 57 2.2.3.4 ANNUAL WIND POWER OUTPUT

................................ ................................ ................................ ......... 59 2.2.3.5 ANNUAL CO 2 EMISSIONS

................................ ................................ ................................ ....................... 59

3 DETAILED INDIVIDUA L PROTOTYPICAL FEEDE R RESULTS

................................ ................................ . 62 3.1 RESIDENTIAL PV

................................ ................................ ................................ ................................ ........... 62 3.1.1 EXAMPLE FEEDER R1

- 12.47

- 1

................................ ................................ ................................ .................. 62 3.2 COMMERCIAL PV

................................ ................................ ................................ ................................ .......... 67 3.2.1 EXAMPLE FEEDER GC

- 12

- 47

- 1_R1

................................ ................................ ................................ ........... 67 3.3 COMBINED PV

................................ ................................ ................................ ................................ ................ 72 3.3.1 EXAMPLE FEEDER R5

- 35.00

- 1

................................ ................................ ................................ .................. 72 3.4 COMMERCIAL WTG

................................ ................................ ................................ ................................ ...... 77 3.4.1 EXAMPLE FEEDER GC

- 12 .47

- 1_R2

................................ ................................ ................................ ........... 77

4 SGIG IMPACT METRIC VALUES

................................ ................................ ................................ .................... 82 4.1 RESIDENTIAL PV I MPACT METRICS

................................ ................................ ................................ ......... 82 4.2 COMMERCIAL PV IM PACT METRICS

................................ ................................ ................................ ........ 88 4.3 COMBINED PV IMPA CT METRICS

................................ ................................ ................................ .............. 93 4.4 COMMERCIAL WTG I MPACT METRICS

................................ ................................ ................................ .... 98

5 CONCLUSION AND OBSERVATIONS

................................ ................................ ................................ .......... 103 5.1 DISTRIBUTED GENE RATION OBSERVATIONS AND CONCLUSIONS

................................ ............... 103 5.2 DISTRIBUTED GENE RATION OBSERVATIONS AND CONCLUSIONS SUMM ARY

.......................... 104 5.2.1 OBSERVATIONS A ND CONCLUSIONS SUMMA RY FOR PV INTEGRATIO N

................................ .. 105 5.2.2 OBSERVATIONS A ND CONCLUSIONS SUMMA RY FOR WTG I NTEGRATION

.............................. 106

APPENDIX A: SGIG PRO GRAM IMPACT METRICS

................................ ................................ ...................... 107

APPENDIX B: TAXONOMY OF PROTOTYPICAL DIST RIBUTION FEEDERS

................................ ........... 116 B.1 E ND

- USE L OAD M ODELS

................................ ................................ ................................ ................................ .. 117 B.1.1 ZIP L OADS

................................ ................................ ................................ ................................ .................... 118 B.1.2 S INGLE

- S TATE D ETAILED P HYSICAL M ODELS

................................ ................................ .............................. 120 B.1.3 M ULTI

- S TATE D ETAILED P HYSICAL M ODELS

................................ ................................ ............................... 123 B.2 M ODEL E XTRACTION AND P OPULATION

................................ ................................ ................................ .......... 126 B.2.1 R ESIDENTIAL L OADS

................................ ................................ ................................ ................................ ..... 126 B.2.2 C OMMERCIAL L OADS

................................ ................................ ................................ ................................ .... 134 B.3 T AXONOMY F EEDER E MISSION P ROFILES

................................ ................................ ................................ ........ 140 B.4 T AXONOMY F EEDER D ESCRIPTIONS

................................ ................................ ................................ ................. 145 B.4.1 F EEDER 1: GC

- 12.47

- 1

................................ ................................ ................................ ................................ .. 145 B.4.2 F EEDER 2: R1

- 12.47

- 1

................................ ................................ ................................ ................................ ... 146 B.4.3 F EEDER 3: R1

- 12.47

- 2

................................ ................................ ................................ ................................ ... 146

### v B.4.4 F EEDER 4: R1

- 12.47

- 3

................................ ................................ ................................ ................................ ... 146 B.4.5 F EEDER 5: R1

- 12.47

- 4

................................ ................................ ................................ ................................ ... 146 B.4.6 F EEDER 6: R1

- 25.00

- 1

................................ ................................ ................................ ................................ ... 147 B.4.7 F EEDER 7: R2

- 12.47

- 1

................................ ................................ ................................ ................................ ... 147 B.4.8 F EEDER 8: R2

- 12.47

- 2

................................ ................................ ................................ ................................ ... 147 B.4.9 F EEDER 9: R2

- 12.47

- 3

................................ ................................ ................................ ................................ ... 147 B.4.10 F EEDER 10: R2

- 25.00

- 1

................................ ................................ ................................ ............................... 148 B.4.11 F EEDER 11: R2

- 35.00

- 1

................................ ................................ ................................ ............................... 148 B.4.12 F EEDER 12: R3

- 12.47

- 1

................................ ................................ ................................ ............................... 148 B.4.13 F EEDER 13: R3

- 12.47

- 2

................................ ................................ ................................ ............................... 148 B.4.14 F EEDER 14: R3

- 12.47

- 3

................................ ................................ ................................ ............................... 149 B.4.15 F EEDER 15: R4

- 12.47

- 1

................................ ................................ ................................ ............................... 149 B.4.16 F EEDER 16: R4

- 12.47

- 2

................................ ................................ ................................ ............................... 149 B.4.17 F EEDER 17: R4

- 25.00

- 1

................................ ................................ ................................ ............................... 149 B.4.18 F EEDER 18: R5

- 12.47

- 1

................................ ................................ ................................ ............................... 150 B.4.19 F EEDER 19: R5

- 12.47

- 2

................................ ................................ ................................ ............................... 150 B.4.20 F EEDER 20: R5

- 12.47

- 3

................................ ................................ ................................ ............................... 150 B.4.21 F EEDER 21: R5

- 12.47

- 4

................................ ................................ ................................ ............................... 150 B.4.22 F EEDER 22: R5

- 12.47

- 5

................................ ................................ ................................ ............................... 151 B.4.23 F EEDER 23: R5

- 25.00

- 1

................................ ................................ ................................ ............................... 151 B.4.24 F EEDER 24: R5

- 35.00

- 1

................................ ................................ ................................ ............................... 151

APPENDIX C: SIMULATI ON TECHNOLOGY AND ME THODOLOGY

................................ ........................ 152

APPENDIX D: PLOTS FO R INDIVIDUAL FEEDER RESULTS

................................ ................................ ...... 155 D.1 A DDITION OF R ESIDENTIAL PV S PLOTS

................................ ................................ ................................ ............ 155 D.1.1 D ETAILED R ESIDENTIAL PV P LOTS FOR R1

- 12.47

- 1

................................ ................................ ..................... 155 D.1.2 D ETAILED R ESIDENTIAL PV P LOTS FOR R1

- 12.47

- 2

................................ ................................ ..................... 155 D.1.3 D ETAILED R ES IDENTIAL PV P LOTS FOR R1

- 12.47

- 3

................................ ................................ ..................... 158 D.1.4 D ETAILED R ESIDENTIAL PV P LOTS FOR R1

- 12.47

- 4

................................ ................................ ..................... 160 D.1.5 D ETAILED R ESIDENTIAL PV P LOTS FOR R1

- 25.00

- 1

................................ ................................ ..................... 163 D.1.6 D ETAILED R ESIDENTIAL PV P LOTS FOR R2

- 12.47

- 1

................................ ................................ ..................... 165 D.1.7 D ETAILED R ESIDENTIAL PV P LOTS FOR R2

- 12.47

- 2

................................ ................................ ..................... 168 D.1.8 D ETAILED R ESIDENTIAL PV P LOTS FOR R2

- 12.47

- 3

................................ ................................ ..................... 170 D.1.9 D ETAILED R ESIDENTIAL PV P LOTS FOR R2

- 25.00

- 1

................................ ................................ ..................... 173 D.1.10 D ETAILED R ESIDENTIAL PV P LOTS FOR R2

- 35.00

- 1

................................ ................................ ................... 175 D.1.11 D ETAILED R ESIDENTIAL PV P LOTS FOR R3

- 12.47

- 1

................................ ................................ ................... 178 D.1.12 D ETAILED R ESIDENTIAL PV P LOTS FOR R3

- 12.47

- 3

................................ ................................ ................... 181 D.1.13 D ETAILED R ESIDENTIAL PV P LOTS FOR R4

- 12.47

- 1

................................ ................................ ................... 183 D.1.14 D ETAILED R ESIDENTIAL PV P LOTS FOR R4

- 12.47

- 2

................................ ................................ ................... 186 D.1.15 D ETAILED R ESIDENTIAL PV P LOTS FOR R4

- 25.00

- 1

................................ ................................ ................... 188 D.1.16 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 12.47

- 1

................................ ................................ ................... 191 D.1.17 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 12.47

- 2

................................ ................................ ................... 194 D.1.18 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 12.47

- 3

................................ ................................ ................... 196 D.1.19 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 12.47

- 4

................................ ................................ ................... 199 D.1.20 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 12.47

- 5

................................ ................................ ................... 201 D.1.21 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 25.00

- 1

................................ ................................ ................... 204 D.1.22 D ETAILED R ESIDENTIAL PV P LOTS FOR R5

- 35.00

- 1

................................ ................................ ................... 206

### vi D.2 A DDITION OF C OMMERCIAL PV P LOTS

................................ ................................ ................................ ............ 209 D.2.1 D ETAILED C OMMERCIAL PV P LOTS FOR GC

- 12.47

- 1_R1

................................ ................................ ............ 209 D.2.2 D ETAILED C OMMERCIAL PV P LOTS FOR R1

- 12.47

- 1

................................ ................................ .................... 209 D.2.3 D ETAILED C OMMERCIAL PV P LOTS FOR R1

- 12.47

- 2

................................ ................................ .................... 212 D.2.4 D ETAILED C OMMERCIAL PV P LOTS FOR R1

- 12.47

- 3

................................ ................................ .................... 214 D.2.5 D ETAILED C OMMERCIAL PV P LOTS FOR R1

- 12.47

- 4

................................ ................................ .................... 217 D.2.6 D ETAILED C OMMERCIAL PV P LOTS FOR R1

- 25.00

- 1

................................ ................................ .................... 219 D.2.7 D ETAILED C OMMERCIAL PV P LOTS FOR GC

- 12.47

- 1_R2

................................ ................................ ............ 222 D.2.8 D ETAILED C OMMERCIAL PV FOR R2

- 12.47

- 1

................................ ................................ ............................... 224 D.2.9 D ETAILED C OMMERCIAL PV FOR R2

- 12.47

- 2

................................ ................................ ............................... 227 D.2.10 D ETAILED C OMMERCIAL PV FOR R2

- 25.00

- 1

................................ ................................ ............................. 229 D.2.11 D ETAILED C OMMERCIAL PV FOR R2

- 35.00

- 1

................................ ................................ ............................. 232 D.2.12 D ETAILED C OMMERCIAL PV FOR GC

- 12.47

- 1_R3

................................ ................................ ..................... 234 D.2.13 D ETAILED C OMMERCIAL PV P LOTS FOR R3

- 12.47

- 1

................................ ................................ .................. 237 D.2.14 D ETAILED C OMMERCIAL PV P LOTS FOR R3

- 12.47

- 2

................................ ................................ .................. 239 D.2.15 D ETAILED C OMMERCIAL PV P LOTS FOR R3

- 12.47

- 3

................................ ................................ .................. 242 D.2.16 D ETAILED C OMMERCIAL PV P LOTS FOR GC

- 12.47

- 1 _R4

................................ ................................ .......... 245 D.2.17 D ETAILED C OMMERCIAL PV P LOTS FOR R4

- 12.47

- 1

................................ ................................ .................. 247 D.2.18 D ETAILED C OMMERCIAL PV P LOTS FOR R4

- 12.47

- 2

................................ ................................ .................. 250 D.2.19 D ETAILED C OMMERCIAL PV P LOTS FOR R4

- 25.00

- 1

................................ ................................ .................. 253 D.2.20 D ETAILED C OMMERCIAL PV P LOTS FOR GC

- 12.47

- 1_R5

................................ ................................ .......... 256 D.2.21 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 12.47

- 1

................................ ................................ .................. 258 D.2.22 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 12.47

- 2

................................ ................................ .................. 261 D.2.23 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 12.47

- 3

................................ ................................ .................. 263 D.2.24 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 12.47

- 4

................................ ................................ .................. 266 D.2.25 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 12.47

- 5

................................ ................................ .................. 268 D.2.26 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 25.00

- 1

................................ ................................ .................. 271 D.2.27 D ETAILED C OMMERCIAL PV P LOTS FOR R5

- 35.00

- 1

................................ ................................ .................. 274 D.3 A DDITION OF C OMBINED PV

................................ ................................ ................................ ............................ 2 76 D.3.1 D ETAILED C OMBINED PV P LOTS FOR R1

- 12.47

- 1

................................ ................................ ........................ 276 D.3.2 D ETAILED C OMBINED PV P LOTS FOR R1

- 12.47

- 2

................................ ................................ ........................ 278 D.3.3 D ETAILED C OMBINED PV P LOTS FOR R1

- 12.47

- 3

................................ ................................ ........................ 281 D.3.4 D ETAILED C OMBINED PV P LOTS FOR R1

- 12.47

- 4

................................ ................................ ........................ 283 D.3.5 D ETAILED C OMBINED PV P LOTS FOR R1

- 25.00

- 1

................................ ................................ ........................ 286 D.3.6 D ETAILED C OMBINED PV P LOTS FOR R2

- 12.47

- 1

................................ ................................ ........................ 288 D.3.7 D ETAILED C OMBINED PV P LOTS FOR R2

- 12.47

- 2

................................ ................................ ........................ 291 D.3.8 D ETAILED C OMBINED PV P LOTS FOR R2

- 25.00

- 1

................................ ................................ ........................ 293 D.3.9 D ETAILED C OMBINED PV P LOTS FOR R2

- 35.00

- 1

................................ ................................ ........................ 296 D.3.10 D ETAILED C OMBINED PV P LOTS FOR R3

- 12.47

- 1

................................ ................................ ...................... 298 D.3.11 D ETAILED C OMBINED PV P LOTS FOR R3

- 12.47

- 3

................................ ................................ ...................... 301 D.3.12 D ETAILED C OMBINED PV P LOTS FOR R4

- 12.47

- 1

................................ ................................ ...................... 303 D.3.13 D ETAILED C OMBINED PV P LOTS FOR R4

- 12.47

- 2

................................ ................................ ...................... 306 D.3.14 D ETAILED C OMBINED PV P LOTS FOR R4

- 25.00

- 1

................................ ................................ ...................... 308 D.3.15 D ETAILED C OMBINED PV P LOTS FOR R5

- 12.47

- 1

................................ ................................ ...................... 311 D.3.16 D ETAILED C OMBINED PV P LOTS FOR R5

- 12.47

- 2

................................ ................................ ...................... 313 D.3.17 D ETAILED C OMBINED PV P LOTS FOR R5

- 12.47

- 3

................................ ................................ ...................... 316 D.3.18 D ETAILED C OMBINED PV P LOTS FOR R5

- 12.47

- 4

................................ ................................ ...................... 318 D.3.19 D ETAILED C OMBINED PV P LOTS FOR R5

- 12.47

- 5

................................ ................................ ...................... 321

### vii D.3.20 D ETAILED C OMBINED PV P LOTS FOR R5

- 25.00

- 1

................................ ................................ ...................... 323 D.3.21 D ETAILED C OMBINED PV P LOTS FOR R5

- 35.00

- 1

................................ ................................ ...................... 326 D.4 A DDITION OF C OMMERCIAL WTG

................................ ................................ ................................ ................... 326 D.4.1 D ETAILED C OMMERCIAL WTG PLOTS FOR GC

- 12.47

- 1_R1

................................ ................................ ......... 326 D.4.2 D ETAILED C OMMERCIAL WTG PLOTS FOR GC

- 12.47

- 1_R2

................................ ................................ ......... 329 D.4.3 D ETAILED C OMMERCIAL WTG PLOTS FOR GC

- 12.47

- 1_R3

................................ ................................ ......... 329 D.4.4 D ETAILED C OMMERCIAL WTG PLOTS FOR GC

- 12.47

- 1_R4

................................ ................................ ......... 331 D.4.5 D ETAILED C OMMERCIAL WTG PLOTS FOR GC

- 12.47

- 1_R5

................................ ................................ ......... 334

APPENDIX E: INDIVIDU AL FEEDER IMPACT MET RICS

................................ ................................ ............ 335 E.1 I NDIVIDUAL P ERFORMANCE M ETRICS FOR B ASE C ASE

................................ ................................ .................... 335 E.2 I NDIVIDUAL R ESIDENTIAL PV P ERFORMANCE M ETRICS

................................ ................................ .................. 341 E.3 I NDIVIDUAL C OMMERCIAL PV P ERFORMANCE M ETRICS

................................ ................................ ................. 345 E.4 I NDIVIDUAL C OMBINED PV P ERFORMANCE M ETRICS

................................ ................................ ...................... 350 E.5 I NDIVIDUAL C OMMERCIAL WTG P ERFORMANCE M ETRICS

................................ ................................ ............. 355

REFERENCES

................................ ................................ ................................ ................................ ...................... 361

### viii 

## Table of Figure s Figure 2.1. Climate Zones Used for Development of Prototypical Feeders

................................ ............................. 10 Figure 2.2: Comparison o f peak load by feeder

................................ ................................ ................................ ....... 12 Figure 2.3: Change in peak load on feeders in (MW)

................................ ................................ .............................. 13 Figure 2.4: Change in peak demand by feeder (%)

................................ ................................ ................................ .. 13 Figure 2.5: Comparison of annual energy consumption by feeder

................................ ................................ .......... 15 Figure 2.6 : Change in annual energy consumption by feeder (MWh)

................................ ................................ .... 16 Figure 2.7 : Change in annua l energy consumption for feeders (%)

................................ ................................ ........ 16 Figure 2.8: Comparison of annual losses by feeder

................................ ................................ ................................ . 17 Figure 2.9: Change in annual losses by feeder (MWh)

................................ ................................ ............................ 18 Figure 2.10: Change in annual losses by feeder (%)

................................ ................................ ................................ 19 Figure 2.11 : Comparison of average annual power factor by feeder

................................ ................................ ...... 20 Figure 2.12 : Comparison of minimum an nual power factor by feeder

................................ ................................ ... 21 Figure 2.13: Annual residential PV power output by feeder

................................ ................................ .................... 22 Figure 2.14: Comparison of total annual CO 2 emission by feeder

................................ ................................ ........... 23 Figure 2.15: Change in total annual CO 2 emissions by feeder (tons)

................................ ................................ ...... 24 Figure 2.16: Change in total annual CO 2 emissions by feeder (%)

................................ ................................ .......... 24 Figure 2.17 : Comparison of peak load by feeders

................................ ................................ ................................ .. 27 Figure 2.18: Change in peak load by feeder (MW)

................................ ................................ ................................ .. 28 Figure 2.19: Change in peak load by feeder (%)

................................ ................................ ................................ ...... 28 Figure 2.20 : Comparison of annual energy consumption by feeder (MWh)

................................ ........................... 29 Figure 2.21 : Change in annual energy consumption by feeder (GWh)

................................ ................................ ... 30 Figure 2.22 : Change in annual energy consumption by feeders (%)

................................ ................................ ...... 30 Figure 2.23: Comparison of total annual losses by feeder

................................ ................................ ....................... 31 Figure 2.24: Change in annual losses by feeder (MWh)

................................ ................................ .......................... 32 Figure 2.25: Change in annual losses by feeder (%)

................................ ................................ ................................ 32 Figure 2.26: Comparison of average annual power factor by feeder

................................ ................................ ....... 33 Figure 2.27: Comparison of minimum annua l power factor by feeders

................................ ................................ ... 34 Figure 2.28: Annual commercial PV output by feeder

................................ ................................ ............................ 35 Figure 2.29 : Change in total annual CO 2 emissions by feeder (kilotons)

................................ ............................... 36 Figure 2.30: Change in total annual CO 2 emissions by feeder (tons)

................................ ................................ ...... 36 Figure 2.31: Change in total annual CO 2 emissions by feeder (%)

................................ ................................ .......... 37 Figure 2.32 Climate Zone s Used for Development of Prototypical Feeders

................................ ............................ 39 Figure 2.33 : Comparison of peak load by feeder

................................ ................................ ................................ .... 40 Figure 2.34 : Change in peak load by feeder (MW)

................................ ................................ ................................ . 41 Figure 2.35: Change in peak load by feeder (%)

................................ ................................ ................................ ...... 41 Figure 2.36: Comparison of annual energy consumption by feed er (GWh)

................................ ............................ 42 Figure 2.37: Change in annual energy consumption by feeder (GWh)

................................ ................................ .... 43 Figure 2.38: Change in annual energy consumption by feeder (%)

................................ ................................ ......... 43 Figure 2.39: Comparison of total annual losses by feeder (MWh)

................................ ................................ .......... 44 Figure 2.40: Change in annual losses by feeder (GWh)

................................ ................................ ........................... 45 Figure 2.41: Change in annual losses by feeder (%)

................................ ................................ ................................ 45 Figure 2.42 : Comparison of annual average power factor by feeder

................................ ................................ ...... 46 Figure 2.43: Comparison of minimum annual power factor by feeder

................................ ................................ .... 47 Figure 2.44: Annual combined PV output by feeder

................................ ................................ ............................... 48 Figure 2.45: Comparison of annual CO 2 emission by feeder

................................ ................................ ................... 49 Figure 2.46: Change in annual CO 2 emission by feed er (kilotons)

................................ ................................ .......... 49

### ix Figure 2.47: Change in annual CO 2 emission by feeder (%)

................................ ................................ ................... 50 Figure 2.48: Comparison of peak load by feeder

................................ ................................ ................................ ..... 53 Figure 2.49: Change in peak load by feeder (MW)

................................ ................................ ................................ .. 54 Figure 2.50: Change in peak load by feeder (%)

................................ ................................ ................................ ...... 54 Figure 2.51: Comparison of annual energy consumption by feeder (GWh)

................................ ............................ 55 Figure 2.52: Change in annual energy consumption by feeder (GWh)

................................ ................................ .... 56 Figure 2.53: Change in annual energy consumption by feeder (%)

................................ ................................ ......... 56 Figure 2.54: Comparison of total annual losses by feeder

................................ ................................ ....................... 57 Figure 2.55: Change in annual losses by feeder (MWh)

................................ ................................ .......................... 58 Figure 2.56: Change in annual losses by feeder (%)

................................ ................................ ................................ 58 Figure 2.57 : Comparison of annual wind output by feeders

................................ ................................ ................... 59 Figure 2.58 : Comparison of annual CO 2 emission by feeders

................................ ................................ ................ 60 Figure 2.59 : Change in annual CO 2 emi ssion by feeders (tons)

................................ ................................ .............. 60 Figure 2.60 : Change in annual CO 2 emission by feeders (%)

................................ ................................ ................. 61
Figure 3.1: Comparison of peak load by month for R1

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................................ ................................ .............. 63 Figure 3.2: Comparison of energy consumption by month for R1

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................................ ............................. 64 Figure 3.3: Comparison of losses by month for R1

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- 1

................................ ................................ .................... 65 Figure 3.4: Comparison of PV o utput by month for R1

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................................ ................................ ............. 66 Figure 3.5: Comparison of CO 2 emissions by month for R1

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................................ ................................ ...... 67 Figure 3.6: Comparison of peak load by month for GC

- 12.47

- 1_R1

................................ ................................ ...... 68 Figure 3.7: Comparison of energy consumption by month for GC

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- 1_R1

................................ ..................... 69 Figure 3.8: Comparis on of losses by month for GC

- 12.47

- 1_R1

................................ ................................ ............ 70 Figure 3.9: PV output by month for GC

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- 1_R1

................................ ................................ .............................. 71 Figure 3.10: Comparison of CO 2 emissions by month for GC

- 12.47

- R1

................................ ................................ 71 Figure 3.11: Comparison of peak load by month for R5

- 35.00

- 1

................................ ................................ ............ 73 Figure 3.12: Comparison of energy consumption by month for R5

- 35.00

- 1

................................ ........................... 74 Figure 3.13: Comparison of losses by month for R5

- 35.00

- 1

................................ ................................ .................. 75 Figure 3.14: Comparison of PV output by month for R5

- 35.00

- 1

................................ ................................ ........... 76 Figure 3.15: Comparison of CO 2 emissions by month for R5

- 35.00

- 1

................................ ................................ .... 77 Figure 3.16: Comparison of peak load by month for GC

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- 1_R2

................................ ................................ .... 78 Figure 3.17: Comparison of energy consumption by month for GC

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- 1_R2

................................ ................... 78 Figure 3.18: Comparison of losses by month for GC

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- 1_R2

................................ ................................ .......... 79 Figure 3.19: Comparison of WTG output by month for GC

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- 1_R2

................................ ................................ 80 Figure 3.20: Comparison of CO 2 by month for GC

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- 1_R2

................................ ................................ ............. 81
Figure B.1: Climate Zones Used for Development of Prototypical Feeders

................................ .......................... 116 Figure B.2: Voltage depe ndent energy consumption of 13W CFL

................................ ................................ ........ 120 Figure B.3: The ETP mode of a residential heating/cooling system

................................ ................................ ...... 121 Figure B.4: Multi

- state load model

................................ ................................ ................................ ........................ 124 Figure B.5: Exemplary cooling set points diversified with time and daytime and nighttime offsets

..................... 131 Figure B.6: Average cooling set points of entire population of R1

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................................ .......................... 131 Figure B.7: Average energy consumption o f responsive loads

................................ ................................ .............. 133 Figure B.8: Average energy consumption of unresponsive loads

................................ ................................ .......... 133 Figure B.9: Office zonal floor plan representing 1 of 3 identical floors

................................ ................................ 135 Figure B.10: Retail “big box” zonal floor plan

................................ ................................ ................................ ...... 136 Figure B.11: Retail strip mall z onal floor plan with N zones depending upon scaling factor

................................ 137 Figure B.12: Average office end

- use load shape (weekday)

................................ ................................ .................. 138

### x Figure B.13: Average office end

- use load shape (weekend)

................................ ................................ .................. 138 Figure B.14: Average big box and strip mall end

- use load shape (weekday)

................................ ........................ 139 Figure B.15: Average big box and strip mall end

- use load shape (weekend)

................................ ........................ 139 Figure B.16: 15

- minute interval comparing fuel dispatch for the peak load versus the base case load versus a technology modified load.

................................ ................................ ................................ ................................ ......... 144

Figure C.1: GridLAB

- D architecture

................................ ................................ ................................ ..................... 152 Figure C.2: Variable step sizes in GridLAB

- D simulation

................................ ................................ .................... 153
Figure D.1: Comparison of peak load by month for R1

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................................ ................................ ........... 155 Figure D.2: Comparison of energy con sumption by month for R1

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................................ .......................... 156 Figure D.3: Comparison of losses by month for R1

- 12.47

- 2

................................ ................................ ................. 156 Figure D.4: Comparison of PV output by month for R1

- 12.47

- 2

................................ ................................ .......... 157 Figure D.5: Comparison of CO 2 emissions by month for R1

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................................ ................................ ... 157 Figure D.6: Comparison of peak load by month for R1

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................................ ................................ ........... 158 Figure D.7: Comparison of energy consumption by month for R1

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................................ .......................... 158 Figure D.8: Comparison of losses by month for R1

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- 3

................................ ................................ ................. 159 Figure D.9: Comparison of PV output by month for R1

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- 3

................................ ................................ .......... 159 Figure D.10: Comparison of CO 2 emissions by month for R1

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................................ ................................ . 160 Figure D.11: Comparison of peak load by month for R1

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................................ ................................ ............ 160 Figure D.12: Comparison of e nergy consumption by month for R1

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................................ ........................ 161 Figure D.13: Comparison of losses by month for R1

- 12.47

- 4

................................ ................................ ............... 161 Figure D.14: PV output by month for R1

- 12.47

- 4

................................ ................................ ................................ . 162 Figure D.15: Comparison of CO 2 emissions by month for R1

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................................ ................................ . 162 Figure D.16: Comparison of peak lo ad by month for R1

- 25.00

- 1

................................ ................................ ......... 163 Figure D.17: Comparison of energy consumption by month for R1

- 25.00

- 1

................................ ........................ 163 Figure D.18: Comparison of losses by month for R1

- 25.00

- 1

................................ ................................ ............... 164 Figure D.19: PV output by month for R1

- 25.00

- 1

................................ ................................ ................................ . 164 Figure D.20: Comparison of CO 2 emissions b y month for R1

- 25.00

- 1

................................ ................................ . 165 Figure D.21: Comparison of peak load by month for R2

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................................ ................................ ......... 165 Figure D.22: Comparison of energy consumption by month for R2

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................................ ........................ 166 Figure D.23: Comparison of losses by month for R2

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- 1

................................ ................................ ............... 166 Figure D.24: PV output by month for R2

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- 1

................................ ................................ ................................ . 167 Figure D.25: Comparison of CO 2 emissions by mont h for R2

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- 1

................................ ................................ . 167 Figure D.26: Comparison of peak load by month for R2

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................................ ................................ ......... 168 Figure D.27: Comparison of energy consumption by month for R2

- 12.47

- 2

................................ ........................ 168 Figure D.28: Comparison of losses by month for R2

- 12.47

- 2

................................ ................................ ............... 169 Figure D.29: PV output by month for R2

- 12.47

- 2

................................ ................................ ................................ . 169 Figure D.30: Comparison of CO 2 emissions by month for R2

- 12.47

- 2

................................ ................................ . 170 Figure D.31: Comparison of peak load by month for R2

- 12.47

- 3

................................ ................................ ......... 170 Figure D.32: Comparison of energy consumption by month for R2

- 12.47

- 3

................................ ........................ 171 Figure D.33: Comparison of losses by month for R2

- 12.47

- 3

................................ ................................ ............... 171 Figure D.34: PV output by month for R2

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- 1

................................ ................................ ................................ . 172 Figure D.35: Comparison of CO 2 emissions by month for R2

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- 3

................................ ................................ . 172 Figure D.36: Comparison of peak load by month for R2

- 25.00

- 1

................................ ................................ ......... 173 Figure D.37: Comparison of energy consumption by month for R2

- 25.00

- 1

................................ ........................ 173 Figure D.38: Compariso n of losses by month for R2

- 25.00

- 1

................................ ................................ ............... 174 Figure D.39: PV output by month for R2

- 25.00

- 1

................................ ................................ ................................ . 174

### xi Figure D.40: Comparison of CO 2 emissions by month for R2

- 25.00

- 1

................................ ................................ . 175 Figure D.41: Comparison of peak load by month for R2

- 35.00

- 1

................................ ................................ ......... 175 Figure D.42: Comparison of energy consumption by month for R2

- 35.00

- 1

................................ ........................ 176 Figure D.43: Compariso n of losses by month for R2

- 35.00

- 1

................................ ................................ ............... 176 Figure D.44: PV output by month for R2

- 35.00

- 1

................................ ................................ ................................ . 177 Figure D.45: Comparison of CO 2 emissions by month for R2

- 35.00

- 1

................................ ................................ . 177 Figure D.46: Comparison of peak load by month for R3

- 12.47

- 1

................................ ................................ ......... 178 Figure D.47: Comparison of energy consumption by month for R3

- 12.47

- 1

................................ ........................ 179 Figure D.48: Compariso n of losses by month for R3

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- 1

................................ ................................ ............... 179 Figure D.49: PV output by month for R3

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- 1

................................ ................................ ................................ . 180 Figure D.50: Comparison of CO 2 emissions by month for R3

- 12.47

- 1

................................ ................................ . 180 Figure D.51: Comparison of peak load by month for R3

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- 3

................................ ................................ ......... 181 Figure D.52: Comparison of energy consumption by month for R3

- 12.47

- 3

................................ ........................ 181 Figure D.53: Comparison of losses by month for R3

- 12.47

- 3

................................ ................................ ............... 182 Figure D.54: PV output by month for R3

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- 3

................................ ................................ ................................ . 182 Figure D.55: Comparison of CO 2 emissions by month for R3

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- 3

................................ ................................ . 183 Figure D.56: Comparison of peak load by month for R4

- 12.47

- 1

................................ ................................ ......... 183 Figure D.57: Comparison of energy consumption by month for R4

- 12.47

- 1

................................ ........................ 184 Figure D.58: Comparison of losses by month for R4

- 12.47

- 1

................................ ................................ ............... 184 Figure D.59: PV output by month for R4

- 12.47

- 1

................................ ................................ ................................ . 185 Figure D.60: Comparison of CO 2 emissions by month for R4

- 12.47

- 1

................................ ................................ . 185 Figure D .61: Comparison of peak load by month for R4

- 12.47

- 2

................................ ................................ ......... 186 Figure D.62: Comparison of energy consumption by month for R4

- 12.47

- 2

................................ ........................ 186 Figure D.63: Comparison of losses by month for R4

- 12.47

- 2

................................ ................................ ............... 187 Figure D.64: PV output by month for R4

- 12.47

- 2

................................ ................................ ................................ . 187 Figure D.65: Comparison of CO 2 emissions by month for R3

- 12.47

- 1

................................ ................................ . 18 8 Figure D.66: Comparison of peak load by month for R4

- 25.00

- 1

................................ ................................ ......... 188 Figure D.67: Comparison of energy consumption by month for R4

- 25.00

- 1

................................ ........................ 189 Figure D.68: Comparison of losses by month for R4

- 25.00

- 1

................................ ................................ ............... 189 Figure D.69: PV output by month for R4

- 25.00

- 1

................................ ................................ ................................ . 190 Figure D.70: Comparison of CO 2 emissions by mont h for R4

- 25.00

- 1

................................ ................................ . 190 Figure D.71: Comparison of peak load by month for R5

- 12.47

- 1

................................ ................................ ......... 191 Figure D.72: Comparison of energy consumption by month for R5

- 12.47

- 1

................................ ........................ 192 Figure D.73: Comparison of losses by month for R5

- 12.47

- 1

................................ ................................ ............... 192 Figure D.74: PV output by month for R5

- 12.47

- 1

................................ ................................ ................................ . 193 Figure D.75: Comparison of CO 2 emissions by mont h for R5

- 12.47

- 1

................................ ................................ . 193 Figure D.76: Comparison of peak load by month for R5

- 12.47

- 2

................................ ................................ ......... 194 Figure D.77: Comparison of energy consumption by month for R5

- 12.47

- 2

................................ ........................ 194 Figure D.78: Comparison of losses by month for R5

- 12.47

- 2

................................ ................................ ............... 195 Figure D.79: PV output by month for R5

- 12.47

- 2

................................ ................................ ................................ . 195 Figure D.80: Comparison of CO 2 emissions by month for R5

- 12.47

- 2

................................ ................................ . 196 Figure D.81: Comparison of peak load by month for R5

- 12.47

- 3

................................ ................................ ......... 196 Figure D.82: Comparison of energy consumption by month for R5

- 12.47

- 3

................................ ........................ 197 Figure D.83: Comparison of losses by month for R5

- 12.47

- 3

................................ ................................ ............... 197 Figure D.84: PV output by month for R5

- 12.47

- 3

................................ ................................ ................................ . 198 Figure D.85: Comparison of CO 2 emissions by month for R5

- 12.47

- 3

................................ ................................ . 198 Figure D.86: Comparison of peak load by month for R5

- 12.47

- 4

................................ ................................ ......... 199 Figure D.87: Comparison of energy consumption by month for R5

- 12.47

- 4

................................ ........................ 199

### xii Figure D.88: Compariso n of losses by month for R5

- 12.47

- 4

................................ ................................ ............... 200 Figure D.89: PV output by month for R5

- 12.47

- 4

................................ ................................ ................................ . 200 Figure D.90: Comparison of CO 2 emissions by month for R5

- 12.47

- 4

................................ ................................ . 201 Figure D.91: Comparison of peak load by month for R5

- 12.47

- 5

................................ ................................ ......... 201 Figure D.92: Comparison of energy consumption by month for R5

- 12.47

- 5

................................ ........................ 202 Figure D.93: Comparison of losses by month for R5

- 12.47

- 5

................................ ................................ ............... 202 Figure D.94: PV output by month for R5

- 12.47

- 5

................................ ................................ ................................ . 203 Figure D.95: Comparison of CO 2 emissions by month for R5

- 12.47

- 5

................................ ................................ . 203 Figure D.96: Comparison of peak load by month for R5

- 25.00

- 1

................................ ................................ ......... 204 Figure D.97: Comparison of energy consumption by month for R5

- 25.00

- 1

................................ ........................ 204 Figure D.98: Comparison of losses by month for R5

- 25.00

- 1

................................ ................................ ............... 205 Figure D.99: PV output by month for R5

- 25.00

- 1

................................ ................................ ................................ . 205 Figure D.100: Comparison of CO 2 emissions by month for R5

- 25.00

- 1

................................ ............................... 206 Figure D.101: Comparison of peak load by month for R5

- 35.00

- 1

................................ ................................ ....... 206 Figure D.102: Comparison of energy consumption by month for R5

- 35.00

- 1

................................ ...................... 207 Figure D.103: Comparis on of losses by month for R5

- 35.00

- 1

................................ ................................ ............. 207 Figure D.104: PV output by month for R5

- 35.00

- 1

................................ ................................ ............................... 208 Figure D.105: Comparison of CO 2 emissions by month for R5

- 35.00

- 1

................................ ............................... 208 Figure D.106: Comparison of peak load by month for R1

- 12.47

- 1

................................ ................................ ....... 209 Figure D.107: Comparison o f energy consumption by month for R1

- 12.47

- 1

................................ ...................... 210 Figure D.108: Comparison of losses by month for R1

- 12.47

- 1

................................ ................................ ............. 210 Figure D.109: PV output by month for R1

- 12.47

- 1

................................ ................................ ............................... 211 Figure D.110: Comparison of CO 2 by month for R1

- 12.47

- 1

................................ ................................ ................ 211 Figure D.111: Comp arison of peak load by month for R1

- 12.47

- 2

................................ ................................ ....... 212 Figure D.112: Comparison of energy consumption by month for R1

- 12.47

- 2

................................ ...................... 212 Figure D.113: Comparison of losses by month for R1

- 12.47

- 2

................................ ................................ ............. 213 Figure D.114: PV output by month for R1

- 12.47

- 2

................................ ................................ ............................... 213 Figure D.115: Comparison of CO 2 by month for R1

- 12.47

- 2

................................ ................................ ................ 214 Figure D.116: Comparison of peak load by month for R1

- 12.47

- 3

................................ ................................ ....... 214 Figure D.117: Comparison of energy consumption by month for R1

- 12.47

- 3

................................ ...................... 215 Figure D.118: Comparison of losses by month for R1

- 12.47

- 3

................................ ................................ ............. 215 Figure D.119: PV output by month for R1

- 12.47

- 3

................................ ................................ ............................... 216 Figure D.120: Comparison of CO 2 by month for R 1

- 12.47

- 3

................................ ................................ ................ 216 Figure D.121: Comparison of peak load by month for R1

- 12.47

- 4

................................ ................................ ....... 217 Figure D.122: Comparison of energy consumption by month for R1

- 12.47

- 4

................................ ...................... 217 Figure D.123: Comparison of losses by month for R1

- 12.47

- 4

................................ ................................ ............. 218 Figure D.124: PV output by month for R1

- 12.47

- 4

................................ ................................ ............................... 218 Figure D.125: Comparison of CO 2 by month for R 1

- 12.47

- 4

................................ ................................ ................ 219 Figure D.126: Comparison of peak load by month for R1

- 25.00

- 1

................................ ................................ ....... 219 Figure D.127: Comparison of energy consumption by month for R1

- 25.00

- 1

................................ ...................... 220 Figure D.128: Comparison of losses by month for R1

- 25.00

- 1

................................ ................................ ............. 220 Figure D.129: PV outpu t by month for R1

- 25.00

- 1

................................ ................................ ............................... 221 Figure D.130: Comparison of CO 2 by month for R1

- 25.00

- 1

................................ ................................ ................ 221 Figure D.131: Comparison of peak load by month for GC

- 12.47

- 1_R2

................................ ................................ 222 Figure D.132: Comparison of energy consumption by month for GC

- 12.47

- 1_R2

................................ ............... 222 Figure D.133: Comparison of losses by month for GC

- 12.47

- 1_R2

................................ ................................ ..... 223 Figure D.134: PV o utput by month for GC

- 12.47

- 1_R2

................................ ................................ ....................... 223 Figure D.135: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R2

................................ ....................... 224

### xiii Figure D.136: Comparison of peak load by month for R2

- 12.47

- 1

................................ ................................ ....... 224 Figure D.137: Comparison of energy consumption by month for R2

- 12.47

- 1

................................ ...................... 225 Figure D.138: Comparison of losses by month for R2

- 12.47

- 1

................................ ................................ ............. 225 Figure D.139: PV output by month for R2

- 12.47

- 1

................................ ................................ ............................... 226 Figure D.140: Comparison of CO 2 emissions by month for R2

- 12.47

- 1

................................ ............................... 226 Figure D.141: Comparison of peak load by month for R2

- 12.47

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................................ ................................ ....... 227 Figure D.142: Comparison of energy consumption by month for R2

- 12.47

- 2

................................ ...................... 227 Figure D.143: Compar ison of losses by month for R2

- 12.47

- 2

................................ ................................ ............. 228 Figure D.144: PV output by month for R2

- 12.47

- 2

................................ ................................ ............................... 228 Figure D.145: Comparison of CO 2 emissions by month for R2

- 12.47

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................................ ............................... 229 Figure D.146: Comparison of peak load by month for R2

- 25.00

- 1

................................ ................................ ....... 229 Figure D.147: Comparison of energy consumption by month for R2

- 25.00

- 1

................................ ...................... 230 Figure D.148: Compar ison of losses by month for R2

- 25.00

- 1

................................ ................................ ............. 230 Figure D.149: PV output by month for R2

- 25.00

- 1

................................ ................................ ............................... 231 Figure D.150: Comparison of CO 2 by month for R2

- 25.00

- 1

................................ ................................ ................ 231 Figure D.151: Comparison of peak load by month for R2

- 35.00

- 1

................................ ................................ ....... 232 Figure D.152: Comparison of energy consumption by month for R2

- 35.00

- 1

................................ ...................... 232 Figure D.153: Compar ison of losses by month for R2

- 35.00

- 1

................................ ................................ ............. 233 Figure D.154: PV output by month for R2

- 35.00

- 1

................................ ................................ ............................... 233 Figure D.155: Comparison of CO 2 emissions by month for R2

- 35.00

- 1

................................ ............................... 234 Figure D.156: Comparison of peak load by month for GC

- 12.47

- 1_R3

................................ ................................ 234 Figure D.157: Comparis on of energy consumption by month for GC

- 12.47

- 1_R3

................................ ............... 235 Figure D.158: Comparison of losses by month for GC

- 12.47

- 1_R3

................................ ................................ ..... 235 Figure D.159: PV output by month for GC

- 12.47

- 1_R3

................................ ................................ ....................... 236 Figure D.160: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R3

................................ ....................... 236 Figure D.161: Comparison of peak load by month for R3

- 12.47

- 1

................................ ................................ ....... 237 Figure D.162: Comparison of energy consumption by month for R3

- 1 2.47

- 1

................................ ...................... 237 Figure D.163: Comparison of losses by month for R3

- 12.47

- 1

................................ ................................ ............. 238 Figure D.164: PV output by month for R3

- 12.47

- 1

................................ ................................ ............................... 238 Figure D.165: Comparison of CO 2 emissions by month for R3

- 12.47

- 1

................................ ............................... 239 Figure D.166: Comparison of peak load by month for R3

- 12.47

- 2

................................ ................................ ....... 240 Figure D.167: Comparison of energy consumption by month for R3

- 12.47

- 2

................................ ...................... 240 Figure D.168: Comparison of losses by month for R3

- 12.47

- 2

................................ ................................ ............. 241 Figure D.169: PV output by month for R3

- 12.47

- 2

................................ ................................ ............................... 241 Figure D.170: Comparison of CO 2 emissions by m onth for R3

- 12.47

- 2

................................ ............................... 242 Figure D.171: Comparison of peak load by month for R3

- 12.47

- 3

................................ ................................ ....... 242 Figure D.172: Comparison of energy consumption by month for R3

- 12.47

- 3

................................ ...................... 243 Figure D.173: Comparison of losses by month for R3

- 12.47

- 3

................................ ................................ ............. 243 Figure D.174: PV output by month for R3

- 12.47

- 3

................................ ................................ ............................... 244 Figure D.175: Comparison of CO 2 emissions by m onth for R3

- 12.47

- 3

................................ ............................... 244 Figure D.176: Comparison of peak load by month for GC

- 12.47

- 1_R4

................................ ................................ 245 Figure D.177: Comparison of energy consumption by month for GC

- 12.47

- 1_R4

................................ ............... 245 Figure D.178: Comparison of losses by month for GC

- 12.47

- 1_R4

................................ ................................ ..... 246 Figure D.179: PV output by month for GC

- 12.47

- 1_R4

................................ ................................ ....................... 246 Figure D.180: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R4

................................ ....................... 247 Figure D.181: Comparison of peak load by month for R4

- 12.47

- 1

................................ ................................ ....... 248 Figure D.182: Comparison of energy consumption by month for R4

- 12.47

- 1

................................ ...................... 248 Figure D.183: Comparison of losses by month for R4

- 12.47

- 1

................................ ................................ ............. 249

### xiv Figure D.184: PV outpu t by month for R4

- 12.47

- 1

................................ ................................ ............................... 249 Figure D.185: Comparison of CO 2 emissions by month for R4

- 12.47

- 1

................................ ............................... 250 Figure D.186: Comparison of peak load by month for R4

- 12.47

- 2

................................ ................................ ....... 250 Figure D.187: Comparison of energy consumption by month for R4

- 12.47

- 2

................................ ...................... 251 Figure D.188: Compar ison of losses by month for R4

- 12.47

- 2

................................ ................................ ............. 251 Figure D.189: PV output by month for R4

- 12.47

- 2

................................ ................................ ............................... 252 Figure D.190: Comparison of CO 2 emissions by month for R4

- 12.47

- 2

................................ ............................... 252 Figure D.191: Comparison of peak load by month for R4

- 25.00

- 1

................................ ................................ ....... 253 Figure D.192: Comparison of energy consumption by month for R4

- 25.00

- 1

................................ ...................... 253 Figure D.193: Compar ison of losses by month for R4

- 25.00

- 1

................................ ................................ ............. 254 Figure D.194: PV output by month for R4

- 25.00

- 1

................................ ................................ ............................... 254 Figure D.195: Comparison of CO 2 emissions by month for R4

- 25.00

- 1

................................ ............................... 255 Figure D.196: Comparison of peak load by month for GC

- 12.47

- 1_R5

................................ ................................ 256 Figure D.197: Comparison of energy consumption by month for GC

- 12.47

- 1_R5

................................ ............... 256 Figure D.198: Comparison of losses by month for GC

- 12.47

- 1_R5

................................ ................................ ..... 257 Figure D.199: PV output by month for GC

- 12.47

- 1_R5

................................ ................................ ....................... 257 Figure D.200: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R5

................................ ....................... 258 Figure D.201: Comparison of peak load by month for R5

- 12.47

- 1

................................ ................................ ....... 258 Figure D.202: Co mparison of energy consumption by month for R5

- 12.47

- 1

................................ ...................... 259 Figure D.203: Comparison of losses by month for R5

- 12.47

- 1

................................ ................................ ............. 259 Figure D.204: PV output by month for R5

- 12.47

- 1

................................ ................................ ............................... 260 Figure D.205: Comparison of CO 2 emissions by month for R5

- 12.47

- 1

................................ ............................... 260 Figure D.206: Comparison of peak load by month for R5

- 12.47

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................................ ................................ ....... 261 Figure D.207: Comparison o f energy consumption by month for R5

- 12.47

- 2

................................ ...................... 261 Figure D.208: Comparison of losses by month for R5

- 12.47

- 2

................................ ................................ ............. 262 Figure D.209: PV output by month for R5

- 12.47

- 2

................................ ................................ ............................... 262 Figure D.210: Comparison of CO 2 emissions by month for R5

- 12.47

- 2

................................ ............................... 263 Figure D .211: Comparison of peak load by month for R5

- 12.47

- 3

................................ ................................ ....... 263 Figure D.212: Comparison of energy consumption by month for R5

- 12.47

- 3

................................ ...................... 264 Figure D.213: Comparison of losses by month for R5

- 12.47

- 3

................................ ................................ ............. 264 Figure D.214: PV output by month for R5

- 12.47

- 3

................................ ................................ ............................... 265 Figure D.215: Comparison of CO 2 emissions by month for R5

- 12.47

- 3

................................ ............................... 265 Figure D.216: Comparison of peak load by month for R5

- 12.47

- 4

................................ ................................ ....... 266 Figure D.217: Comparison of energy consumption by month for R5

- 12.47

- 4

................................ ...................... 266 Figure D.218: Comparison of losses by month for R5

- 12.47

- 4

................................ ................................ ............. 267 Figure D.219: PV output by month for R5

- 12.47

- 4

................................ ................................ ............................... 267 Figure D.220: Comparison of CO 2 emissions by m onth for R5

- 12.47

- 4

................................ ............................... 268 Figure D.221: Comparison of peak load by month for R5

- 12.47

- 5

................................ ................................ ....... 269 Figure D.222: Comparison of energy consumption by month for R5

- 12.47

- 5

................................ ...................... 269 Figure D.223: Comparison of losses by month for R5

- 12.47

- 5

................................ ................................ ............. 270 Figure D.224: PV output by month for R5

- 12.47

- 5

................................ ................................ ............................... 270 Figure D.225: Comparison of CO 2 emissions by m onth for R5

- 12.47

- 5

................................ ............................... 271 Figure D.226: Comparison of peak load by month for R5

- 25.00

- 1

................................ ................................ ....... 272 Figure D.227: Comparison of energy consumption by month for R5

- 25.00

- 1

................................ ...................... 272 Figure D.228: Comparison of losses by month for R5

- 25.00

- 1

................................ ................................ ............. 273 Figure D.229: Comparis on of CO 2 emissions by month for R5

- 25.00

- 1

................................ ............................... 273 Figure D.230: Comparison of peak load by month for R5

- 35.00

- 1

................................ ................................ ....... 274 Figure D.231: Comparison of energy consumption by month for R5

- 35.00

- 1

................................ ...................... 274

### xv Figure D.232: Comparison of losses by month for R5

- 35.00

- 1

................................ ................................ ............. 275 Figure D.233: Comparison of CO 2 emissions by month for R5

- 35.00

- 1

................................ ............................... 275 Figure D.234: Comparison of peak load by month for R1

- 12.47

- 1

................................ ................................ ....... 276 Figure D.235: Comparison of energy consumption by month for R1

- 12.47

- 1

................................ ...................... 276 Figure D.236: Comparison of losses by month for R1

- 12.47

- 1

................................ ................................ ............. 2 77 Figure D.237: PV output by month for R1

- 12.47

- 1

................................ ................................ ............................... 277 Figure D.238: Comparison of CO 2 emissions by month for R1

- 12.47

- 1

................................ ............................... 278 Figure D.239: Comparison of peak load by month for R1

- 12.47

- 2

................................ ................................ ....... 278 Figure D.240: Comparison of energy consumption by month for R1

- 12.47

- 2

................................ ...................... 279 Figure D.241: Comparison of losses by month for R1

- 12.47

- 2

................................ ................................ ............. 279 Figure D.242: PV output by month for R1

- 12.47

- 2

................................ ................................ ............................... 280 Figure D.243: Comparison of CO 2 emissions by month for R1

- 12.47

- 2

................................ ............................... 280 Figure D.244: Comparison of peak load by month for R1

- 12.47

- 3

................................ ................................ ....... 281 Figure D.245: Comparison of energy consumption by month for R1

- 12.47

- 3

................................ ...................... 281 Figure D.246: Comparison of losses by month for R1

- 12.47

- 3

................................ ................................ ............. 282 Figure D.247: PV output by month for R1

- 12.47

- 3

................................ ................................ ............................... 282 Figure D.248: Comparison of CO 2 em issions by month for R1

- 12.47

- 3

................................ ............................... 283 Figure D.249: Comparison of peak load by month for R1

- 12.47

- 4

................................ ................................ ....... 283 Figure D.250: Comparison of energy consumption by month for R1

- 12.47

- 4

................................ ...................... 284 Figure D.251: Comparison of losses by month for R1

- 12.47

- 4

................................ ................................ ............. 284 Figure D.252: PV outpu t by month for R1

- 12.47

- 4

................................ ................................ ............................... 285 Figure D.253: Comparison of CO 2 emissions by month for R1

- 12.47

- 4

................................ ............................... 285 Figure D.254: Comparison of peak load by month for R1

- 25.00

- 1

................................ ................................ ....... 286 Figure D.255: Comparison of energy consumption by month for R1

- 25.00

- 1

................................ ...................... 286 Figure D.256: Compar ison of losses by month for R1

- 25.00

- 1

................................ ................................ ............. 287 Figure D.257: PV output by month for R1

- 25.00

- 1

................................ ................................ ............................... 287 Figure D.258: Comparison of CO 2 emissions by month for R1

- 25.00

- 1

................................ ............................... 288 Figure D.259: Comparison of peak load by month for R2

- 12.47

- 1

................................ ................................ ....... 288 Figure D.260: Comparison of energy consumption by month for R2

- 12.47

- 1

................................ ...................... 289 Figure D.261: Compar ison of losses by month for R2

- 12.47

- 1

................................ ................................ ............. 289 Figure D.262: PV output by month for R2

- 12.47

- 1

................................ ................................ ............................... 290 Figure D.263: Comparison of CO 2 emissions by month for R2

- 12.47

- 1

................................ ............................... 290 Figure D.264: Comparison of peak load by month for R2

- 12.47

- 2

................................ ................................ ....... 291 Figure D.265: Comparison of energy consumption by month for R2

- 12.47

- 2

................................ ...................... 291 Figure D.266: Comparison of losses by month for R2

- 12.47

- 2

................................ ................................ ............. 292 Figure D.267: PV output by month for R2

- 12.47

- 2

................................ ................................ ............................... 292 Figure D.268: Comparison of CO 2 emissions by month for R2

- 12.47

- 2

................................ ............................... 293 Figure D.269: Comparison of peak load by month for R2

- 25.00

- 1

................................ ................................ ....... 293 Figure D.270: Comparison o f energy consumption by month for R2

- 25.00

- 1

................................ ...................... 294 Figure D.271: Comparison of losses by month for R2

- 25.00

- 1

................................ ................................ ............. 294 Figure D.272: PV output by month for R2

- 25.00

- 1

................................ ................................ ............................... 295 Figure D.273: Comparison of CO 2 emissions by month for R2

- 25.00

- 1

................................ ............................... 295 Figure D.274: Comparison of peak load by month for R2

- 35.00

- 1

................................ ................................ ....... 296 Figure D.275: Comparison o f energy consumption by month for R2

- 35.00

- 1

................................ ...................... 296 Figure D.276: Comparison of losses by month for R2

- 35.00

- 1

................................ ................................ ............. 297 Figure D.277: PV output by month for R2

- 35.00

- 1

................................ ................................ ............................... 297 Figure D.278: Comparison of CO 2 emissions by month for R2

- 35.00

- 1

................................ ............................... 298 Figure D .279: Comparison of peak load by month for R3

- 12.47

- 1

................................ ................................ ....... 298

### xvi Figure D.280: Comparison of energy consumption by month for R3

- 12.47

- 1

................................ ...................... 299 Figure D.281: Comparison of losses by month for R3

- 12.47

- 1

................................ ................................ ............. 299 Figure D.282: PV output by month for R3

- 12.47

- 1

................................ ................................ ............................... 300 Figure D.283: Comparison of CO 2 emissions by month for R3

- 12.47

- 1

................................ ............................... 300 Figure D.284: Comparison of peak load by month for R3

- 12.47

- 3

................................ ................................ ....... 301 Figure D.285: Comparison of energy consumption by month for R3

- 12.47

- 3

................................ ...................... 301 Figure D.286: Comparison of losses by month for R3

- 12.47

- 3

................................ ................................ ............. 302 Figure D.287: PV output by month for R3

- 12.47

- 3

................................ ................................ ............................... 302 Figure D.288: Comparison of energy consumption by month for R3

- 12.47

- 3

................................ ...................... 303 Figure D.289: Comparison of peak loadby month for R4

- 12.47

- 1

................................ ................................ ........ 303 Figure D.290: Comparison of energy consumption by month for R4

- 12.47

- 1

................................ ...................... 304 Figure D.291: Comparison of losses by month for R4

- 12.47

- 1

................................ ................................ ............. 304 Figure D.292: PV output by month for R4

- 12.47

- 1

................................ ................................ ............................... 305 Figure D.293: Comparison of CO 2 emissions by m onth for R4

- 12.47

- 1

................................ ............................... 305 Figure D.294: Comparison of peak loadby month for R4

- 12.47

- 2

................................ ................................ ........ 306 Figure D.295: Comparison of energy consumption by month for R4

- 12.47

- 2

................................ ...................... 306 Figure D.296: Comparison of losses by month for R4

- 12.47

- 2

................................ ................................ ............. 307 Figure D.297: PV output by month for R4

- 12.47

- 2

................................ ................................ ............................... 307 Figure D.298: Comparison of CO 2 emissions by m onth for R4

- 12.47

- 2

................................ ............................... 308 Figure D.299: Comparison of peak loadby month for R4

- 25.00

- 1

................................ ................................ ........ 308 Figure D.300: Comparison of energy consumption by month for R4

- 25.00

- 1

................................ ...................... 309 Figure D.301: Comparison of losses by month for R4

- 25.00

- 1

................................ ................................ ............. 309 Figure D.302: PV outpu t by month for R4

- 25.00

- 1

................................ ................................ ............................... 310 Figure D.303: Comparison of CO 2 emissions by month for R4

- 25.00

- 1

................................ ............................... 310 Figure D.304: Comparison of peak load by month for R5

- 12.47

- 1

................................ ................................ ....... 311 Figure D.305: Comparison of energy consumption by month for R5

- 12.47

- 1

................................ ...................... 311 Figure D.306: Comparison of losses by month for R5

- 12.47

- 1

................................ ................................ ............. 312 Figure D.307: PV outpu t by month for R5

- 12.47

- 1

................................ ................................ ............................... 312 Figure D.308: Comparison of CO 2 emissions by month for R5

- 12.47

- 1

................................ ............................... 313 Figure D.309: Comparison of peak load by month for R5

- 12.47

- 2

................................ ................................ ....... 313 Figure D.310: Comparison of energy consumption by month for R5

- 12.47

- 2

................................ ...................... 314 Figure D.311: Compar ison of losses by month for R5

- 12.47

- 2

................................ ................................ ............. 314 Figure D.312: PV output by month for R5

- 12.47

- 2

................................ ................................ ............................... 315 Figure D.313: Comparison of CO 2 emissions by month for R5

- 12.47

- 2

................................ ............................... 315 Figure D.314: Comparison of peak load by month for R5

- 12.47

- 3

................................ ................................ ....... 316 Figure D.315: Comparison of energy consumption by month for R5

- 12.47

- 3

................................ ...................... 316 Figure D.316: Compar ison of losses by month for R5

- 12.47

- 3

................................ ................................ ............. 317 Figure D.317: PV output by month for R5

- 12.47

- 3

................................ ................................ ............................... 317 Figure D.318: Comparison of CO 2 emissions by month for R5

- 12.47

- 3

................................ ............................... 318 Figure D.319: Comparison of peak loadby month for R5

- 12.47

- 4

................................ ................................ ........ 318 Figure D.320: Comparison of energy consumption by month for R5

- 12.47

- 4

................................ ...................... 319 Figure D.321: Compar ison of losses by month for R5

- 12.47

- 4

................................ ................................ ............. 31 9 Figure D.322: PV output by month for R5

- 12.47

- 4

................................ ................................ ............................... 320 Figure D.323: Comparison of CO 2 emissions by month for R5

- 12.47

- 4

................................ ............................... 320 Figure D.324: Comparison of peak loadby month for R5

- 12.47

- 5

................................ ................................ ........ 321 Figure D.325: Comparison of energy consumption by month for R5

- 12.47

- 5

................................ ...................... 321 Figure D.326: Comparison of losses by month for R5

- 12.47

- 5

................................ ................................ ............. 322 Figure D.327: PV output by month for R5

- 12.47

- 5

................................ ................................ ............................... 322

### xvii Figure D.328: Comparison of CO 2 emissions by month for R5

- 12.47

- 5

................................ ............................... 323 Figure D.329: Comparison of peak load by month for R5

- 25.00

- 1

................................ ................................ ....... 323 Figure D.330: Comparison of energy consumption by month for R5

- 25.00

- 1

................................ ...................... 324 Figure D.331: Comparison of losses by month for R5

- 25.00

- 1

................................ ................................ ............. 324 Figure D.332: PV output by month for R5

- 25.00

- 1

................................ ................................ ............................... 325 Figure D.333: Comparison of CO 2 emissions by month for R5

- 25.00

- 1

................................ ............................... 325 Figure D.334: Comparison of peak load by month for GC

- 12.47

- 1_R1

................................ ................................ 326 Figure D.335: Comparis on of energy consumption by month for GC

- 12.47

- 1_R1

................................ ............... 327 Figure D.336: Comparison of losses by month for GC

- 12.47

- 1_R1

................................ ................................ .... 327 Figure D.337: Comparison of WTG output by month for GC

- 12.47

- 1_R1

................................ ........................... 328 Figure D.338: Comparison of CO 2 output by month for GC

- 12.47

- 1_R1

................................ ............................. 328 Figure D.339: Comparison of peak load by month for GC

- 12.47

- 1_R2

................................ ................................ 329 Figure D.340: Comparison of energy consumption by month for GC

- 12.47

- 1_R3

................................ ............... 329 Figure D.341: Comparison of losses by month for GC

- 12.47

- 1_R3

................................ ................................ ..... 330 Figure D.342: Comparison of WTG output by month for GC

- 12.47

- 1_R3

................................ ........................... 330 Figure D.343: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R3

................................ ....................... 331 Figure D.344: Comparison o f peak load by month for GC

- 12.47

- 1_R4

................................ ................................ 331 Figure D.345: Comparison of energy consumption by month for GC

- 12.47

- 1_R4

................................ ............... 332 Figure D.346: Comparison of losses by month for GC

- 12.47

- 1_R4

................................ ................................ ..... 332 Figure D.347: Comparison of WTG output by month for GC

- 12.47

- 1_R4

................................ ........................... 333 Figure D.348: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R4

................................ ....................... 333 Figure D.349: Comparison of peak load by month for GC

- 12.47

- 1_R5

................................ ................................ 334 Figure D.350: Comparison of energy consumption by month for GC

- 12.47

- 1_R5

................................ ............... 334 Figure D.351: Comparison of losses by month for GC

- 12.47

- 1_R5

................................ ................................ ..... 335 Figure D.352: Comparison of WTG output by month for GC

- 12.47

- 1_R5

................................ ........................... 335 Figure D.353: Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R5

................................ ....................... 336

### xviii 

## Table of Tables Table 2.1: Impact Metrics affected by addition of Residential PVs

................................ ................................ ........... 9 Table 2.2: Residential PV penetration by region

................................ ................................ ................................ ..... 10 Table 2.3: Impact Metrics affected by addition of Commercial PVs

................................ ................................ ....... 25 Table 2.4: Commercial PV penetration by region

................................ ................................ ................................ .... 26 Table 2.5: Impact metrics affected by the addition of combined PVs

................................ ................................ ..... 38 Table 2.6: Combined PV penetration by regi on

................................ ................................ ................................ ....... 39 Table 2.7: Impact metrics affected by commercial WTG

................................ ................................ ........................ 51
Table 4.1: Residential PV impact metrics for region 1

................................ ................................ ............................ 83 Table 4.2: Residential PV impact metrics for region 2

................................ ................................ ............................ 84 Table 4.3: Residen tial PV impact metrics for region 3

................................ ................................ ............................ 85 Table 4.4: Residential PV impact metrics for region 4

................................ ................................ ............................ 86 Table 4.5: Residential PV impact metrics for region 5

................................ ................................ ............................ 87 Table 4.6: Commercial PV impact metrics for region 1

................................ ................................ .......................... 88 Table 4.7: Commercial PV impact metrics for regi on 2

................................ ................................ .......................... 89 Table 4.8: Commercial PV impact metrics for region 3

................................ ................................ .......................... 90 Table 4.9: Commercial PV impact metrics for region 4

................................ ................................ .......................... 91 Table 4.10: Commercial PV impact metrics for region 5

................................ ................................ ........................ 92 Table 4.11: Combined PV impact metrics for regio n 1

................................ ................................ ........................... 93 Table 4.12: Combined PV impact metrics for region 2

................................ ................................ ........................... 94 Table 4.13: Combined PV impact metrics for region 3

................................ ................................ ........................... 95 Table 4.14: Combined PV impact metrics for region 4

................................ ................................ ........................... 96 Table 4.15: Combined PV impact metrics for region 5

................................ ................................ ........................... 97 Table 4.16: Commercial WTG impact metrics fo r region 1

................................ ................................ .................... 98 Table 4.17: Commercial WTG impact metrics for region 2

................................ ................................ .................... 99 Table 4.18: Commercial WTG impact metrics for region 3

................................ ................................ .................. 100 Table 4.19: Commercial WTG impact metrics for region 4

................................ ................................ .................. 101 Table 4.20: Commercial WTG metrics for reg ion 5

................................ ................................ .............................. 102
Table A.1: SGIG program impact metrics from guidebook

................................ ................................ ................... 107
Table B.1: Summary of prototypical feeders

................................ ................................ ................................ ......... 117 Table B.2: End use load classifications

................................ ................................ ................................ .................. 118 Table B.3: Heat pump state transition logic

................................ ................................ ................................ ........... 125 Table B.4: Table of weighting factors for mapping regional parameters

................................ ............................... 127 Table B.5: Residential thermal integrity values by age of home

................................ ................................ ........... 128 Table B.6: Percentage of single family homes in total population by age and region

................................ ........... 128 Table B.7: Percentage of apartments in total population by age and region

................................ .......................... 129 Table B.8: Percentage of mobile homes in total population by age and region

................................ ..................... 129 Table B.9: Percentage of key building parameters by region

................................ ................................ ................ 129 Table B.10: Percentage of nighttime heating and cooling set points by housing type

................................ ........... 130 Table B.11: Average square footage by building type and region

................................ ................................ ......... 130 Table B.12: Key parameters for commercial buildings

................................ ................................ ......................... 135 Table B.13: Dispatch order of fuel by region

................................ ................................ ................................ ......... 141 Table B.14: Percent of energy consumed, broken down by fuel type and month in region 1

................................ 141 Table B.15: Percent of energy consumed, broken down by fuel type and month in region 2

................................ 142

### xix Table B.16: Percent of energy consumed, broken down by fuel type and month in region 3

................................ 142 Table B.17: Percent of energy consumed , broken down by fuel type and month in region 4

................................ 142 Table B.18: Percent of energy consumed, broken down by fuel type and month i n region 5

................................ 143 Table B.19: Average thermal efficiencies by fuel type.

................................ ................................ ......................... 145 Table B.20: Pollutant production per BTU of fuel (lbs./MBTU)

................................ ................................ ........... 145
Table E.1: Base case performance metrics for region 1

................................ ................................ ......................... 336 Table E.2: Base case performance metrics for region 2

................................ ................................ ......................... 337 Table E.3: Base case performance metrics for region 3

................................ ................................ ......................... 338 Table E.4: Base case performance metrics for region 4

................................ ................................ ......................... 339 Table E.5: Base case performance metrics for region 5

................................ ................................ ......................... 340 Table E.6: Residential PV performance metrics for region 1

................................ ................................ ................ 341 Table E.7: Residential PV performance metrics for region 2

................................ ................................ ................ 342 Table E.8: Residential PV performance metrics for region 3

................................ ................................ ................ 343 Table E.9: Residential PV performance metrics for region 4

................................ ................................ ................ 344 Table E.10: Residential PV performance metrics for region 5

................................ ................................ .............. 345 Ta ble E.11: Commercial PV performance metrics for region 1

................................ ................................ ............. 346 Table E.12: Commercial PV performance metrics for region 2

................................ ................................ ............. 347 Table E.13: Commercial PV performance metrics for region 3

................................ ................................ ............. 348 Table E.14: Commercial PV performance metrics for region 4

................................ ................................ ............. 3 49 Table E.15: Commercial PV performance metrics for region 5

................................ ................................ ............. 350 Table E.16: Combined PV performanc e metrics for region 1

................................ ................................ ................ 351 Table E.17: Combined PV performance metrics for region 2

................................ ................................ ................ 352 Table E.18: Combined PV performance metrics for region 3

................................ ................................ ................ 353 Table E.19: Combined PV performance metrics for region 4

................................ ................................ ................ 354 Table E.20: Combined PV performance me trics for region 5

................................ ................................ ................ 355 Table E.21: Commercial WTG performance metrics for region 1

................................ ................................ ......... 356 Table E.22: Commercial WTG performance metrics for region 2

................................ ................................ ......... 357 Table E.23: Commercial WTG performance metrics for region 3

................................ ................................ ......... 358 Table E.24: Commercial WTG performance metrics for region 4

................................ ................................ ......... 359 Table E.25: Commercial WTG perform ance metrics for region 5

................................ ................................ ......... 360

### 1 

# Summary

This document is one of a series of five reports commissioned by the United States Department of Energy, Office of Electricity Delivery and Energy Reliability. The purpose of these reports is to estimate some of the benefits of deploying technologies similar t o those implemented on the Smart Grid Investment Grant (SGIG) projects. Four technical reports cover the various types of technologies deployed in the SGIG projects: distribution automation, demand response, energy storage, and distributed generation. Wh ile the results of these reports provide insight into the variation of impacts by technology, feeder composition and region, it should be noted that the actual impacts and benefits of employing specific technologies in individual SGIG projects may vary fro m these projections. A fifth report in the series examines the benefits of deploying these technologies on a national level. This technical report examines the impacts of distribution automation technologies deployed in the SGIG projects.

### 2 

# 1 Introduct ion
As part of the American Recovery and Reinvestment Act of 2009, the U.S. Department of Energy (DOE) Office of Electricity Delivery and Energy Reliability (OE) provided Smart Grid Investment Grant (SGIG) funding to 99 award reci pients totaling $3.4 Billion [1] . Coupled with matching funds of $4.6 Billion from industry, the SGIG projects are intended to accelerate the modernization of the nation’s electricity infrastructure. To help evaluate the effect of these projects, a set of impact metrics h as been developed by the DOE [2] . Once the SGIG projects are complete, it will be possible to analyze collected field measurements and determine the exact benefit from each of the various technologies within each of the projects. OE has several initiatives operating in current and near

- term time frames to assess impacts and disseminate information as data becomes available. These initiatives include analysis partnerships with individual SGIG recipients, specific technology assessments, stakeholder briefings , and improvements to existing algorithms and tools.

In order to examine the SGIG project benefits, the Pacific Northwest National Laboratory (PNNL) utilized the GridLAB

- D simulation environment to conduct extensive simulations on representative technolog ies. GridLAB

- D was originally developed at PNNL, via DOE OE funding, to provide an open source simulation environment to evaluate the impacts of emerging technologies on the nation’s electricity infrastructure. The unique multi

- disciplinary agent based

s tructure of GridLAB

- D allows for the effective evaluation of complex emerging technologies such as voltage optimization and demand response. These are the same technologies that being deployed as part of the SGIG projects.

The impact of these technologies , at the distribution feeder level across various climate regions of the United States [3], is presented in a series of 4 technical reports, of which this report is the first. Each of the 4 technical reports examines a class of technologies deployed in the SGIG projects. The 4 technical reports examine distribution automation, demand response, energy storage, and renewable integration. A 5 th 

### report uses the results of the four technical reports to generate a policy level examination of the various technol ogies. The final report includes extrapolation to a national level deployment at various penetration levels.

To ensure that the results of this report can be reproduced by other researchers, all of the tools, models, and materials used are openly availabl e at [ 4 ]. Through detailed time

- series simulations conducted in GridLAB

- D, the impact of adding distribution automation capabilities to the grid can be examined on the relevant prototypical feeders. Utilities, regulators, vendors and other stakeholders in terested in analyses more specific to their systems, goals, and conditions may make use of these open tools for their own purposes.

### 3 

# 1.1 Report Scope

Due to the large number of SGIG projects and the wide range of specific implementations, it is not feasible to simulate each of the specific SGIG projects. In addition to the numerous implementations, it would be necessary to model the electrical infrastructu re of each of the projects. To address these issues, the technical reports will model a selection of technologies that are representative of those seen in the SGIG projects, and it will examine their impact on a set of prototypical distribution feeders th at are representative of those seen in the various climate regions of North America [3]. By utilizing representative technologies and prototypical distribution feeders, it will be possible for this report to estimate the feeder level impact of each techno logy. Once the impact of the technologies has been evaluated on the prototypical feeders, the results will be extrapolated to explore the impacts and considerations associated with deploying the technology on a national level.

The technologies deployed as part of the SGIG projects can be placed in one of two categories: direct and enabling. Direct technologies are those that provide direct benefit to the system. Enabling technologies are those that may not provide a direct benefit to the system, but th ey enable other beneficial technologies. As an example, a communications network does not provide any reduction in energy consumption, but it does enable demand response systems that create reductions in energy consumption.

The technical reports focus on the benefits obtained from the deployment of direct technologies when supported with the necessary enabling technologies. 

# 1.1.1 Direct Representative Technologies

These are the 15 technologies that will be specifically analyzed using GridLAB

- D simulations. Wi thin each of the 4 technical reports there are one or more specific direct technologies that are examined.

Distribution Automation (DA)

- t1: Volt

- VAR Optimization (VVO)

- t2: Capacitor Automation (CA)

- t3: Reclosers and Sectionalizers (R&S)

- t4: Distribution Ma nagement and Outage Management Systems (DMS&OMS)

- t5: Fault Detection Identification and Reconfiguration (FDIR)

### 4 

### Demand Response (DR)

- t6: TOU/CPP with enabling technologies

- t7: TOU/CPP without enabling technologies

- t8: TOU with enabling technologies

- t9: TO U without enabling technologies

- t10: Direct Load Control (DLC) Energy Storage (ES)

- t11: Thermal Energy Storage (TES) Distributed Generation (DG)

- t12: Solar residential

- t13: Solar commercial

- t14: Solar combined

- t15: Wind commercial 

# 1.1.2 Enabling Technologies

In addition to technologies that provide direct benefits to the system, there are those that enable other technologies to benefit the system, but themselves may not provide a direct benefit. The majority of the projects in the SGIG program have committed to deploying a large number of enabling technologies that do not provide any direct measurable benefit. Despite the lack of a direct benefit, these technologies form the foundation needed for the technologies that do provide direct benefits to the system. 

# 1.1.2.1 Smart Meters

Traditional electromechanical metering devices have proven to be accurate and reliable over multiple decades, but have the significant disadvantage of requiring manual data collection; there is no network connectivity. The deployment of new “ smart meters” is the largest common element to the SGIG projects, ranging from projects with a few thousand, to projects with multiple millions. These new meters are able to bi

- directionally communicate information via a wired or wireless communications n etwork. Communications to the customer can now include time

- based electricity rates or event

- triggered signals. Communications from the customer allow remote meter reading, as well as usage patterns.

### 5 

# 1.1.2.2 Communications Infrastructure

Communications infrastru cture, both wireless and wired, is an excellent example of an enabling technology. A communications infrastructure in an isolated environment does not provide any direct benefit to the system. However, direct technologies and capabilities, such as demand response, would not be possible without a supporting communications infrastructure. For the purposes of the conducted analysis, it is assumed that the required communications infrastructure is available, but it will not be simulated. Zero latency and in finite bandwidth is assumed. While an explicit communications system model is not used in this analysis, there are issues outside the scope of this work where a communications system model would be essential. 

# 1.1.2.3 Human Machine Interface

Human Machine Interfa ces (HMI) can exist in many forms. In a single family residence, the HMI can range from a simple thermostat to a fully functional Home Energy Management System (HEMS). An HMI can allow a residential user to see the current price of electricity, interact with their heating and cooling system, or with an energy storage system. By providing an end

- user with more information about the current price of electricity and the state of their consumption, the effectiveness of demand response opportunities can be in creased. 

# 1.2 Report Structure

The structures of the four technical reports follow a similar design. The four reports share a common introduction in Section 1 with Section 2, discussing the representative technologies to be examined in each report. Section 3 contains the detailed feeder level examination of the impact of each technology, while Section 4 examines the change in the impact metrics between the base case and the case with various technologies. It should be noted that the base case is a represent ative simulation without new technologies; it is not representative of the operation of any actual SGIG project. Section 5 contains the concluding comments. Additionally, there are multiple appendices. Appendices A, B, and C are common to all 4 reports with Appendix A giving a detailed description of the SGIG impact metrics, Appendix B detailing the taxonomy of prototypical distribution feeders, and Appendix C discussing GridLAB

- D and the simulation methodology. Appendix D is specific to each report and contains the plots produced for individual feeders from the simulations. Appendix E contains the impact metric values for each technology and is the basis for the differential impact metrics in Section 4.

The fifth report has a structure independent of t he four technical reports.

### 6 

# 2 Distributed Generation Technology Areas

A study conducted by Electric Power Research Institute (EPRI) indicates that by 2010, 25% of the new generation in the United States will be distributed [4] and a study by the Natural G as F oundation concluded that thi s number could be as high as 30 % [5]

. Distributed G eneration (DG) can be defined as electric power generation from either renewable and /or non
- renewable

resources at the distribution level , or on the customer side of the se rvice meter. A review of the SGIG projects indicated that the inclusion of DG was in a rela tively small number of projects, but because of its importance it is included as one of the four main technologies types.

In the United States , renewable portfolio standards (RPS) have been developed by the individual states in order to obtain a minimum percentage of their electric power from renewable energy resources within a given time frame. The proliferation of RPSs has enabled a contin ual shift towards increas ed penetration level of renewable resources in the power grid [6]

. Currently, there are 24 states which have RPS policies in place and t hese 24 states account for more than half the sales of electricity in the US [6]

. There are numerous advantages to integrating renewable resources

. F or utilities , it might mean less dependen ce on purchasing high cost power during peak periods from spot markets, increasing their generation mix options , taking advantage of subsidies by increasing their renewable portfol io , or increas ing the reliability of the power grid

. F or customers , having renewable resources supplying their energy needs means the potential for smaller electricity bills and the potential to reduce the ir carbon footprint. The

a ddition of renewable distributed resources represents a paradigm shift for the power industry , from the traditional central generation scheme to a more distributed generation pattern

. Because of this fundamental change in operation, the integration of rene wable resources presents significant challenges and opportunities

.

In the United States, t he m ost common ly deployed renewable resources are solar photovoltaic ( PV) and wind turbine generators (WTG)

. Many of the SGIG projects identified the need to develop interface capabilities to support the integration of renewable resources in the coming years , with one project specifying a need for a small scale comme rcial PV d eployment. I t was also observed that some of the utilities are deploying PV units and WTGs as their own utility

wide initiative

. This report examines the effect s, at the distribution level, of adding residential scale PVs , commercial scale PVs , combined residential and commercial scale PVs , an d commerci al level wind turbine generator deployment.

The stud ies associated with the three PV technology types , residential PV, commercial PV, and combined PV , are similar to each other in many aspects. Each of these technology types has a similar impact on the distribution feeder, with the level of the impa ct varying for the three cases

. The size of the PV units and the number of PV units of these three technology types are very different from each other , which results in varying impacts at both the feeder level and point of interconnect

.

### 7 

### The wind turbine ge nerator studied in this report are commercial scale units, small residential type units are not examined. Effects of this installation were examined at the distribution feeder level , giv ing insight in to the effect of large scale wind integration on the dis tribution feeder.

For the analysis conducted in the following Sections , the operation of PV and WTG units is in accordance with the IEEE 1547 standard. Both the solar and wind models in this study provide only real power to the grid and are always grid connected; there is no islanded operation and they do not regulate voltage

. Section 2 .1 will examine the three technologies associated with solar PV and the ir specific implementation s

. Section 2 .2 will examine the technology associated with WTGs and its sp ecific implementation. 

# 2.1 Photovoltaic (PV) Model Implementation

The PV module developed in GridLAB

- D is an electrical model which converts values of incident solar radiation into electrical energy

. Individual PV system s with name plate values of 600 Volt s and 200 Watt s i s used for all residential, commercial, and industrial applications. For the SGIG analysis, the PVs are assumed to be made of single crystal line silicon material with an efficiency factor of 0.2 [10]

. The number of modules in these applicat ions will vary based on the surface area (i.e. , the area covered by the PV arrays) as specified by the user. The DC power output of the solar model can be described by equations (2.1) and ( 2. 2) , selected from [7] :

where:

P DC : DC power output of the solar panel (kW) P STC : DC power output of the solar panel under standard test conditions (kW) G STC : Solar irradiance at STC of 1000 W/m2 T

C : nominal terminal voltage ( °C ) T

STC : STC temperature of the solar cell, 25 °C C T : power temperature coefficient (for single crystal silicon =

- 0.00437/K) T

a : ambient temperature in ( °C )



### Manufacturers often assign an allowable output tolerance to the module’s rating. For example, if this tolerance is +/

- 5 % of the rating, a 100 W module will actually produce 95W

- 105W . The cell temperatures of a solar array will also vary drastically due to the ambient conditions , such as sun intensity, air temperature , etc ., and is also accounted for as g iven in equation ( 2.1 ) . PV arrays eventually get covered with fine layer of dirt and dust, decreasing the amount of light reaching each cell. The amount of power loss is dependent on the location, type of dust, and the length of 

# [ ] T STC C STC

A STC DC 

## C T T G

G P P ) ( 1 * *

− + = (2.1)

/ 800 ) 20 (
2 

### m W C NOCT G T T o
A a C 

### − + = 

### (2.2)

### 8 

### the time since rainfall. Fo r the PV model used in this analysis, an average derating factor of 0.95 is applied to account for manufacturing tolerance and soiling losses (dirt, etc.) in the PV arrays.

Output power for the solar arrays was determined by the solar irradiance of the particular region. Since all of the thermal models were built off typical meteorological year (TMY) data, these data sets were utilized for the PV generators as well. This ke pt the electrical output of the PV units correlated with the thermal flux on the buildings . Such a correlation ensured the solar model was producing an output representative to the conditions the rest of the feeder was experiencing.

For the purposes of th is study, optimum solar tracking was assumed. The total solar insolation (or irradiation) obtained from the TMY data set was applied to the photovoltaic panel. Efficiencies associated with the process are still applied and equation (2.1) still drives the output power. However, no angle of incidence or panel angle was included in the calculations to determine the solar irradiance received at the panel. The DC power output from the solar is then converted to AC power with a single phase inverter model

. Different inverter models have different efficiency levels , but the average conversion efficiency is around 90 % [9] , which is considered for the inverter model used in this analysis

. The inverter model operates in a constant power factor mode , where the c onstant power factor is assumed to be unity for the SGIG analysis , so that only real power from the solar electric system is injected to the distribution grid.


# 2.1.1 

# Residential PVs

Photovoltaics are expected to be the fastest growing renewable at the distribution level due to the large availability of solar resources in the US [8]

. The addition of residential PVs is expected to be the quickest to deploy due to the availability of smaller PV arrays , which are ideal for offsetting some of the major loads of a residential end

- user . Both consumers and utilities have been installing small

- scale PVs in the residential sector. The average r esidential PV unit range s from 3kW

- 5kW and can be easily mou nted on roof

- tops of residential end

- users [10] [11]

. While there have been numerous studie s of PV integration to the grid, limited work has been done to analyze the effect s at the distribution level of residential PV for different climate regions within the United States [12][13]

. It is necessary to analyze the im pact of residential PV because the large number of installations that can be found on a single distribution feeder. Instead of a single large unit with a single point of interconnection , there are multiple units small units at various locations of the feeder

. The large spatial placement of residential PV units presents a challenging analysis problem for planning engineers.

To ensure that the results of this report can be reproduced by other researchers, all of the tools, models, and materials used are openly available at [18]

. Through detailed time

- series simulations

### 9 

### conducted in GridLAB

- D, the impact of addi ng residential PVs to the grid can be examined on the relevant prototypical f eeders. U tilities, regulators, vendors and other stakeholders intere sted in analyses more specific to their systems, goals , and conditions may make use of these open tools for their own purposes. 

# 2.1.1.1 SGIG Impact Metrics Affected by Residential PVs A detailed list of the SGIG impact metrics can be found in Appendix A. These metrics are for all of the SGIG projects. The following SGIG metrics are affected by the addition of residential PVs and will be tracked in this analysis:

Table 2. 1 : Impact m etrics affected by addition of Residential PVs


### Index Metric Units 1 Hourly Customer Electricity Usage kWh 2 Monthly Customer Electricity Usage MWh

3 Peak Generation kW Nuclear % Solar % Bio % Wind % Coal % Hydroelectric % Natural Gas % Geothermal % Petroleum % Distributed Solar PV % Distributed Wind % 4 Peak Load kW 7 Annual Electricity Production MWh 12 CO2 Emissions Tons

13 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons 21 Feeder Real Load kW Feeder Reactive Load kVAR 29 Distribution Losses % 30 Distribution Power Factor pf 39 CO2 Emissions Tons

40 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons

### 10 

# 2.1.1.2 Specific Implementation of Residential PVs

For the SGIG analysis, r esidential PVs are assumed to be roof mounted units, sized from 3kW to 5kW , and randomly distributed on houses based on the per
- feeder penetration level. The level of penetration for the SGIG analysis ranges from 1%

- 6% of the peak feeder load and is depe ndent on the region where the PV is being deployed . These deployments are summarized in Table 2. 2 , using the regions of Figure 2.1 , which is also shown in Appendix B.

Figure 2. 1

. Climate Zones Used for Development of Prototypica l Feeders

Table 2. 2 : Residential PV penetration by region


### Region Number Penetration Level (% of peak feeder load)
1 3 2 1 3 4 4 5 5 6



### The penetration levels selected for this analysis conservative, but are still be able to capture the benefits of the technology deployed. The basis for Table 2.2 is obtained from current solar penetration and wind penetration , given in [15] [16] , and are divided by states. When different states are grouped together to form a t axonomy region, the solar penetration is often less than 1%. In order to capture the benefits of a large scale renewable deployment, the current penetration levels were extrapolated to present penetration levels that were higher than current, but not so h igh as to represent an unrealistic scenario.

### 11 

### The residential PV simulation has been done for only those feeders that have residential houses , so the five GC

- 12.47

- 1 feeders and the R3

- 12.47

- 2 feeder are excluded from this study. Please see Appendix B for a more detailed discussion of the prototypical feeders used in this report. 

# 2.1.1.3 Overall Residential PVs Simulation Results

In this section , we examine the overall simulation results of the addition of residential PVs to the prototyp ical distribution feeders

. At this level of examination , the data will not be divided into monthly values ; only annual values for each feeder will be examine d

. Simulation results for 22 of the 2 8 prototypical distr ibution feeders will be examined for each climate region. As indicated above, 6 of the feeders do not contain any residential end

- use loads and are ignored for this section of the analysis. The following sections will examine the impacts of PV s with respect to peak load, annual energy consumption, system losses, power factor, annual output, and CO 2 

### emissions.


# 2.1.1.4 Annual Peak Load

One of the goals of adding residential photovoltaics to the distribution system is to reduce peak feeder load. However, it is important to note that the addition of PV does not necessarily reduce the peak. The peak load for most residential feeders occurs during the evening while the peak output from solar PV units occurs around noon. As a result, solar PV may reduce the peak load of a feeder, but the reduction wi ll only be a fraction of the aggregate solar PV rating. As a result, the amount of peak load reduction will be vary based on the penetration of residential PV, the time of the feeder peak load, and the climate region.

### 12 Figure 2. 2 : Comparison of p eak l oad by feeder


### From Figure 2. 2 , it can be seen that the addition of PV reduces annual peak load in almost all the feeders , except for R2

- 12.47

- 3. While there is a nominal reduction in most feeders, c ertain feeders have larger peak loa d reduction . This difference is based on the penetration and distribution of PV units , as well as the coincidence between the peak solar and peak load time s
occur . The change in peak load is shown in Figure 2. 3 and the percent change values are shown in F igure 2. 4

.

### 13 Figure 2. 3 : Change in peak load on feeders in ( M W )

Figure 2. 4 : Change in peak demand by feeder ( % )

### 14 

### Figure 2. 3 shows the change in the peak load when compared to the base case. The maximum percentage peak reduction was seen for feeder R1

- 12.47

- 3 . There was a slight increase in peak demand for feeder R2

- 12.47

- 2 , whereas feeder R1

- 2500

- 1 shows no effect on the peak load

. The increase in the peak demand associated with R2

- 12.47

- 2 is a small fraction of the feeder peak demand (~ 1% of annual peak load , as shown in Figure 2. 4 ) . This increase can be explained by voltage dependent loads chang ing their state behavior when voltage is varied, causing the consumption of more power. For ex ample, variable voltages can cause HVAC (Heating, Ventilation and Air Conditioning) units to shift their behavior and can c ause them to consume more power or shift their operations to a time period where there is more coincident load. Also, it is important to note that this behavior is also highly dependent on the feeder type, load composition , and the distribution of PV units

. 

# 2.1.1.5 Annual Energy Consumption

Reduction in annual energy supplied to the feeder is the greatest benefit of the addition of residential PVs

. In contract to peak load reduction, the reduction in annual energy consumption benefits from energy produced by the PV units at any time. Annual energy consumption for a distribution feeder is the total energy that must be sup plied as measured at the head of the feeder. In the case of residential PV deployment, some of the energy that would normally be supplied by the feeder is supplied by the local PV units, thus reducing the annual energy consumption from the perspective of t he feeder. The re d uction in annual energy consumption is shown in Figure 2. 5 for each of the prototypical feeders with residential end

- use customers

.

### 15 Figure 2. 5 : Comparison of annual energy c onsumption by feeder


### As can be seen in Figure 2. 5 , the annual energy consumption provided by the feeder, was reduced for all the feed ers with residential PVs added. With the PV units operating in accordance with IEEE 1547, t he PV units inject ed real power at the point of inter connection which supplied some of the local energy demand. T his was true even for feeder R2

- 12.47

- 2 which had showed

an annual peak load increase because of interactions with the end

- use loads , as seen in Figure 2.4 . Despite the negative impact on peak load, the annual energy consumption was stil l decreased because the PV units injected real power into the feeder.
Th e change in annual energy consumption , when compared to the base case , is given by Figure 2. 6

. Figure 2.7 demonstrates that r egion 5 shows the largest percentage reduction in the energy consumption . This is primarily because it has the largest penetration of PV units as compared to its feeder load, and because Region 5 has the highest solar resource.

### 16 F igure 2. 6 : Change in annual energy consumption by feeder (MWh)

Figure 2. 7 : Change in annual energy consumption for feeders ( % )

### 17 

### The largest reduction in energy consumption is seen for feeder R5

- 12.47

- 2 in region 5 , w here the solar penetration was 6 % of the peak feeder load and solar incidence was highest

. From the full year simulations, region 4 ’s solar penetration of 5% resulted in the second largest reduction in energy consumption.


# 2.1.1.6 Annual System Losses

One of the benefits of add ing PVs at the distribution level is the reduction of system losses. When electrical current flows through a conductor, some of that energy is lost in the form of heat. In the case of alternating current distribution lines, further energy can be lost to inductive losses on the conductor. At peak load , the amount of current flowing through a conductor is at its highest , generating the greatest losses . The total current f low in a conductor is a combination of the current flows associated with real and reactive power components. As a result , reducing either the real or reactive power flow will reduce the losses associated with that current. Solar PV reduces current flows b y reducing the amount of real power that a feeder must supply, thereby reducing the current and losses. Figure 2. 8 shows the annual system losses calculated as losses downstream of the distribution feeder head

. In this case , the PV injects only real power back to the distribution grid and hence affects only the real part of the current flowing through the conductor

.

Figure 2. 8 : Comparison of annual losses by feeder

### 18 

### The annual losses were reduced for all the feeders when residentia l PV units were added to the system; however, the level of reduction varied between the different feeders. For example , feeder R5

- 12.47

- 3 saw the largest reduction in system losses compared to other feeders , as seen in Figure 2. 10

.

The red uction in the system losses are not necessarily proportional to the level of PV penetration , but are also dependent on the system type and the placement of the PV units. Since the PV unit s w ere randomly placed , it i s possible that the largest load , or an entire branch of the feeder , did not have a PV unit associated with it

. T herefore , the losses contributed by it will not be offset and the reduction in losses seen at the feeder level will be small. The energy loss reduction is also a function of the fe eder loading, because a more heavily

- loaded system will tend to have higher losses

. Therefore, the addition of DG in the form of PV will reduce line losses whenever there is PV output . However, maximu m loss reduction will be achieved when solar generation is during the feeder peak loading time.

Figure 2. 9 : Change in annual losses by feeder (MWh)

### 19 Figure 2. 10 : Change in annual losses by feeder ( % ) 

# 2.1.1.7 Power Factor Impact

The deployment of residential PV units that only produce real power will have an impact on the power factor of the distribution feeders. Since the PV units provide an aggregated amount of real power, this amount of real power will not need to be supplied by the feeder, but the reactive

r equirements are still similar. As a result, it is possible for the power factor as seen at the substation to degrade and move farther away from unity. Figure 2.11 shows the variation of the average power factor over the course of the year for the prototyp ical feeders with residential end

use customers.

### 20 Figure 2. 11 : Comparison of average annual power factor by feeder 

### Figure 2. 1 2 shows the minimum annual power factor for all the feeders. It can be seen that the power factor seems to be more lagging on a few feeders when residential PVs are integrated. This can be explained in the fact that the PVs are only injecting real power to the grid , and there is no control to regulate the reactive power . Without any significant change to the reactive power components of the system, the power factor lag s

. Overall, this lagging power factor was not improved . This emphasiz es the need for reactive power control , either integrated in the in the inverter system , or via addition of automatic capacitor banks on the feeder to perform reactive power control. The next version of IEEE 1547.8 will include guidelines for how to operate distributed resources in other than unity mode.

### 21 Figure 2. 12 : Comparison of minimum annual power factor by feeder 

# 2.1.1.8 Annual Residential PV output

Figure 2. 1 3 shows the annual power output by residential PV arrays added to the dist ribution feeder

. Recall that r egion 5 had the highest penetration of PV units , a s well as the highest PV output as a percent of its peak load

. As such, it is not surprising that it has the highest average PV energy output for all five regions.

### 22 Figure 2. 13 : Annual residential PV power output by feeder 

# 2.1.1.9 Annual CO 2 

# Emissions

One of the most important benefits of renewable resources for electricity production is reducing the emissions associated with carbon dioxide, particulate matter , and other harmful gases. Environmental emissions for each feeder were estimated usi ng a simple dispatch algorithm. Generation sources were sized by the regional generat ion types, and ranked to dispatch in an appropriate order. Full commitments were achieved before proceeding to the next generator. For example, consider a region where natural gas turbines dispatch first to support 250 MW of load, followed by 400 MW of petroleum

- fired generation. To support 300 MW of load, the natural gas unit is fully dispatched, then the remaining 50 MW is attributed to the petroleum

- fired generation. Representative heat rates and emission rates are then applied to these power outputs to determine the overall environmental impacts. The details of these rates, along with the dispatch orders and penetration levels for each regio n, are explained in Appe ndix B

.

Figure 2. 1 4 shows the annual CO 2 

### emissions for both the base case and the residential PV case.

### 23 Figure 2. 14 : Comparison of total annual CO 2 emission by feeder 

### Figure 2. 1 4 shows that the CO 2 

### emissions were reduced o n all the feeders when residential PVs were added to the grid

. The greater the reduction in energy consumption, the more potential there is for a reduction in emitted CO 2 

### . Region 2 received the lowest amount of PV penetration and showed the smallest impro vement in energy consumption reduction , as was seen from Figure 2. 7 . Hence, region 2 also demonstrated the smallest reduction in CO 2 

### emission s. R egion 5 received higher penetration of PV as fraction of the feeder peak load and showed the highest reduction in CO 2 

### emissions

. Figure 2. 1 5 and Figure 2. 1 6 show the emission differences in both tons and percentages when compared to the base case. As expected, the addition of residential PV decreased the carbon dioxide emissions on all the feeders

.

### 24 Figure 2. 15 : Change in total annual CO 2 emissions by feeder (tons)

Figure 2. 16 : Change in total annual CO 2 emissions by feeder (%)

### 25 

# 2.1.2 C ommercial PVs

The a ddition of commercial scale PVs is gaining popularity as the penetration of renewable generation sources increases

. Both utilities and businesses are taking the initiative in installing commercial scale PV systems

. Commercial PV arrays are generally larger in size when compared to residential PV i nstallations and can be either rooftop mounted or ground mounted , either as one large unit or connected to the grid as solar farms.

As with the residential PV, the t ools, models, and materials used to model the commercial PV placements are openly availabl e at [18]

. 

# 2.1.2.1 SGIG Metrics Affected by Commercial PVs The following SGIG metrics are affected by commercial PVs and will be tracked in this analysis:

Table 2. 3 : Impact metrics affected by addition of Commercial PVs



### Index Metric Units 1 Hourly Customer Electricity Usage kWh 2 Monthly Customer Electricity Usage MWh

3 Peak Generation kW Nuclear % Solar % Bio % Wind % Coal % Hydroelectric % Natural Gas % Geothermal % Petroleum % Distributed Solar PV % Distributed Wind % 4 Peak Load kW 7 Annual Electricity Production MWh 12 CO 2 Emissions Tons

13 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons 21 Feeder Real Load kW Feeder Reactive Load kVAR 29 Distribution Losses % 30 Distribution Power Factor pf 39 CO 2 Emissions Tons

40 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons

### 26 

# 2.1.2.2 Specific Implementation of Commercial PV The prototypical feeders were populated with PV units as commercial end

- use locations

. The various types of commercial loads include : office buildings, strip malls , and big box retailers

. The commercial loads were populated with 100kW solar arrays , randomly distributed across the commercial end

- use customers on the feeders. The solar penetration levels were similar to the residential PV penetration levels for all the regions , as shown in Table 2.4

. The region al breakdown is the same as was shown in Figure 2.1

. Table 2. 4 : Commercial PV penetration by region 

### Region Number Penetration Level (% of peak feeder load) 1 3 2 1 3 4 4 5 5 6



# 2.1.2.3 High Level Commercial PV Simulation Results

In this se ction , the aggregated annual simulation results will be examined. Simulation results for each of the prototypical distribution feeders will be examined, including five case s of GC

12.47

- 1; one for each climate region. The high level analysis will examine the impacts of PVs with respect to peak load, annual energy consumption, system losses, power factor, annual output, and CO 2 

### emissions. 

# 2.1.2.4 Annual Peak Load As observed in the previous case with residential installations , peak reduction might not necessarily be a benefit of commercial PV integration , as shown by Figu re 2. 1 7

.

### 27 Figure 2. 17 : Comparison of peak load by feeders 

### The maximum peak reduction observed was seen on feeders R4

- 12.47

- 2

. An increase in peak load was seen on four of the feeders and was equal to no more than 0.8 % , as seen in Figure 2. 1 9

. As was the case with adding PV units to the residential loads, having end

- use loads with voltage dependence can sometimes create increases in peak because of increases in local voltage as well as changes in operating states. Also, as explained with the residential PV units , PV peak output doesn’t necessarily occur at the same time as the feeder peak load , so the ability to reduce the peak is a function of locations as well as end

- use load composition

.

### 28 Figure 2. 18 : Change in peak load by feeder (M W)

Figure 2. 19 : Change in peak load by feeder (%)

### 29 

# 2.1.2.5 Annual Energy Consumption

The a ddition of commercial scale PV reduce s the average annual energy consumption for all the feeders , as shown in Figure 2. 20

. Similar to the previous residential annual energy consumption is the energy that must be supplied by the feeder via a substation transformer

.

Figure 2. 20 : Comparison of annual energy consum ption by feeder (MWh)


### From Figure 2. 2 1 and Figure 2. 2 2 , it can be observed that the maximum reduction in annual energy consumption that can be achieved by a single feeder is about 1.3 GWh , or 3 % , which

occurs on feeder R3

- 12.47

- 3. Similar to the performance of the residential PV units , the reduction in annual energy consumption is not only dependent on the penetration level of PV

. For example , region 5 had the largest PV penetration , but the feeders in region 3 show a slightly higher reduction in annual energy consumption

. Factors such as end

- use load composition and climate will also have an impact on the reduction of annual energy consumption.

### 30 Figure 2. 21 : Change in annual energy consumption by feeder (G Wh)

Figure 2. 22 : Change in annual energy consumption by feeders (%)

### 31 

# 2.1.2.6 Annual System Losses

The addition of commercial PV units also results in increased system efficiency by reducing system losses , as shown in Figure 2.23

. The a nnual system losses for each feeder are examined and it is seen that they decreased due to the same reasons described in Section 2 .1.1.6 for the residential PV integration.

Figure 2. 23 : Comparison of total annual losses by feeder


### As seen from Figure 2. 2 4 and Figure 2. 2 5 , losses were reduced for nearly all the feeders

. However, feeder R4

- 2500

- 1 showed no noticeable change . The largest loss reduction w as seen on feeder R5

- 12.47

- 3

. The reduction in losses varies for the different feeders since l osses are affected by a number of factors , such as end

- use load compositio n, equipment sizing, feeder design , and most importantly the distribution of the PV units on the feeder . These various factors cause the imb alance in the observed load reduction previously described in Section 2 .1.1.6

.

### 32 Figure 2. 24 : Change in annual losses by feeder (MWh)

Figure 2. 25 : Change in annual losses by feeder (%)

### 33 

# 2.1.2.7 Power Factor Impact

The deployment of commercial PV units that only produce real power will have an impact on the power factor of the distribution feeders, similar to the residential PV units. Since the PV units provide an aggregated amount of real power, this amount of rea l power will not need to be supplied by the feeder, but the reactive requirements are still similar. As a result, it is possible for the power factor as seen at the substation to degrade and move farther away from unity. Figure 2.2 6 shows the variation of the average power factor over the course of the year for the

Figure 2. 26 : Comparison of average annual power factor by feeder

### 34 Figure 2. 27 : Comparison of minimum annual power factor by feeders


### As observed in the residential PV installations and in Figure 2.27 , the lagging power factor was not improved for the feeders with a larger lagging power factor. This again emphasizes the potential benefit of including reactive power control s , either integra ted in the in the inverter system or via addition of automatic capacitor banks on the feeder

. Once again, the next release of IEEE 1547.8 will include guidelines for distributed resources producing other than unity power factor power. 

# 2.1.2.8 Annual Commercial PV output

Figure 2. 2 8 shows the annual energy produced by the addition of commercial PV

. The commercial PV output shown is the sum of the commercial PV installation s on office buildings, strip mall s and big box retailers

. The largest PV output is observed for feeder R5

- 35

. 00

- 1 in region 5 , which contain ed a commercial PV penetration of 6% of the feeder peak load. Feeders in region 4 received the second largest commercial PV penetration (5% of feeder peak load) , but do not have the second highest PV output

. Th is indicates that PV output in a region is not only dependent on the penetration level . In this study, fixed PV array s of 100kW were distributed on commercial loads , one array per load, which limited the number of PV units on the feeder based on the num ber of commercial end

- use customers

.

### 35 Figure 2. 28 : Annual commercial PV output by feeder 

# 2.1.2.9 Annual CO 2 

# emissions

Figure 2. 2 9 , Figure 2. 30 , and Figure 2. 3 1 show the annual reduction of carbon dioxide emitted by generati ng resources as a result of adding PV units to commercial end

- use loads

. Similar to the residential PV installations, a reduction in energy consumption reduces the need for more carbon

- intense sources of power ; thus , reducing carbon dioxide emission. The details for t he emissions calculations are again outlined in Appendix B

.

### 36 Figure 2. 29 : Change in total annual CO 2 emissions by feeder ( kilo tons)

Figure 2. 30 : Change in total annual CO 2 emissions by feeder (tons)

### 37 Figure 2. 31 : Change in total annual CO 2 emissions by feeder (%) 

# 2.1.3 Addition of both commercial and residential (Combined) PVs

In the previous sections residential and commercial PV installations we examined as separate cases. In reality, these installations could be occurring simultaneously on the same distribution feeder. Individually , these installations (residential and comme rcial ) have different level s of impact on the distribution feeder. When both residential and commercial installations coexist on the same feeder , they will have a cumulative impact . This concurrent deployment needs to be analyzed for distribution planning purposes and proper resource deployment. 

# 2.1.3.1 SGIG Metrics Affected by Com bined PVs The following SGIG metrics are affected by the addition of combined commercial and residential PVs on to the distribution system and will be examined in this report.

### 38 Table 2. 5 : Impact metrics affected by the addition of combined PVs



### Index Metric Units 1 Hourly Customer Electricity Usage kWh 2 Monthly Customer Electricity Usage MWh

3 Peak Generation kW Nuclear % Solar % Bio % Wind % Coal % Hydroelectric % Natural Gas % Geothermal % Petroleum % Distributed Solar PV % Distributed Wind % 4 Peak Load kW 7 Annual Electricity Production MWh 12 CO 2 Emissions Tons

13 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons 21 Feeder Real Load kW Feeder Reactive Load kVAR 29 Distribution Losses % 30 Distribution Power Factor pf 39 CO 2 Emissions Tons

40 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons 

# 2.1.3.2 Spec ific Implementation of Combined PV

Combined PV used penetration levels , as shown in Table 2.6, for different region s , but had both commercial scale (100kW array size) and residential scale PV units (~3kW

- 5kW array size) . These PV units were populated randomly over the prototypical feeders at the appropriate end

- use customer types

. Seven feeders , which were purely residential or purely commercial , were excluded from th i s simulation. Figure 2.32 again shows the various portions of the continental United States each climate region represents.

### 39 Figure 2. 32 Climate Zones Used for Development of Prototypical Feeders

T able 2. 6 : Com bined PV penetration by region 

### Region Number Penetration Level (% of peak feeder load) 1 3 2 1 3 4 4 5 5 6



# 2.1.3.3 High Level Combined PV Simulation Results

In this section , the high level results of the combined PV simulations are examined . At this level of examination , the data is not divided into monthly values ; only an nual values are exami ned. Simulation results of 21 prototypical feeders are examined , which excludes the feeders that are purely residential or purely commercial

. The high level analysis will examine the impacts of PVs with respect to peak load, annual energy consumption, system losses, power factor, annu al output, and CO 2 

### emissions. 

# 2.1.3.4 Annual Peak Load

As seen in previous s ections for residential PV integration and commercial PV integration, pe ak reduction is not the primary benefit from adding PV units to a distribution feeder. However, it is interesting to note that peak reduction is observed for all but one of the feeders under the combined PV case

. Since there was an equal penetration of commercial and residential PV units on the feeder s , this helped miti gate the voltage issues cause d when a large PV unit , as in the case of commercial PV unit , was added to the grid ; thus , reducing the effect that variations in the line voltage had on the energy consumption of the end

- use load s

. Figure 2. 33 shows the comparison

### 40 

### of annual peak load reduction for all the feeders under study

. Figure 2. 3 4 and Figure 2. 3 5 show the change in annual peak load due to the addition of combined PVs.

Figure 2. 33 : Comparison of peak load by fee der

### 41 Figure 2. 34 : Change in peak load by feeder (M W)

Figure 2. 35 : Change in peak load by feeder (%)

### 42 

# 2.1.3.5 Annual Energy Consumption

As seen in the residential and commercial PV installations, the reduction of annual energy consumption is the primary benefit of installing PV units . As shown in Figure 2. 3 6 , e nergy consumption is reduced for all the feeders when a combination of PV units are installed on the distribution feeders

. Figure 2. 3 7 and Figure 2. 3 8 show the energy savings in both kW and percent when compared to the base case.

Figure 2. 36 : Comparison of annual energy consumption by feeder (G Wh)

### 43 Figure 2. 37 : Change in annual energy consumption by feeder (G Wh)

Figure 2. 38 : Change in annual energy consumption by feeder (%)

### 44 

# 2.1.3.6 Annual System Losses

As stated in previous s ections , the reduction of distribution system loss es is also one of the primary benefits of PV installation. It can be observed from Figure 2. 3 9 , Figure 2. 40 , and Figure 2. 4 1 that the integration of combined PV also results in the reduction of system losses. Although the amount of the reduction in losses is different for different component s and different feeders , it can be concluded that the addition of PV general ly increases system efficiency by reducing real power loss es

.

Figure 2. 39 : Comparison of total annual losses by feeder (MWh)

### 45 Figure 2. 40 : Chan ge in annual losses by feeder (G Wh)

Figure 2. 41 : Change in annual losses by feeder (%)

### 46 

# 2.1.3.7 Power Factor Impact

The deployment of a combination of residential and commercial PV units is similar to the results that were seen for purely residential and purely commercial. The deployment of PV units that only produce real power will have an impact on the power factor o f the distribution feeders. Since the PV units provide an aggregated amount of real power, this amount of real power will not need to be supplied by the feeder, but the reactive requirements are still similar. As a result, it is possible for the power fa ctor as seen at the substation to degrade and move far ther away from unity. Figure 2.42 shows the variation of the average power factor over the course of the year while Figure 2.43 examines the minimum annual power factor.

Figure 2. 42 : Comparison of annual average power factor by feeder

### 47 Figure 2. 43 : Comparison of minimum annual power factor by feeder 

# 2.1.3.8 Annual Combined PV Output

Figure 2. 4 4 shows the combined annual PV output for each of the prototypical feeders. Similar to the results for residential and commercial PV deployments , it can be observed that feeders in region 5 had the largest PV output. Consistent with both the individual residential and commercial studies, the largest amount of PV production was not necessarily only dependent on the amount of PV penetration in that region. Region 5 had the highest PV penetration and the greatest overall output, but region 4 contained the second highest penetration levels. However, it does not have the second highest PV output.

### 48 Figure 2. 44 : Annual combined PV output by feeder 

# 2.1.3.9 Annual CO 2 

# emissions

Similar to the other PV integration technologies discussed in previous s ections , carbon dioxide emissions for the feeder were reduced with the addition of PV. F igure 2. 4 5 , Figure 2. 4 6 and Figure 2. 4 7 demonstrate that annual carbon dioxide emissions are reduced when the energy consumption is reduced. The amount of CO 2 

### reduced is directly proportional to the amoun t of energy consumption reduced and will also vary depending on the generation mix for the region.

### 49 Figure 2. 45 : Comparison of annual CO 2 emission by feeder

Figure 2. 46 : Change in annual CO 2 emission by feeder ( kilo tons)

### 50 Figure 2. 47 : Change in annual CO 2 emission by feeder ( % ) 

# 2.2 Addition of Commercial WTG
In this section , the addition of commercia l wind turbine generators at the distribution level will be examined. As the delivered cost of wind power from large wind turbines continue s to fall, the cost of wind power becomes eve r more competitive with utility supplied power. The improving economics of wind generation has prompted schools, businesses, and ranchers to consider the possibility o f installing a single large wind turbine to sell power to the utility , or to offset their own electrical needs. The SGIG projects have mentioned the development of interface capabilit ies for the future addition of community

- scale wind turbines

. Given these deployments, WTG inclusion in this report is in the interest of completeness , along with the view that increasing penetration in the distribution infrastructure is inevitable . When compared to solar, small

- scale wind turbines at the residential level ha ve still not gained popularity. For the purpose s of the SGIG analysis , a large

- scale wind turbine connected to the distribution feeder was considered to simulate installations currently carried on by utilities in the US.

Much like the photovoltaic simulatio ns, the analysis can be replicated and adapted by interested parties using the GridLAB

- D materials available at [18]. A ll of the tools, models, and materials used by the researchers for this portion of the study a re openly available

.

### 51 

# 2.2.1 SGIG Metrics Affected by Commercial WTG The following SGIG metrics are affected by the addition of commercial WTG and will be tracked in this analysis:

Table 2. 7 : Impact metrics affected by commercial WTG


### Index Metric Units 1 Hourly Customer Electricity Usage kWh 2 Monthly Customer Electricity Usage MWh

3 Peak Generation kW Nuclear % Solar % Bio % Wind % Coal % Hydroelectric % Natural Gas % Geothermal % Petroleum % Distributed Solar PV % Distributed Wind % 4 Peak Load kW 7 Annual Electricity Production MWh 12 CO 2 Emissions Tons

13 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons 21 Feeder Real Load kW Feeder Reactive Load kVAR 29 Distribution Losses % 30 Distribution Power Factor pf 39 CO 2 Emissions Tons

40 SOx Emissions Tons NOx Emissions Tons PM

- 10 Emissions Tons



# 2.2.2 Specific Implementation of Commercial WTG

This simulation involved the modeling of a single large

- scale wind turbine on a purely commercial prototypical feeder. The wind turbine in our simulation is a 1 .8 MW unit based on a device produced by Vestas . The effect of the addition of these turbines on a commercially loaded feeder is examined. Severa l locations , such as the beginning of the feeder, mid

- feeder , and at t he end of the feeder, were examined to determine a desirable installation location. The best overall energy savings occurred during the mid

- feeder placement, so this point was used for all simulations. Furthermore, this simulation represented a more real istic scenario where the commercial wind turbine is not located directly near the substation.

### 52 

### The theoretical power available in the wind ( wind 

## P ) is given by equation 2. 3 , where ρ 

### is the air density, A 

### is the area swept by the rotor, 

## v is the wind velocity , and R is the blade radius.

3 2 3 

### 2 1 2 1 v R Av P

wind 

### ρπ ρ = = 

### (2.3)



### The fraction of the power extracted from the wind by a practical wind turbine is usually represented by p 

## C
, which is the coefficient of performa nce , or power coefficient. T he actual mechanical power outpu t can be written as equation 2.4 , where p 

## C is a function of tip speed ratio, 

## λ

, and blade pitch angle, β 

### (deg). The tip ratio is defined in equation 2.5 , where, R 

### ω 

### is the mechanical angular speed. 

## ( ) β λ ρπ , 2 1 3 2 p mech 

### C v R P = 

### (2.4)



### v R R 

### ω λ = (2.5)



### In the model used for the SGIG analysis, p 

## C is calculated using the Vestas manufacturer specification sheet [17] . The wind turbine model consists of two parts: the mechanical model and the electrical model. The mechanical power output is achieved using equations 2.3 and 2.4

. Wind data for this mechanical input is once again obtained from typical meteorological year data. To keep power output consistent with weather conditions affecting the rest of the simulation models, the 1

- hour reading from the TMY file was utilized for the wind speed. Direction was ignored for these simulations and it is assumed the turbine had the ability to track into the wind. The converted mechanical power is then used by a 90% efficient synchronous generator to convert the mechanical power to electrical power. While the elec trical model for the generator is simplistic, it is adequate to examine the impacts of WTG operation on prototypical feeders. If specific distribution feeders were to be analyzed, significantly more accurate electrical models of the WTGs would be necessary.


# 2.2.3 High Level Commercial WTG Results

In this section , the high level results of the addition of a single large WTG conne cted to the distribution feeder will be examined . At this level of examination , the data will not be divided into monthly values; only an nual values will be examined. Simulation results fo r the five purely commercial GC

- 12.47

- 1 feeders will be examined for each taxonomy region. The high level

### 53 

### analysis will examine the impacts of WTG s with respect to peak load, annual energy consumption, system losses, power factor, annual output, and CO 2 

### emissions. 

# 2.2.3.1 Annual Peak Load

Figure 2. 4 8 shows a c omparison of peak load on feeders when a commercial wind turbine is added to the feeder compared to the base case

. The direct changes are shown in Figure 2. 4 9 and Figure 2. 50 . As the differences show , peak demand is reduced for all the feeders except for GC

12.47

- 1 _R4 . This is similar to the behavior observed from the solar integration studies

. As with the solar cases , peak demand reduction is not necessarily a benefit of commercial WTG and is largely depen dent on the feeder characteristics and the end

- use load behavior. However, it can be noted that the peak increase is less than 0.5 % when compared to base case peak demand.

Figure 2. 48 : Comparison of peak load by feeder

### 5 4 Figure 2. 49 : Change in peak load by feeder ( M W )

Figure 2. 50 : Change in peak load by feeder (%)

### 55 

# 2.2.3.2 Annual Energy Consumption

Figure 2. 5 1 shows the comparison of annual energy consumption for all the feeders. Similar to the PV technologies, the addition of a WTG reduces the annual energy consumption for all the feeders; it is this reduction in annual energy consumption that is the primary benefit of WTGs

. Figure 2. 5 2 and Figure 2. 5 3 show that a maximum of 4 GWh , or 22% , of energy savings can be achieved by the addition of a single 1.8 MW wind turb ine to the GC feeders within the taxonomy. Since the GC feeders have a peak load of ar ound 5 MW a single 1.8 MW WTG represents a very high penetration level for that single feeder

.

Figure 2. 51 : Comparison of annual energy consumption by feeder (G Wh)

### 56 Figure 2. 52 : Change in annual energy consumption by feeder (G Wh)

Figure 2. 53 : Change in annual energy consumption by feeder (%)

### 57 

# 2.2.3.3 Annual System Losses

The reduction of system losses is another benefit of integrating a commercial WTG. Figure 2. 5 4 shows the comparison of annual system losses by feeder. Figure 2. 5 5 and Figure 2. 5 6 show the reduction in annual system losses for the five GC feeders. The newer WTG models implemented provide for reactive power compensation , and thus improve the performan ce of the WTG. Reductions in losses were quite large because of the design characteristics of the GC feeders. The GC feeders are relatively small feeders serving a small number of large commercial customers. In this configuration there are few laterals so that reducti ons in line flows affect sectionals where the majority of the feeder power flows. As a result, the loss reductions for the GC feeders occur primarily in the series elements of the overhead lines.

Figure 2. 54 : Comparison of total annual losses by feeder

### 58 Figure 2. 55 : Change in annual losses by feeder (MWh)

Figure 2. 56 : Change in annual losses by feeder (%)

### 59 

# 2.2.3.4 Annual Wind Power Output Figure 2. 5 7 shows t he total annual wind power output for all the commercial GC feeders

. The GC feeder in region 1 had the highest amount of wind power generated. This figure is very useful because the decision to deploy commercial WTGs in a region is partially dependent upo n on the capacity of wind power that can be extracted in a particular region. From Figure 2. 5 7 , it is observed that feeders in region 1, 2, and 5 are most favorable for commercial wind installations. While this generalization to the regional level is usef ul for some applications, wind resources within a region will vary greatly. As a result, the deployment of a WTG on a specific feeder would need climate information with a higher level of granularity than provided by regionalization of the nation.

Figur e 2. 57 : Comparison of annual wind output by feeders 

# 2.2.3.5 Annual CO 2 

# E mission s

Reduction of carbon dioxide emissions is another important benefit from the integration of WTG s at the distribution level . The amount of carbon dioxide emitted is a result of the energy consumed and the mix of generating resources in that region . The greater the energy consumption, in general, the greater the CO 2 

### emission will be

. Figure 2. 5 8 compares the annual CO 2 

### emission by feeder. Figure 2. 5 9 and Figure 2. 60 show the change in annual carbon dioxide emissions for the GC feeders. As was the case with the added solar distributed generation, adding wind turbines decreased the energy consumed by the feeder. This reduction resulted in a direct reduction of CO 2 

### emissions.

### 60 Figure 2. 58 : Comparison of annual CO 2 emission by feeders

Figure 2. 59 : Change in annual CO 2 emission by feeders (tons)

### 61 Figure 2. 60 : Change in annual CO 2 emission by feeders (%)

### 62 

# 3 Detailed Individual Prototypical Feeder Results

Due to the large number of plots generated by the simulations , it is not practical to place all of the results in this section. Section 3 will examine the output re sults of a single feeder and the output for the remaining feeders will be provided in Appendix D

. Analysis presented in this section can be extended to other feeders detailed in Appendix D. 

# 3.1 Residential PV For residential PV, there are 6 plots that wil l be displayed for each feeder : peak monthly demand, monthly energy consumption, monthly losses, monthly PV output, and monthly CO 2 

### emissions. 

# 3.1.1 Example Feeder R1

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- 1 Figure 3.1 through Figure 3.5 show the monthly plots that are ge nerated for feeder R1

- 12.47

- 1

. This particular feeder belongs to climate region 1 , which represents the west coast of the United States , and typically ha s a temperate climate. This region received a penetration of residential PV units equal to 3% of feeder peak load. This particular feeder , R1

- 12.47

- 1, has a moderate suburban and rural population with almost 1 , 200 single family residences and is ideal for the analysis of residential PV installation. Figure 3.1 show s a comparison of peak load by month for the simulations on this feeder. It is observed that peak load was reduced for all the feeders when residential PV was installed on the system.

### 63 Figure 3. 1 : Comparison of peak load by month for R1

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- 1 

### Figure 3.2 show s a comparison of reductions in energy consumptio n when residential PV units were added to this distribution feeder. As stated in Section 2 , a reduction in the energy consumption is the most important benefits achieved by residential PV installations. The r esults demonstrate that energy consumption was reduced for every month under simulation.
It is i nteresting to note that the overall energy consumption decreased during the period of high solar availability ( i.e. , summer and spring months )

. Temperature impacts on the thermal models used for residential and commercial buildings are the likely cause of this decrease. However, the introduction of residential PV still served to reduce the overall energy consumption during these months.
As seen in Figure 3.1 , the month s of December and January showed a very slight increase of peak load; however , the energy consumption is reduced for the same month s , as seen in Figure 3.2

. Since the peak solar output typically does not coincide with the pe ak feeder load, a direct reduction did not occur. Furthermore, the change in voltage caused by the solar generation altered the states of the different loads in GridLA B

- D’s voltage

- dependent models . As such, the devices would be operating in a slightly d ifferent manner during the feeder peak, and may consume more power at this time. However, the PV was still providing energy into the system at some point, so the overall energy consumption was reduced

.

### 64 Figure 3. 2 : Comparison o f energy consumption by month for R1

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- 1


### The reduction of l osses is also a benefit associated with residential PV installation s , as was demonstrated in Section 2 . The monthly losses plots for ’ Base’ and ‘ResPV’ are shown in Figure 3.3 for 4 different loss types : losses in overhead lines ‘OHL’, underground lines ‘UGL’, transformers ‘TFR’, and triplex (secondary) lines ‘TPL’. As can be seen from the figure, losses were decrease d for all the components in the distribution system. The largest loss savings w ere achieved on overhead lines and distribution transformers. Higher savings can be achieved with higher residential PV penetration on this system.

### 65 Figure 3. 3 : Comparison of l osses by month for R1

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- 1 

### Figure 3.4 shows the monthly PV output profile for feeder R1

- 12.47

- 1. As previously mentioned , this feeder received a penetration of residential PV units equal to 3% of feeder peak load

. As expected, peak PV output occurred during the expected peak solar interval: s ummer. The months of June and July produced the most energy output from the solar distributed generation.

### 66 Figure 3. 4 : Comparison of PV output by month for R1

- 12.47

- 1 

### Figure 3.5 shows the carbon dioxide emission comparison to the base case when residential PV units were deployed on feeder R1

- 12.47

- 1. The reduction in energy consumption is directly related to the amount of carbon dioxide emission reduction . For these studies, the addition of residential PV units decrease s the CO 2 

### emissions with a strong seasonal influence

.

### 67 Figure 3. 5 : Comparison of CO 2 e missions by month for R1

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- 1 

# 3.2 Commercial PV Only the feeders which had commercial loads were simulated in this category , therefore only 27 out of the total 28 prototypical feeders were simulated. Feeder R2

- 12.47

- 3 did not have any commercial loads and thus was not selected for this simulation. For commercial PV, there are 5 plots that wi ll be displayed for each feeder : peak monthly demand, m onthly energy consumption, monthly losses, monthly PV output, and monthly CO 2 

### emissions. 

# 3.2.1 Example Feeder GC

- 12

- 47

- 1 _R1

Figure 3.6 through Figure 3.10 show the monthly plots that are generated for feeder GC

- 12.47

- 1 _R1

. This feeder belongs to region 1 , which represents the west coast of the United States and has a temperate climate. This region received a penetration of commercial PV units equal to 3% of feeder peak load , each with fixed array size s of 100kW. This feeder is comprised of large commercial loads, such as a large shopping mall , and is well suited for the analysis of the addition of commercial PV units.

### 68 Figure 3. 6 : Comparison of peak load by month for GC

- 12.47

- 1_R1 

### Reduction of peak load is not a primary benefit of commercial PV installations ; however , there is the potential for some reduction in peak load

. Figure 3.6 shows a comparison of peak load for months under the simulation. Despite not being a primary benefit of PV, the feeder peak load was reduced for al l months except for the month of Nov ember

.

### 69 Figure 3. 7 : Comparison of energy consumption by month for GC

- 12.47

- 1_R1 

### As discussed in previous s ections , reduced energy consumption is the primary benefit of commercial PV units . The reduction in energy consumption can be seen in Figure 3.7 for feeder GC

- 12.47

- 1_R1 . The figure shows that energy consumption was reduced for all the months and that the maximum energy reduction appears to occur in the months when solar availability i s high, e.g. July and August.

### 70 Figure 3. 8 : Comparison of losses by month for GC

- 12.47

- 1_R1


### As discussed in the residential PV installation case , the reduction of distribution system loss es is a benefits of PV installation. Figure 3.8 shows a comparison of m onthly losses plots ’ Base’ and ‘ComPV’ for 4 different loss types: losses in overhead lines ‘OHL’, underground lines ‘UGL’, transformers ‘TFR’, and triplex (secondary) lines ‘TPL’. It can be seen in the figure that the major ity of losses for feeder GC

- 12.47

- 1_R1 occur in the underground lines and the major loss reduction occurs for that component when commercial PV units are added to feeder

. However, t he overhead line losses increase when commercial PV is installed on the fe eder. This can be explained by the fact that the commercial PV units were of fixed array size and not sized appropriately based on the demand of the end

- use load . This sizing increas ed the potential for the excessive amount s of reverse current flow in the overhead lines , caus ing the losses to increase. Therefore, appropriate sizing of PV is important to achieve a reduction in losses for every component of the distribution system.

Figure 3.9 shows the monthly PV output for GC

- 12.47

- 1_R1 feeder. As expected, the PV output is higher for summer months when compared to winter months.

### 71 Figure 3. 9 : PV output by month for GC

- 12.47

- 1_R1

Figure 3. 10 : Comparison of CO 2 emissions by month for GC

- 12.47

- R1

### 72 

### Reduction in energy consumption leads to a reduction in carbon dioxide emissions , a s discussed in previous sections. Figure 3.10 shows that carbon dioxide emissions were reduced for all months for GC

- 12.47

- 1_R1. 

# 3.3 Combined PV

There were 21 prototypical feeders out of a potential 28 simulated in this category ; a ny feeder with purely residential or purely commercial loads was not simulated. These simulations focused on the analysis when both residential PV units (~3kW

- 5kW) and commercial PV units (~100kW)

were deployed on the distribution feeder s . This analysis is particularly interesting because some of the issues caused by the addition of PV can be solved by adding different size d PV units

. For combined PV deployments , there are 5 plots that wi ll be disp layed for each feeder : peak monthly demand, monthly energy consumption, monthly losses, monthly PV output and monthly CO 2 

### emissions. 

# 3.3.1 Example Feeder R5

- 35.00

- 1

Figure 3.11 through Figure 3.15 show the plots that are generated for feeder R5

- 35.00

- 1. Region 5 represents the south east part of the U nited S tates , which has a hot and humid climate. Feeder R5

- 35.00

- 1 is comprised of mostly family residences with a moderate number of commercial end

- use loads

.

Figure 3.11 shows the monthly peak load fo r the R5

- 35 .00

- 1 feeder. It can be seen from the plot that the peak monthly demand decrease s for all the months. It should be noted that the peak load reduction is dependent on the feeder characteristics, climate, PV unit distribution , and load composition. Different feeders with different load compositions, even if they are in the same region, may not experience the same peak reduction for each of the months.

### 73 Figure 3. 11 : Comparison of peak load by month for R5

- 35.00

- 1 

### Figure 3.12 shows the monthly comparison of energy consumption for feeder R5

- 35.00

- 1. As noted in previous s ections , a reduction in the energy consumption is the primary benefit of combined PV installations. The addition of combined PV generation to the feeder reduces the overall energy consumption for every month of the year. As expected, the greatest reduction is once again during the summer months when the maximum solar energy is available.

### 74 Figure 3. 12 : Comparison of energy consum ption by month for R5

- 35.00

- 1 

### As seen in p revious implementations , reduction in losses is another benefit of combined PV deployments . As shown in Figure 3.13 , the largest losses for feeder R5

- 35.00

- 1 come from the distribution transformers. It can be obser ved that the real power losses were reduced for almost all components of the distribution feeder , except for a slight increase on the overhead lines . This slight increase can be attributed to the same reasons described in previous s ections

. Specifically , the commercial PV arrays were of fixed size and not sized for optimal interaction with the local end

- use load level. This increased the poten tial for reverse current flow in the overhead lines, causing the losses to increase. Once a gain , for a specific deployment of PV it would be necessary to appropriately size the units for optimal operation, the distributions of seizes used here are only appropriate for regional level analysis.

### 75 Figure 3. 13 : Comparison of losses by month for R5

- 35.00

- 1

### 76 Figure 3. 14 : Comparison of PV output by month for R5

- 35.00

- 1 

### Figure 3.14 shows the combined monthly PV output , for both residential and commercial units, for feeder R5

- 35.00

- 1. From the figure it can once again be seen that the summer months yield the highest solar output, which directly reflects the high solar availability during spring and summer months

.

Figure 3.15 shows the reduction on carbon dioxide emissions for feeder R5

- 35.00

- 1. Once again, i t can be seen t hat reduction s in energy consumption leads to reduction s in carbon dioxide emissions for all the months.

### 77 Figure 3. 15 : Comparison of CO 2 emissions by month for R5

- 35.00

- 1 

# 3.4 Commercial WTG

Commercial wind generators were only simulated for the GC feeders , one for each of the five climate region s

. The main intent for the analysis was to study the effect of the addition of a single large wind turbine , 1.8 MW, on the distribution feeder. For commercial WTG, there are 5 plots that wi ll be displayed for each feeder : peak monthly demand, monthly energy consumption, monthly losses, monthly PV output, and monthly CO 2 

### emissions. 

# 3.4.1 Example Feeder GC

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- 1_R2 Figure 3.16 through Figure 3.20 sho w the plots that are generated for feeder GC

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- 1_R2 when it is operated with a single large WTG

. Climate region 2 represents the north central and north eastern part of the U nited S tates , which generally has a cold climate , but does have considerable wind availability. A model of a 1 .8 M W WTG was added to the feed er and the impact of its operation was compared to the base case

.

Figure 3.16 show s the effect of adding a commercial WTG on feeder GC

- 12.47

- 1_R2. It can be observe d that the peak load is reduced for nearly all the months

. As with the solar PV cases, the peak wind generation may not necessarily coincide with the peak feeder load . Such a case occurs for the months of November and December, where no significant peak change was observed

. During these months, there was little to no wind during the peak load periods.

### 78 Figure 3. 16 : Comparison of peak load by month for GC

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- 1_R2

Figure 3. 17 : Comparison of energy consumption by month for GC

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- 1_R2

### 79 

### Figure 3.17 shows the reduction in m onthly energy consumption for feeder GC

- 12.47

- 1_R2. S imilar to the P V technologies described in previous sections , the addition of a large WTG considerably reduces energy consumptio n. From Figure 3.19 , it can be seen that the wind production in the months of March and April is very high, which leads to a large reduction in the energy consumption from the perspective of the feeder.

The reduction of losses is another benefit on integra ting a large WTG

. Figure 3.18 shows the monthly losses associated with various components on feeder GC

- 12.47

- 1_R2. There are 4 different loss types: losses in overhead lines ‘OHL’, underground lines ‘UGL’, transformers ‘TFR’, and triplex lines ‘TPL’ for ba se case and commercial WTG “CWTG”

. As seen from Figure 3.18 , the largest reduction in losses occurred on underground cables

. However, nearly all of the components show a sizeable reduction in losses with integration of the wind turbine generator

.

Figure 3. 18 : Comparison of losses by month for GC

- 12.47

- 1_R2

### 80 Figure 3. 19 : Comparison of WTG output by month for GC

- 12.47

- 1_R2 

### The monthly wind output for feeder GC

- 12.47

- 1_R2 is given in Figure 3. 19. In contrast to the output of solar PV, the WTG tends to have higher energy outputs in the winter and shoulder months. Similar to solar PV, this generalization is only appropriate for regionalized values, a specific WTG installation may have a seasonal outpu t that is much different than the regional average.

Similar to PV technologies, reduction in energy consumption leads to reduction in the carbon dioxide emissions , as shown in Figure 3.20

. In general, t he larger the reduction in energy consumption, the larger the reduction in CO 2 

### emitted. The reduction in emissions will be heavily dependent on the generation mix within the region.

### 81 Figure 3. 20 : Comparison of CO 2 by month for GC

- 12.47

- 1_R2

### 82 

# 4 SGIG Impact Metric Values

Specific metric impact values are filled in , by feeder, for the metrics identified in Sections 2.1.1 .1 , 2. 1. 2.1, 2.1.3.1 and 2.2 .1

. The raw metric values, by technology and region , are in Appendix E. Please note that some of the values reported in the metrics table are zero due to very small difference from the base case . These values were rounded off to the next large st value. 

# 4.1 Residential PV Impact Metrics

### 83 Table 4. 1 : Residential PV impact metric s for region 1



### Index ∆ Metric UnitsR1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh -27.25 -11.46 -7.16 -25.27 -8.68
2 Monthly Customer Electricity Usage MWh -19.90 -8.37 -5.23 -18.45 -6.33 Peak Generation kW 69.40 -12.81 -84.65 -134.18 42.60 Nuclear % -0.12 -0.72 -0.54 0.65 -0.19 Solar % 0.00 -0.04 -0.04 0.04 0.00 Bio % -0.01 0.04 0.05 -0.05 -0.01 Wind % -0.05 -0.56 -0.50 0.54 -0.07 Coal % -0.03 1.45 1.52 -1.48 -0.05 Hydroelectric % -0.41 -10.89 -10.44 10.77 -0.67 Natural Gas % -0.46 9.22 10.10 -9.63 -0.75 Geothermal % -0.03 0.23 -1.64 -2.01 -0.05 Petroleum % -0.18 0.34 -0.35 -0.38 -0.01 Distributed Solar PV % 1.30 0.93 1.84 1.55 1.81 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 4 Peak Load MW -13.31 -46.06 -79.09 -106.55 -0.10
7 Annual Electricity Production MWh -243.46 -102.40 -63.54 -226.93 -77.84 12 CO2 Emissions Tons -62.10 -27.43 -21.48 -68.92 -26.49 SOx Emissions Tons 0.00 0.00 0.00 0.00 0.00 NOx Emissions Tons 0.00 0.00 0.00 -0.01 0.00 PM-10 Emissions Tons -0.01 0.00 0.00 -0.01 0.00 Feeder Real Load MW -27.79 -11.69 -7.25 -25.91 -8.89 Feeder Reactive Load MVAR -0.94 -0.22 -0.04 -0.34 -0.24 29 Distribution Losses % 0.01 0.01 -0.01 -0.02 -0.01 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -63.57 -28.18 -21.64 -70.05 -26.94 SOx Tons 0.00 0.00 0.00 0.00 0.00 NOx Tons 0.00 0.00 0.00 -0.01 0.00 PM-10 Tons -0.01 0.00 0.00 -0.01 0.00 3

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### 84 Table 4. 2 : Residential PV impact metrics for region 2



### Index ∆ Metric UnitsR2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh -8.97 -8.49 -3.90 -21.06 -16.02
2 Monthly Customer Electricity Usage MWh -6.54 -6.20 -2.85 -15.38 -11.69 Peak Generation kW -192.59 191.26 -90.30 30.84 -293.43 Nuclear % 0.34 -0.49 -1.54 -0.09 1.62 Solar % 0.00 0.00 0.00 0.00 0.00 Bio % 0.01 -0.02 -0.02 0.00 0.02 Wind % 0.02 -0.03 -0.29 0.00 0.29 Coal % 0.60 -0.88 1.79 -0.16 -1.64 Hydroelectric % -1.24 -0.14 -1.61 -0.03 1.52 Natural Gas % 0.21 -0.30 1.91 -0.06 -1.86 Geothermal % -0.07 0.00 -0.07 0.00 -0.07 Petroleum % -0.43 1.36 -0.37 -0.16 -0.43 Distributed Solar PV % 0.56 0.50 0.18 0.50 0.54 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 Peak Load MW -114.54 80.27 -63.80 -26.60 -292.30 Annual Electricity Production MWh -79.13 -75.59 -34.80 -186.55 -141.80 12 CO2 Emissions Tons -58.98 -70.27 -32.39 -156.74 -96.78 SOx Emissions Tons -0.02 -0.03 -0.01 -0.07 -0.04 NOx Emissions Tons -0.02 -0.02 -0.01 -0.04 -0.02 PM-10 Emissions Tons -0.01 -0.01 0.00 -0.02 -0.01 Feeder Real Load MW -9.03 -8.63 -3.97 -21.30 -16.19 Feeder Reactive Load MVAR -0.10 -0.27 -0.07 -0.21 -0.33 29 Distribution Losses % 0.00 0.00 0.00 0.00 0.00 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -59.55 -71.50 -33.06 -158.37 -97.72 SOx Tons -0.02 -0.03 -0.01 -0.07 -0.04 NOx Tons -0.02 -0.02 -0.01 -0.04 -0.02 PM-10 Tons -0.01 -0.01 0.00 -0.02 -0.01 3

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### 85 Table 4. 3 : Residential PV impact metrics for region 3



### Index ∆ Metric UnitsR3-12.47-1R3-12.47-3

1 Hourly Customer Electricity Usage kWh -64.88 -48.69
2 Monthly Customer Electricity Usage MWh -47.36 -35.55 Peak Generation kW -330.54 -78.90 Nuclear % 0.06 -0.05 Solar % 0.00 0.00 Bio % 0.00 0.00 Wind % 0.02 -0.01 Coal % 0.26 -0.20 Hydroelectric % -1.25 -0.03 Natural Gas % 0.24 -0.18 Geothermal % -1.40 -1.16 Petroleum % -0.25 -0.25 Distributed Solar PV % 2.32 1.87 Distributed Wind % 0.00 0.00 Peak Load MW -273.28 -118.28 Annual Electricity Production MWh -577.14 -434.71 12 CO2 Emissions Tons -349.23 -276.83 SOx Emissions Tons -0.08 -0.07 NOx Emissions Tons -0.06 -0.05 PM-10 Emissions Tons -0.05 -0.04 Feeder Real Load MW -65.88 -49.62 Feeder Reactive Load MVAR -1.49 -0.91 29 Distribution Losses % 0.01 0.02 30 Distribution Power Factor pf 0.00 0.00 39 CO2 Emissions Tons -354.37 -281.26 SOx Tons -0.08 -0.07 NOx Tons -0.06 -0.05 PM-10 Tons -0.05 -0.04 3

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### 86 Table 4. 4 : Residential PV impact metrics for region 4



### Index ∆ Metric UnitsR4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh -37.94 -15.05 -7.72
2 Monthly Customer Electricity Usage MWh -27.70 -10.99 -5.64 Peak Generation kW -190.61 -29.47 -53.15 Nuclear % 0.08 -1.96 0.38 Solar % 0.00 0.00 0.00 Bio % 0.00 -0.03 0.00 Wind % 0.00 0.00 0.01 Coal % 0.20 0.32 0.90 Hydroelectric % -2.20 -2.16 -3.09 Natural Gas % -0.90 1.12 -0.43 Geothermal % 0.00 0.00 0.00 Petroleum % -0.48 -0.33 -0.33 Distributed Solar PV % 3.30 3.03 2.56 Distributed Wind % 0.00 0.00 0.00 Peak Load MW -174.48 -58.26 -38.77 Annual Electricity Production MWh -335.59 -132.65 -68.08 12 CO2 Emissions Tons -323.05 -133.23 -68.59 SOx Emissions Tons -0.15 -0.06 -0.03 NOx Emissions Tons -0.09 -0.04 -0.02 PM-10 Emissions Tons -0.05 -0.02 -0.01 Feeder Real Load MW -38.31 -15.14 -7.77 Feeder Reactive Load MVAR -0.83 -0.06 -0.07 29 Distribution Losses % 0.04 0.03 0.04 30 Distribution Power Factor pf 0.00 0.00 0.00 39 CO2 Emissions Tons -328.60 -135.14 -69.68 SOx Tons -0.15 -0.06 -0.03 NOx Tons -0.09 -0.04 -0.02 PM-10 Tons -0.05 -0.02 -0.01 3

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### 87 Table 4. 5 : Residential PV impact metrics for region 5



### Index ∆ Metric UnitsR5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh -82.07 -49.25 -94.05 -67.10 -82.29 -120.83 -121.47
2 Monthly Customer Electricity Usage MWh -59.91 -35.95 -68.65 -48.98 -60.07 -88.21 -88.67 Peak Generation kW -226.85 -13.05 117.64 -298.25 -153.53 -490.04 -579.69 Nuclear % -0.03 -0.29 -0.26 0.08 0.19 0.03 0.11 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.00 -0.01 -0.01 0.00 0.02 0.00 0.00 Wind % 0.00 -0.03 -0.03 0.01 -0.27 0.00 0.01 Coal % -0.16 -0.62 -0.58 -0.77 -0.48 -1.20 -1.21 Hydroelectric % -0.63 -0.46 -0.01 -0.63 -0.59 -0.63 -0.63 Natural Gas % -0.10 -1.07 -0.97 0.29 -0.08 0.12 0.40 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % -1.86 -1.86 -0.81 -1.86 -1.98 -1.86 -1.86 Distributed Solar PV % 2.78 4.34 2.67 2.88 3.20 3.53 3.17 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW -244.59 -115.87 -81.50 -257.53 -234.78 -461.70 -487.40 Annual Electricity Production MWh -728.46 -447.21 -874.03 -598.71 -741.49 -1073.93 -1075.49 12 CO2 Emissions Tons -669.78 -394.62 -720.25 -537.56 -638.27 -931.17 -949.63 SOx Emissions Tons -0.29 -0.17 -0.29 -0.23 -0.26 -0.38 -0.39 NOx Emissions Tons -0.18 -0.10 -0.18 -0.14 -0.16 -0.24 -0.24 PM-10 Emissions Tons -0.10 -0.06 -0.11 -0.08 -0.09 -0.14 -0.14 Feeder Real Load MW -83.16 -51.05 -99.78 -68.35 -84.65 -122.59 -122.77 Feeder Reactive Load MVAR -1.85 -1.52 -15.69 -1.15 -5.46 -1.53 -0.52 29 Distribution Losses % 0.00 -0.03 -0.03 0.00 -0.01 0.00 0.00 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -678.17 -405.27 -757.86 -547.87 -654.80 -944.50 -961.10 SOx Tons -0.29 -0.17 -0.30 -0.23 -0.27 -0.38 -0.40 NOx Tons -0.18 -0.10 -0.19 -0.14 -0.17 -0.24 -0.25 PM-10 Tons -0.10 -0.06 -0.11 -0.08 -0.10 -0.14 -0.14 3

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### 88 

# 4.2 Commercial PV Impact Metrics

Table 4. 6 : Commercial PV impact metrics for region 1



### Index ∆ Metric UnitsGC-12.47-1 R1R1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh -15.69 -32.78 -7.97 -3.13 -7.87 -6.21
2 Monthly Customer Electricity Usage MWh -11.45 -23.93 -5.82 -2.28 -5.75 -4.53 Peak Generation kW -97.30 -71.87 -54.49 -56.22 -23.29 -3.93 Nuclear % 0.03 -0.04 0.05 0.18 0.00 -0.06 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.00 0.00 0.00 0.01 0.00 0.00 Wind % 0.01 -0.01 0.02 0.07 0.00 -0.02 Coal % 0.01 -0.01 0.01 0.05 0.00 -0.02 Hydroelectric % 0.12 -0.14 0.18 0.61 0.01 -0.22 Natural Gas % 0.13 -0.15 0.20 0.69 0.01 -0.24 Geothermal % -1.16 -1.00 -1.20 -2.52 -0.05 -0.42 Petroleum % -0.35 -0.35 -0.35 -0.35 -0.38 -0.35 Distributed Solar PV % 1.20 1.71 1.07 1.26 0.41 1.33 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 4 Peak Load MW -80.46 -98.60 -41.57 -36.23 -21.89 -17.43
7 Annual Electricity Production MWh -137.66 -293.45 -70.10 -27.68 -69.75 -56.30 12 CO2 Emissions Tons -46.19 -74.89 -19.09 -9.66 -21.92 -19.28 SOx Emissions Tons 0.00 0.00 0.00 0.00 0.00 0.00 NOx Emissions Tons 0.00 -0.01 0.00 0.00 0.00 0.00 PM-10 Emissions Tons -0.01 -0.01 0.00 0.00 0.00 0.00 Feeder Real Load MW -15.71 -33.50 -8.00 -3.16 -7.96 -6.43 Feeder Reactive Load MVAR 0.01 -1.78 -0.16 -0.08 -0.25 -0.33 29 Distribution Losses % 0.00 0.00 0.02 0.00 0.00 -0.01 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -46.28 -76.73 -19.51 -9.72 -22.18 -19.67 SOx Tons 0.00 0.00 0.00 0.00 0.00 0.00 NOx Tons 0.00 -0.01 0.00 0.00 0.00 0.00 PM-10 Tons -0.01 -0.01 0.00 0.00 0.00 0.00 3

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### 89 Table 4. 7 : Commercial PV impact metrics for region 2



### Index ∆ Metric UnitsGC-12.47-1 R2R2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh -13.36 -5.18 -1.52 0.00 -16.78 -5.16
2 Monthly Customer Electricity Usage MWh -9.75 -3.78 -1.11 0.00 -12.25 -3.77 Peak Generation kW 131.17 -55.06 103.03 0.00 351.80 23.01 Nuclear % -0.45 0.07 -0.24 0.00 1.19 1.32 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 Bio % -0.01 0.00 -0.01 0.00 0.01 0.01 Wind % -0.02 0.00 -0.01 0.00 0.26 0.27 Coal % -0.80 0.13 -0.44 0.00 -2.34 -2.12 Hydroelectric % -0.13 -0.07 -0.07 0.00 1.49 1.53 Natural Gas % -0.28 0.05 -0.15 0.00 -2.08 -2.01 Geothermal % 0.00 -0.07 0.00 0.00 0.00 0.00 Petroleum % 0.53 -0.43 0.83 0.00 1.09 0.83 Distributed Solar PV % 1.16 0.31 0.09 0.00 0.37 0.17 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 31.50 -37.44 48.79 0.00 88.80 -111.80 Annual Electricity Production MWh -117.27 -46.33 -13.44 0.00 -147.67 -45.65 12 CO2 Emissions Tons -89.02 -33.76 -12.61 0.00 -125.72 -30.81 SOx Emissions Tons -0.04 -0.01 -0.01 0.00 -0.05 -0.01 NOx Emissions Tons -0.02 -0.01 0.00 0.00 -0.03 -0.01 PM-10 Emissions Tons -0.01 -0.01 0.00 0.00 -0.02 0.00 Feeder Real Load MW -13.39 -5.29 -1.53 0.00 -16.86 -5.21 Feeder Reactive Load MVAR -0.11 -0.15 -0.05 0.00 -0.14 -0.14 29 Distribution Losses % 0.00 0.00 0.00 0.00 0.00 0.00 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -89.22 -34.39 -12.81 0.00 -126.57 -31.08 SOx Tons -0.04 -0.01 -0.01 0.00 -0.05 -0.01 NOx Tons -0.02 -0.01 0.00 0.00 -0.03 -0.01 PM-10 Tons -0.01 -0.01 0.00 0.00 -0.02 0.00 3

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### 90 Table 4. 8 : Commercial PV impact metrics for region 3



### Index ∆ Metric UnitsGC-12.47-1 R3R3-12.47-1R3-12.47-2R3-12.47-3

1 Hourly Customer Electricity Usage kWh -19.70 -81.04 -13.19 -111.50
2 Monthly Customer Electricity Usage MWh -14.38 -59.16 -9.63 -81.40 Peak Generation kW 106.42 -530.48 -221.83 3.71 Nuclear % 0.93 0.13 0.21 -1.46 Solar % 0.00 0.00 0.00 0.01 Bio % 0.02 0.00 0.01 -0.05 Wind % 0.37 0.03 0.05 -0.31 Coal % 0.70 0.55 0.88 -1.26 Hydroelectric % 0.73 -2.76 -1.25 -1.94 Natural Gas % -4.32 0.50 0.80 2.43 Geothermal % 0.13 -1.40 -1.40 -0.59 Petroleum % 0.32 -0.25 -0.25 -0.25 Distributed Solar PV % 1.12 3.20 0.96 3.42 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 12.73 -415.09 -133.12 -233.04 Annual Electricity Production MWh -173.06 -718.89 -116.82 -988.69 12 CO2 Emissions Tons -107.63 -437.70 -67.95 -653.55 SOx Emissions Tons -0.02 -0.10 -0.01 -0.18 NOx Emissions Tons -0.02 -0.07 -0.01 -0.12 PM-10 Emissions Tons -0.02 -0.06 -0.01 -0.10 Feeder Real Load MW -19.76 -82.07 -13.34 -112.86 Feeder Reactive Load MVAR -0.21 -2.11 -0.25 -2.18 29 Distribution Losses % 0.00 0.01 0.00 0.07 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -107.93 -442.73 -68.75 -659.13 SOx Tons -0.03 -0.10 -0.01 -0.17 NOx Tons -0.02 -0.07 -0.01 -0.12 PM-10 Tons -0.02 -0.07 -0.01 -0.10 3

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### 91 Table 4. 9 : Commercial PV impact metrics for region 4



### Index ∆ Metric UnitsGC-12.47-1 R4R4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh -29.44 -44.35 -19.45 -2.83
2 Monthly Customer Electricity Usage MWh -21.49 -32.38 -14.20 -2.07 Peak Generation kW -153.90 -267.77 -141.70 -2.10 Nuclear % 0.07 0.19 0.25 -0.14 Solar % 0.00 0.00 0.00 0.00 Bio % 0.00 0.00 0.00 0.00 Wind % 0.00 0.01 0.01 0.00 Coal % 0.18 0.49 0.59 -0.34 Hydroelectric % -1.68 -2.20 -3.09 -0.52 Natural Gas % 0.06 -1.95 -1.88 -0.10 Geothermal % 0.00 0.00 0.00 0.00 Petroleum % -0.48 -0.48 -0.33 -0.33 Distributed Solar PV % 1.85 3.95 4.46 1.44 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW -134.40 -228.74 -120.12 -7.88 Annual Electricity Production MWh -258.55 -390.60 -171.04 -24.82 12 CO2 Emissions Tons -231.25 -377.28 -171.87 -24.91 SOx Emissions Tons -0.10 -0.17 -0.08 -0.01 NOx Emissions Tons -0.06 -0.11 -0.05 -0.01 PM-10 Emissions Tons -0.03 -0.06 -0.03 0.00 Feeder Real Load MW -29.52 -44.59 -19.52 -2.83 Feeder Reactive Load MVAR -0.16 -0.62 -0.23 -0.01 29 Distribution Losses % 0.00 0.05 0.04 0.02 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -231.88 -382.80 -174.15 -25.23 SOx Tons -0.10 -0.18 -0.08 -0.01 NOx Tons -0.06 -0.11 -0.05 -0.01 PM-10 Tons -0.03 -0.06 -0.03 0.00 3

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### 92 Table 4. 10 : Commercial PV impact metrics for region 5



### Index ∆ Metric UnitsGC-12.47-1 R5R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh -48.80 -89.58 -45.53 -92.84 -45.96 -78.57 -111.13 -157.45
2 Monthly Customer Electricity Usage MWh -35.62 -65.39 -33.24 -67.77 -33.55 -57.35 -81.13 -114.94 Peak Generation kW 134.15 -340.84 -41.08 -403.25 -73.90 -191.53 -303.04 -518.88 Nuclear % -0.80 0.04 -0.13 0.08 -0.10 0.28 -0.47 -0.38 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % -0.03 0.00 0.00 0.00 0.00 0.02 -0.02 -0.02 Wind % 0.20 0.00 -0.01 0.01 -0.01 -0.26 0.24 0.25 Coal % -0.87 -0.77 -0.29 -0.37 -0.22 -0.30 -0.13 -0.60 Hydroelectric % 0.12 -0.63 -0.01 -0.78 0.00 -0.42 0.14 -0.63 Natural Gas % -2.20 0.14 -0.49 0.31 -0.38 0.23 -0.95 -0.62 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % 1.11 -1.86 -1.77 -1.98 -1.70 -1.98 -1.55 -1.86 Distributed Solar PV % 2.48 3.07 2.71 2.73 2.41 2.44 2.74 3.86 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW -76.94 -315.69 -88.32 -343.30 -128.04 -219.44 -433.20 -572.60 Annual Electricity Production MWh -428.87 -794.90 -408.92 -858.97 -410.59 -702.38 -979.49 -1382.29 12 CO2 Emissions Tons -363.79 -730.36 -362.97 -710.83 -369.33 -607.43 -852.46 -1215.18 SOx Emissions Tons -0.14 -0.31 -0.15 -0.28 -0.16 -0.25 -0.34 -0.49 NOx Emissions Tons -0.09 -0.19 -0.09 -0.18 -0.10 -0.15 -0.21 -0.31 PM-10 Emissions Tons -0.05 -0.11 -0.05 -0.11 -0.05 -0.09 -0.13 -0.18 Feeder Real Load MW -48.96 -90.74 -46.68 -98.06 -46.87 -80.18 -111.81 -157.80 Feeder Reactive Load MVAR -0.42 -4.23 -0.95 -17.25 -1.31 -5.36 -1.02 -0.91 29 Distribution Losses % 0.00 0.00 -0.01 -0.02 0.00 0.01 0.02 0.03 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -364.99 -739.43 -371.12 -746.56 -376.60 -621.10 -861.66 -1225.53 SOx Tons -0.14 -0.32 -0.16 -0.30 -0.16 -0.25 -0.35 -0.50 NOx Tons -0.09 -0.20 -0.10 -0.19 -0.10 -0.16 -0.22 -0.31 PM-10 Tons -0.05 -0.11 -0.06 -0.11 -0.06 -0.09 -0.13 -0.18 3

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### 93 

# 4.3 Combined PV Impact Metrics

Table 4. 11 : Combined PV impact metrics for region 1



### Index ∆ Metric UnitsR1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh -59.78 -19.61 -7.85 -30.72 -14.11
2 Monthly Customer Electricity Usage MWh -43.64 -14.31 -5.73 -22.42 -10.30 Peak Generation kW -221.32 -22.70 -79.12 -160.97 105.04 Nuclear % 0.00 -0.74 -0.53 0.66 -0.35 Solar % 0.00 -0.04 -0.04 0.04 -0.01 Bio % 0.00 0.04 0.05 -0.05 -0.02 Wind % 0.00 -0.57 -0.50 0.55 -0.14 Coal % 0.00 1.44 1.53 -1.48 -0.10 Hydroelectric % 0.01 -10.95 -10.40 10.79 -1.22 Natural Gas % 0.01 9.10 10.17 -9.60 -1.37 Geothermal % -2.65 0.22 -1.05 -2.49 -0.09 Petroleum % -0.35 -0.25 -0.35 -0.38 1.01 Distributed Solar PV % 2.98 1.75 1.12 1.97 2.30 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 4 Peak Load MW -219.96 -61.80 -71.98 -130.71 24.59
7 Annual Electricity Production MWh -534.74 -174.29 -69.57 -274.80 -127.53 12 CO2 Emissions Tons -134.04 -45.82 -23.33 -83.05 -42.73 SOx Emissions Tons 0.00 0.00 0.00 0.00 0.00 NOx Emissions Tons -0.01 0.00 0.00 -0.01 0.00 PM-10 Emissions Tons -0.02 -0.01 0.00 -0.01 -0.01 Feeder Real Load MW -61.04 -19.90 -7.94 -31.37 -14.56 Feeder Reactive Load MVAR -2.81 -0.37 -0.13 -0.56 -0.64 29 Distribution Losses % 0.01 0.03 -0.01 -0.01 -0.03 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -137.29 -47.01 -23.50 -84.31 -43.55 SOx Tons 0.00 0.00 0.00 0.00 0.00 NOx Tons -0.01 0.00 0.00 -0.01 0.00 PM-10 Tons -0.02 -0.01 0.00 -0.01 -0.01 3

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### 94 Table 4. 12 : Combined PV impact metrics for region 2



### Index ∆ Metric UnitsR2-12.47-1R2-12.47-2R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh -14.77 -10.21 -39.38 -18.59
2 Monthly Customer Electricity Usage MWh -10.78 -7.45 -28.74 -13.57 Peak Generation kW 2.79 24.42 -73.88 -461.00 Nuclear % -0.11 -0.13 -0.10 0.41 Solar % 0.00 0.00 0.00 0.00 Bio % 0.00 0.00 0.00 0.01 Wind % -0.01 -0.01 -0.01 0.02 Coal % -0.20 -0.22 -0.18 0.73 Hydroelectric % -0.03 -0.04 -0.34 -1.55 Natural Gas % -0.07 -0.08 -0.06 0.25 Geothermal % 0.00 0.00 -0.07 -0.07 Petroleum % -0.37 -0.06 -0.43 -0.43 Distributed Solar PV % 0.79 0.53 1.19 0.62 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW -23.45 -3.17 -137.50 -271.20 Annual Electricity Production MWh -131.17 -90.59 -347.78 -164.36 12 CO2 Emissions Tons -97.23 -84.84 -293.58 -112.75 SOx Emissions Tons -0.04 -0.04 -0.12 -0.04 NOx Emissions Tons -0.02 -0.02 -0.08 -0.03 PM-10 Emissions Tons -0.01 -0.01 -0.04 -0.02 Feeder Real Load MW -14.97 -10.34 -39.70 -18.76 Feeder Reactive Load MVAR -0.29 -0.32 -0.48 -0.41 29 Distribution Losses % 0.00 0.00 0.00 0.00 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -98.53 -86.21 -296.18 -113.76 SOx Tons -0.04 -0.04 -0.12 -0.05 NOx Tons -0.03 -0.02 -0.08 -0.03 PM-10 Tons -0.01 -0.01 -0.04 -0.02 3

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### 95 Table 4. 13 : Combined PV impact metrics for region 3



### Index ∆ Metric UnitsR3-12.47-1R3-12.47-3

1 Hourly Customer Electricity Usage kWh -138.01 -158.08
2 Monthly Customer Electricity Usage MWh -100.75 -115.40 Peak Generation kW -539.80 -336.83 Nuclear % 0.02 0.04 Solar % 0.00 0.00 Bio % 0.00 0.00 Wind % 0.00 0.01 Coal % 0.07 0.19 Hydroelectric % -3.98 -1.89 Natural Gas % 0.06 0.17 Geothermal % -1.40 -1.40 Petroleum % -0.25 -0.25 Distributed Solar PV % 5.48 3.12 Distributed Wind % 0.00 0.00 Peak Load MW -525.10 -299.75 Annual Electricity Production MWh ###### -1404.73 12 CO2 Emissions Tons -762.02 -944.94 SOx Emissions Tons -0.18 -0.26 NOx Emissions Tons -0.13 -0.18 PM-10 Emissions Tons -0.11 -0.14 Feeder Real Load MW -139.92 -160.36 Feeder Reactive Load MVAR -3.47 -3.20 29 Distribution Losses % 0.02 0.09 30 Distribution Power Factor pf 0.00 0.00 39 CO2 Emissions Tons -771.92 -955.78 SOx Tons -0.18 -0.26 NOx Tons -0.13 -0.18 PM-10 Tons -0.11 -0.14 3

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### 96 Table 4. 14 : Combined PV impact metrics for region 4



### Index ∆ Metric UnitsR4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh -79.80 -34.14 -10.24
2 Monthly Customer Electricity Usage MWh -58.26 -24.92 -7.48 Peak Generation kW -310.17 -55.58 -84.82 Nuclear % 0.19 -2.19 0.54 Solar % 0.00 0.00 0.00 Bio % 0.00 -0.03 0.00 Wind % 0.01 0.00 0.01 Coal % 0.51 -0.28 1.27 Hydroelectric % -2.20 -3.09 -3.09 Natural Gas % -2.82 -0.32 -3.20 Geothermal % 0.00 0.00 0.00 Petroleum % -0.48 -0.33 -0.33 Distributed Solar PV % 4.80 6.25 4.79 Distributed Wind % 0.00 0.00 0.00 Peak Load MW -270.30 -106.90 -65.26 Annual Electricity Production MWh -703.85 -300.53 -90.21 12 CO2 Emissions Tons -682.01 -302.74 -90.79 SOx Emissions Tons -0.32 -0.14 -0.04 NOx Emissions Tons -0.19 -0.09 -0.03 PM-10 Emissions Tons -0.10 -0.05 -0.01 Feeder Real Load MW -80.35 -34.31 -10.30 Feeder Reactive Load MVAR -1.26 -0.31 -0.04 29 Distribution Losses % 0.09 0.08 0.06 30 Distribution Power Factor pf 0.00 0.00 0.00 39 CO2 Emissions Tons -692.74 -306.95 -92.21 SOx Tons -0.32 -0.14 -0.04 NOx Tons -0.19 -0.09 -0.03 PM-10 Tons -0.10 -0.05 -0.01 3

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### 97 Table 4. 15 : Combined PV impact metrics for region 5



### Index ∆ Metric UnitsR5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh -177.30 -92.45 -188.26 -115.63 -164.39 -241.18 -279.22
2 Monthly Customer Electricity Usage MWh -129.43 -67.49 -137.43 -84.41 -120.01 -176.06 -203.83 Peak Generation kW -553.68 21.89 -597.72 -267.35 -554.36 -727.07 -947.98 Nuclear % -0.03 -0.85 -0.01 -0.22 0.03 -0.44 0.01 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.00 -0.03 0.00 -0.01 0.00 -0.02 0.00 Wind % 0.00 0.19 0.00 -0.02 0.00 0.24 0.00 Coal % -3.59 -1.00 -3.08 -3.00 -3.13 -2.28 -5.05 Hydroelectric % -0.63 0.12 -0.78 -0.63 -0.78 -0.63 -0.63 Natural Gas % -0.09 -2.42 -0.04 -0.81 0.10 -0.86 0.04 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % -1.86 0.83 -1.98 -1.86 -1.98 -1.86 -1.86 Distributed Solar PV % 6.20 3.16 5.89 6.54 5.76 5.85 7.49 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW -570.03 -176.12 -604.45 -380.91 -537.35 -832.50 -939.50 Annual Electricity Production MWh -1573.19 -834.38 -1746.37 -1032.64 -1475.06 -2134.64 -2460.55 12 CO2 Emissions Tons -1427.24 -727.87 -1417.15 -918.02 -1252.90 -1821.20 -2121.42 SOx Emissions Tons -0.60 -0.30 -0.55 -0.38 -0.50 -0.72 -0.84 NOx Emissions Tons -0.37 -0.19 -0.35 -0.24 -0.31 -0.45 -0.53 PM-10 Emissions Tons -0.21 -0.11 -0.21 -0.14 -0.19 -0.27 -0.32 Feeder Real Load MW -179.59 -95.25 -199.36 -117.88 -168.39 -243.68 -280.89 Feeder Reactive Load MVAR -6.46 -2.44 -33.44 -2.49 -10.93 -2.40 -1.51 29 Distribution Losses % 0.00 -0.04 -0.05 0.00 0.00 0.02 0.04 30 Distribution Power Factor pf 0.00 0.00 0.00 0.00 0.00 0.00 0.00 39 CO2 Emissions Tons -1444.90 -745.66 -1489.91 -935.96 -1283.07 -1844.13 -2142.90 SOx Tons -0.61 -0.31 -0.58 -0.39 -0.51 -0.73 -0.86 NOx Tons -0.38 -0.19 -0.37 -0.24 -0.32 -0.46 -0.54 PM-10 Tons -0.22 -0.11 -0.22 -0.14 -0.19 -0.27 -0.32 3

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### 98 

# 4.4 Commercial WTG Impact Metrics
Table 4. 16 : Commercial WTG impact metrics for region 1



### Index ∆ Metric UnitsGC-12.47-1 R1

1 Hourly Customer Electricity Usage kWh -466.03
2 Monthly Customer Electricity Usage MWh -340.20 Peak Generation kW -54.69 Nuclear % -0.25 Solar % -0.01 Bio % -0.02 Wind % -0.09 Coal % -0.20 Hydroelectric % -0.86 Natural Gas % -0.96 Geothermal % -2.84 Petroleum % -0.35 Distributed Solar PV % 0.00 Distributed Wind % 5.57 4 Peak Load MW -176.70
7 Annual Electricity Production MWh ###### 12 CO2 Emissions Tons -713.43 SOx Emissions Tons -0.02 NOx Emissions Tons -0.06 PM-10 Emissions Tons -0.10 Feeder Real Load MW -466.97 Feeder Reactive Load MVAR -194.67 29 Distribution Losses % 0.01 30 Distribution Power Factor pf -0.05 39 CO2 Emissions Tons -714.98 SOx Tons -0.02 NOx Tons -0.06 PM-10 Tons -0.10 3

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### 99 Table 4. 17 : Commercial WTG impact metrics for region 2



### Index ∆ Metric UnitsGC-12.47-1 R2

1 Hourly Customer Electricity Usage kWh -450.89
2 Monthly Customer Electricity Usage MWh -329.15 Peak Generation kW -288.86 Nuclear % 0.28 Solar % 0.00 Bio % 0.01 Wind % 0.01 Coal % 0.49 Hydroelectric % -3.49 Natural Gas % 0.17 Geothermal % -0.07 Petroleum % -0.43 Distributed Solar PV % 0.00 Distributed Wind % 3.03 Peak Load MW -231.76 Annual Electricity Production MWh -3956.88 12 CO2 Emissions Tons -2480.11 SOx Emissions Tons -1.09 NOx Emissions Tons -0.69 PM-10 Emissions Tons -0.37 Feeder Real Load MW -451.70 Feeder Reactive Load MVAR -188.16 29 Distribution Losses % 0.02 30 Distribution Power Factor pf -0.07 39 CO2 Emissions Tons -2485.20 SOx Tons -1.09 NOx Tons -0.70 PM-10 Tons -0.37 3

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### 100 Table 4. 18 : Commercial WTG impact metrics for region 3



### Index ∆ Metric UnitsGC-12.47-1 R3

1 Hourly Customer Electricity Usage kWh -124.55
2 Monthly Customer Electricity Usage MWh -90.92 Peak Generation kW -74.11 Nuclear % 1.11 Solar % 0.00 Bio % 0.02 Wind % 0.41 Coal % 1.47 Hydroelectric % 0.85 Natural Gas % -3.62 Geothermal % -0.18 Petroleum % -0.20 Distributed Solar PV % 0.00 Distributed Wind % 0.12 Peak Load MW -44.05 Annual Electricity Production MWh -1094.41 12 CO2 Emissions Tons -844.50 SOx Emissions Tons -0.30 NOx Emissions Tons -0.19 PM-10 Emissions Tons -0.13 Feeder Real Load MW -124.93 Feeder Reactive Load MVAR -52.15 29 Distribution Losses % 0.00 30 Distribution Power Factor pf 0.00 39 CO2 Emissions Tons -847.11 SOx Tons -0.30 NOx Tons -0.20 PM-10 Tons -0.13 3

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### 101 Table 4. 19 : Commercial WTG impact metrics for region 4



### Index ∆ Metric UnitsGC-12.47-1 R4

1 Hourly Customer Electricity Usage kWh -273.75
2 Monthly Customer Electricity Usage MWh -199.84 Peak Generation kW 343.53 Nuclear % 0.52 Solar % 0.00 Bio % 0.02 Wind % -0.04 Coal % -3.81 Hydroelectric % 0.74 Natural Gas % -2.14 Geothermal % 0.00 Petroleum % 1.40 Distributed Solar PV % 0.00 Distributed Wind % 3.31 Peak Load MW 23.48 Annual Electricity Production MWh -2403.22 12 CO2 Emissions Tons -1793.06 SOx Emissions Tons -0.83 NOx Emissions Tons -0.51 PM-10 Emissions Tons -0.27 Feeder Real Load MW -274.34 Feeder Reactive Load MVAR -114.40 29 Distribution Losses % 0.01 30 Distribution Power Factor pf -0.03 39 CO2 Emissions Tons -1797.38 SOx Tons -0.84 NOx Tons -0.51 PM-10 Tons -0.27 3

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### 102 Table 4. 20 : Commercial WTG metrics for region 5



### Index ∆ Metric UnitsGC-12.47-1 R5

1 Hourly Customer Electricity Usage kWh -402.21
2 Monthly Customer Electricity Usage MWh -293.62 Peak Generation kW -57.26 Nuclear % -0.31 Solar % 0.00 Bio % -0.01 Wind % -0.03 Coal % -1.34 Hydroelectric % -0.63 Natural Gas % -1.15 Geothermal % 0.00 Petroleum % -1.86 Distributed Solar PV % 0.00 Distributed Wind % 5.33 Peak Load MW -185.58 Annual Electricity Production MWh -3533.80 12 CO2 Emissions Tons -2342.49 SOx Emissions Tons -0.74 NOx Emissions Tons -0.50 PM-10 Emissions Tons -0.35 Feeder Real Load MW -403.40 Feeder Reactive Load MVAR -168.44 29 Distribution Losses % 0.01 30 Distribution Power Factor pf -0.02 39 CO2 Emissions Tons -2349.80 SOx Tons -0.74 NOx Tons -0.50 PM-10 Tons -0.35 3

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### 103 

# 5 

# Concl usion and Observations

The SGIG proposals demonstrated considerable interest in the area of distributed generation , specifically in the areas of solar photovoltaic and wind turbine generators

. Additionally, there was considerable interest in developing interfa ce capabilities for the future addition of renewable distributed resources. Only one of the SGIG proposals specifically mentioned implementing 100kW PV’s in their system. However, there were not enough details for residential PV system size or the WTG in stallations to extrapolate a unit size. In order to assess the impact of DG on all the prototypical feeders, assumptions were made for residential PV (3 kW

– 5kW) and WTG (1.8 MW) ratings

. 

# 5.1 Distributed Generation Observations and Conclusions During the simulation of distributed generation across the different climate regions, many different metrics were recorded and examined. Some of the metrics are the primary drivers for the installation of renewable distributed generation , and some of the metrics were secondary benefits. This section will provide some overall observations and conclusions, including some assumptions, from this analysis. A brief summary of this section will be presented in Section 5 .2.

There are several rea sons why utilities may choose to deploy or integrate customer owned distributed generation at the distribution level . A customer may choose to install PV on his or her rooftop to reduce energy consumption and to obtain benefit s from net metering. The utili ties can deploy DGs to offset peak load so that they don’t have to buy power from the market , which can be volatile. Additionally, they may able to accommodate additio nal load growth with no transmission investment. For the purposes of these simulations, a low, 1% to 6% of feeder peak load w as used as the PV populat ion criterion. A single 1.8 MW WTG was used for commercial feeders. The PV and WTG units integrated to the distribution feeders were operated in accordance with the IEEE 1547 standards of dist ributed generation interconnection. The PV and WTG units were grid connected and operated at unity power factor (i.e. , they only supplied real power to the grid) with no active voltage control

.

From the simulations , it was clear that reductions in annual e nergy consumption were always achieved on the prototypical feeders for both PV and WTG integration ; the reduction in annual energy consumption being the primary benefit of these technologies

. One of the other important observations made was the effect of P V integration on the peak demand of the feeder. In most of the feeders, it was observed that peak feeder load was reduced when PVs were integrated onto the distribution feeder

. H owever , in some feeders , there was an increase in the peak load . The amount of the peak load increase was less than 0.5 % when compared to the base case. This is primari ly attributed to the random distribution of the PV units in this analysis , which potentially generates clusters of PV units. These clustered units have the effect of raising the local voltages

### 104 

### because of their injection of real power without a corresponding voltage regulation capability; the lack of voltage regulation is in accordance with IEEE 1547. The local rise in voltage interacts with the voltage dependent end

- u se loads and can impact the peak load.

There were other secondary metrics which showed benefits as a result of DG deployment. System losses were mostly seen to decrease , with the effect varying due to different feeder composition s and various end

- use load types . I ncreasing th e penetration of PVs on the feeder was not the only factor affecting these metrics. For example, losses can be considerably lower for feeders with balanced loads, new conductors , properly sized distribution transformers , and correct pla cement of PVs. Throughout the nation , there are old er feeders , which have not been upgraded , and can have improper equipment sizing and old conductors . Such feeder configurations can worsen the system losses due to improper placement as the penetration of PV units is increased

.

It can also be concluded that distributed generation, when renewable, always decr eases the pollutant emissions. The extent to which the emissions are reduced will be heavily dependent on the generation mix within the region, with e ach generation type within the mix producing varying amounts of CO 2 

### , NOx, SOx , and PM

- 10

. If a particular region generates a majority of its electricity from carbon intensive sources, then the emission profile will see significant improvements with the deployment of renewable distribution generation. In contrast, regions such as the Pacific Northwest where hydropower composes a majority of the generation mix will only see a moderate improvement in the emission p rofile when renewable distribution generation resources are deployed. 

# 5.2 Distributed Generation Observations and Conclusions Summary

The analysis presented in this report has shown that many, but not all, of the benefits of the DG technologies deployed in the SGIG projects can be quantified and tracked using the SGIG metrics guidebook [2]. From the analysis conducted, and the metrics tracked, the following conclusions and observations can be made about DG technologies 1) DG technologies can be deployed by a util ity , or commercial , industrial , or residential customer s

. 2) DG technologies provide the primary benefit of reducing the amount of energy that must be supplied by the feeder for consumption by the end

- use customers. 3) DG technologies, when renewable, reduce net system emissions. 4) DG technologies increase system efficiency if properly coordinated , and ha ve the potential to increase system efficiency at higher penetration levels.

### 105 

### 5) In this report , DG technologies were deployed at the customer level, but have impacts at the feeder, as well as the transmission system. 6) DGs , particularly renewables , provide numerous benefits which have been tracked using the metrics specified in the SGIG guidebook [2]. However, t here are metrics that are either not directly tracked thoug h the SGIG smart grid metrics , or were outside the scope of this analysis

. If these metrics are included then DG provides numerous benefits that make it an even more attractive technology. Potential metrics

are: a) Deferred transmission capacity investment: This is a n SGIG smart grid metric , but a full analysis would require financial analysis of a specific utility and their long term planning strategy. b) Deferred distribution capacity investment: This is a n SGIG smart grid metric , but a full analysis would re quire financial analysis of a specific utility and their long term planning strategy. Improvement in load factor and feeder voltage profile measurements would provide utilities a good view for planning their distribution infrastructure to accommodate futur e load growth. c) Power quality: By providing sources near the end use load , voltage flicker and voltage sags could be reduced. This is especially true on long rural feeders. 

# 5.2.1 Observations and Conclusions Summary for PV Integration

There were three different implementation scenarios (residential PV, commercial PV and

c ombined PV) studied under PV integration. The following conclusions and observations can be made from the various analyses: 1) The primary benefit of solar PV is reduced annual energy consumption from the perspective of the distribution feeder

. The penetration levels in the simulations (2%

- 6% of peak load) provided a reduction of between 0.5% and 5% of ann ual energy consumption on most feeders. 2) Peak load is generally reduced on all the feeders an d is greatest in areas where PV output is coincident with peak load. 3) In general losses are reduced, but there are exceptions. Large unbalances of PV generation, high penetration level without other compensating technologies , or improperly sized PV generat ion can increase losses. 4) Annual CO 2 

### emissions are reduced 1%

- 5%, varying with generation mix.

### 106 

# 5.2.2 Observations and conclusions Summary for WTG Integration

From the simulation results and impact metrics study, the following conclusions and observations can be made for commercial wind: 1) The primary benefit of WTGs is a reduction in the annual energy consumption from the perspective of the distribution feeder . For the examined feeders , a single 1.8 MW wind turbine was installed. This provided between 5% and 20% reduction in annual energy consumption on the commercial feeders. 2) Peak load reductions of between 1% and 5 % were achieved on most of the feeders and were greatest in areas where wind is coincident with peak load. 3) CO 2 

### emissions were reduced by 10% to 50 %. Emission reductions were high because of the size of the WTG’s, 1.8 MW, was large in comparison to the feed er peak load, approximately 5 MW on the commercial feeders. Additionally, WTGs tend to produce more power at night when the generation sources t end to be carbon intensive.

### 107 

# Appendix A: SGIG Program Impact Metrics

An important component of the SGIG projects is the transfer of information from the individual projects to the broader industry audience. The aim of this transfer is to allow individua ls, research organizations and utilities to better understand the performance of the various technologies deployed on the various projects. Due to the large amount of potential data, it is not feasible for each grant recipient to provide all of the availa ble raw data. To address the issue of data collection, the “Guidebook for ARRA Smart Grid Program Metrics and Benefits” [2] was developed as a starting point for the discussion of data collection and impact categories. Specifically, the document containe d a table of impact metrics against which each project could be evaluated; it is these metrics that are used in the 4 technical reports in this series to evaluate the impact of the various technologies. Table A. 1 is a complete list of all 74 metrics liste d in the Guidebook and is included in this appendix as a reference. Not every metric is used for each technology, only those that are relevant to the specific technology are examined in Section 2

.
Table A. 1 : SGIG program impact metrics from guidebook



### # Metric Project Value System Value Remarks A 2.1 IMPACT METRICS: AMI and Customer Systems Metrics Related Primarily to Economic Benefits

1 Hourly Customer Electricity Usage kWh $/kWh Not Applicable Hourly electricity consumption information (kWh) and applicable retail tariff rate. Nature of this data will be negotiated with DOE

2 Monthly Customer Electricity Usage MWh $/kWh Not Applicable Monthly electricity consumption information (kWh) and applicable retail tariff rate. The nature of this data will be negotiated with DOE

3 Peak Generation and Mix MW Mix MW Mix Specify intermittent generation by type and amount

4 Peak Load and Mix MW Mix MW Mix Specify controllable load by type
5 Annual Generation Cost $ $ Total cost of generation to serve load 6 Hourly Generation Cost $/MWh $/MWh Aggregate or market price of energy in each hour

7 Annual Electricity Production MWh MWh Total electricity produced by central generation

8 Ancillary Services Cost $ $ Total cost of Ancillary services
9 Meter Operations Cost $ Not Applicable Includes operations, maintenance, reading and data management

10 Truck Rolls Avoided # Not Applicable Could include trips for meter reading, connection/disconnection, inspection and maintenance

### 108 

### # Metric Project Value System Value Remarks Metrics Related Primarily to Environmental Benefits

11 Meter Operations Vehicle Miles Miles Not Applicable Total miles accumulated related to meter operations 12 CO2 Emissions Tons Tons Could be modeled or estimated
13 Pollutant Emissions (S O x, NOx, PM

- 10) Tons Tons Could be modeled or estimated 

### Metrics Related Primarily to AMI System Performance

14 Meter Data Completeness % Not Applicable Portion of meters that are online and successfully reporting in

15 Meters Reported Daily by 2AM % Not Applicable Portion of meter reads received by 2AM the following day 

### A 2.2 Impact Metrics: Electric Distribution Systems Metrics Related to Economic Benefits
16 Hourly Customer Electricity Usage* kWh $/kWh Not Applicable Hourly electricity consumption information (kWh) and applicable retail tariff rate.
17 Annual Storage Dispatch* KWh Not Applicable Total number of hours that storage is dispatched for retail load shifting
18 Average Energy Storage Efficiency* % Not Applicable Efficiency of energy storage devices installed

19 Monthly Demand Charges* $/kW

month Not Applicable Average commercial or industrial demand charges

20 Distribution Feeder or Equipment Overload Incidents # Not Applicable The total time during the reporting period that feeder or equipment loads exceeded design ratings

21 Distribution Feeder Load MW MVAR Not Applicable Real and reactive power readings for those feeders involved in the project. Information should be based on hourly loads
22 Deferred Distribution Capacity Investments $ Not Applicable The value of the capital project(s) deferred, and the time of the deferral
23 Equipment Failure Incidents # Not Applicable Incidents of equipment failure within the project scope, including reason for failure
24 Distribution Equipment Maintenance Cost $ Not Applicable Activity based cost for distribution equipment maintenance during the reporting period
25 Distribution Operations Cost $ Not Applicable Activity based cost for distribution operations during the reporting period
26 Distribution Feeder Switching Operations # Not Applicable Activity based cost for feeders switching operations during the reporting period
27 Distribution Capacitor Switching Cost $ Not Applicable Activity based cost for capacitor switching operations during the reporting period
28 Distribution Restoration Cost $ Not Applicable Total cost for distribution restoration during the reporting period
29 Distribution Losses % Not Applicable Losses for the portion of the distribution system involved in the project. Modeled or calculated.

### 109 

### # Metric Project Value System Value Remarks

30 Distribution Power Factor pf Not Applicable Power factor for the portion of the distribution system involved in the project. Modeled or calculated.

31 Truck Rolls Avoided # Not Applicable Estimate of the number of times a crew would have been dispatched to perform a distribution operations or maintenance function 

### Metrics Related Primarily to Reliability Benefits
32 SAIF Index Not Applicable As defined in IEEE Std 1366

- 2003, and do not include major events days. Only events involving infrastructure that is part of the project should be included. 33 SAIDI/CAIDI Index Not Applicable

34 MAIFI Index Not Applicable
35 Outrage Response Time Minutes Not Applicable Time between outage occurrence and action initiated

36 Major Event Information Event Statistics Not Applicable Information should including, but not limited to project infrastructure involved (transmission lines, substations and feeders), cause of the event , number of customers affected, total time for restoration, and restoration costs

.

37 Number of High Impedance Faults Cleared # Not Applicable Faults cleared that could be designed as high impedance or slow clearing 

### Metrics Related Primarily to Environmental Benefits
38 Distribution Operations Vehicle Miles Miles Not Applicable Total miles for distribution operations and maintenance during the reporting period 39 CO2 Emissions Tons Tons Could be modeled or estimated
40 Pollutant Emissions ( S O x , NOx, PM

- 10) Tons Tons Could be modeled or estimated 

### A 2.3 Impact Metrics: Electric Transmission Systems Metrics Related Primarily to Economic Benefits

41 Annual Storage Dispatch* MWh MWh Total number of hours that storage is dispatched for wholesale energy markets or Ancillary services

42 Capacity Market Value* $/MW $/MW Capacity value

43 Ancillary Services Prices* $/MWh $/MWh Ancillary service price during hours when Storage was dispatched

44 Annual Generation Cost Not Applicable $ Total cost generation to serve load

### 110 

### # Metric Project Value System Value Remarks

45 Hourly Generation Cost Not Applicable $/MWh Aggregate or market price of energy in each hour

46 Peak Generation and Mix Not Applicable MW Mix Specify intermittent generation by type and amount

47 Peak Load and Mix Not Applicable MW Mix Specify controllable load by type

48 Annual Generation Dispatch Not Applicable MW Mix Total electricity produced by central generation

49 Ancillary Services Cost Not Applicable $ Total cost of Ancillary services

50 Congestion Cost MW Not Applicable Total transmission congestion cost during the reporting period

51 Transmission Line or Equipment Overload Incidents # Not Applicable The total time during the reporting period that line loads exceeded design ratings

52 Transmission Line Load MW MVAR Not Applicable Real and reactive power readings for those lines involved in the project. Information should be based on hourly loads
53 Deferred Transmission Capacity Investments $ Not Applicable The value of the capital project(s) deferred, and the time of the deferral
54 Equipment Failure Incidents # Not Applicable Incidents of equipment failure within the project scope, including reason for failure
55 Transmission Equipment Maintenance Cost $ Not Applicable Activity based cost for transmission equipment maintenance during the reporting period
56 Transmission Operations Cost $ Not Applicable Activity based cost for transmission operations during the reporting period
57 Transmission Restoration Cost $ Not Applicable Total cost for transmission restoration during the reporting period

58 Transmission Losses % Not Applicable Losses for the portion of the transmission system involved in the project. Could be modeled or calculated.

59 Transmission Power Factor pf Not Applicable Power factor for the portion of the transmission system involved in the project. Could be modeled or calculated. 

### Metrics Related Primarily to Transmission Reliability

60 BPS Transmission Related Events Resulting in Loss of Load (NERC ALR 1

- 4) # Not Applicable BPS Transmission Related Events Resulting in Loss of Load (NERC ALR 1

- 4)

61 Energy Emergency Alert 3 (NERC ALR 6

- 2) # Not Applicable Energy Emergency Alert 3 (NERC ALR

- 6

- 2) 

### Metrics Related Primarily to Environmental Benefits

### 111 

### # Metric Project Value System Value Remarks

62 Transmission Operations Vehicle Miles Miles Not Applicable Total mileage for transmission operations and maintenance during the reporting period 63 C O2 Emissions tons tons Could be modeled or estimated
64 Pollutant Emissions ( S O x , NOx, PM

- 10) tons tons Could be modeled or estimated 

### Metrics Related Primarily to Energy Security Benefits

65 Number, Type, and Size Events Cause Load Lost Not Applicable Causes could include line trips, generator trips, or other large disturbances

66 Duration Minutes/ Hours Not Applicable
67 PMU Dynamic Data PMU Data Not Applicable From related PMU's

68 Detection Application Not Applicable Application that detected the event

69 Events Prevented # Not Applicable Include reason for prevention 

### Metrics related primarily to PMU/PDC System Performance
70 PMU Data Completeness % Not Applicable Portion of PMU that are operational and successfully provided data
71 Network Completeness % Not Applicable Portion of PMUs networked into regional PDCs

72 PMU/PDC Performance Reliability Quality Not Applicable

73 Communications Performance Availability Not Applicable

74 Application Performance Description Not Applicable Usefulness of applications, including reliability improvements, markets and congestion management, operational efficiency



### The metrics shown in Table A. 1 were developed for field demonstrations and were not originally intended for simulations. To address this issue, definitions of the metrics in Table A.1 as implemented in the analysis will be given. Because the simulations in this report only examine impacts at the distrib ution level, transmission level impact metrics will not be examined. Of the distribution metrics, many will not be used because they are associated with a monetary cost that would require information from a specific utility. For example, meter operation costs.

The metrics will be presented in two separate places in this report. Appendix E will contain the metric values for each technology on each feeder. These values are individual to a single

### 112 

### technology. Section 4 .1, 4.2, and 4.3 will show the differe nce in metric values between the base case and the specific technology, for each feeder. 1) Hourly customer electricity usage: Instead of reporting a time series of values for an entire year this metric will report the average hourly end use consumption. 2) Monthly customer electricity usage: Instead of reporting a time series of values for an entire year this metric will report the average monthly end use consumption. 3) Peak generation and mix: This metric will report the peak generation as well as the perc entages for generation composition. This is the generation that is required to supply the demand as measured at the substation. The generation composition will include the breakdown of central generation as well as distributed resources on the distributi on system. 4) Peak load and mix: This is the maximum annual end use demand as consumed by the end use customers. This is the load that the utilities meter and charge for. The percent of load that is controllable will also be included. 5) Annual generation cos t: Because this is dependent on the business structure of specific utilities, this metric will not be used in evaluating the simulation results. 6) Hourly generation cost: Because this is dependent on the business structure of specific utilities, this metric will not be used in evaluating the simulation results. 7) Annual electricity production: This metric reports the total energy that is required to supply the demand as measured at the sub station 8) Ancillary services cost: Because this is dependent on the business structure of specific utilities, this metric will not be used in evaluating the simulation results. 9) Meter operations cost: Because this is dependent on the business structure of s pecific utilities, this metric will not be used in evaluating the simulation results. 10) Truck rolls avoided: Because this is dependent on the operational procedures of specific utilities, this metric will not be used in evaluating the simulation results. 11) Meter operations vehicle miles: Because this is dependent on the operational procedures of specific utilities, this metric will not be used in evaluating the simulation results. 12) CO2 emissions: This metric measure s the CO2 emissions required to supply the electricity to the end use load. 13) Pollutant emissions: This metric measure s SOx, NOx, and PM

- 10 emissions required to supply the electricity to the end use load.

### 113 

### 14) Meter data completeness: Because this is dependent on the operational procedures of spec ific utilities, this metric will not be used in evaluating the simulation results. 15) Meter reported daily by 2 a.m.: Because this is dependent on the operational procedures of specific utilities, this metric will not be used in evaluating the simulation r esults. 16) Hourly customer electricity usage: For the purposes of this work, this metric is identical to metric 1, and will not be used. 17) Annual storage dispatch: This metric examines the total number of hours that energy storage is dispatched. 18) Average energy storage efficiency : This is the average round trip efficiency for all energy storage units on a feeder. 19) Monthly demand charge: Because this is dependent on the business structure of specific utilities, this metric will not be used in evaluating the simulation results. 20) Distribution feeder or equipment overloads incidents: Because the taxonomy of prototypical feeders is used for analysis there are not overloads included. This is because the average distribution feeder does not normally have overlo ad conditions. As a result, this metric will not be used. 21) Distribution feeder load: This metric gives the annual average hourly load as measured at the substation. Both real and reactive powers are examined. 22) Deferred distribution capacity investment: B ecause this is dependent on the business structure of specific utilities, this metric will not be used in evaluating the simulation results. 23) Equipment failure incidents: Because the conducted analysis uses representative technologies there is no informa tion associated with equipment failure. The only failures are faults included for the analysis of FDIR. As a result this metric will not be used. 24) Distribution equipment maintenance cost: Because this is dependent on the business structure of specific ut ilities, this metric will not be used in evaluating the simulation results. 25) Distribution operations cost: Because this is dependent on the business structure of specific utilities, this metric will not be used in evaluating the simulation results. 26) Di stribution feeder switching operations: Because this is dependent on the operational procedures and business structure of specific utilities, this metric will not be used in evaluating the simulation results.

### 114 

### 27) Distribution capacitor switching costs: Beca use this is dependent on the operational procedures and business structure of specific utilities, this metric will not be used in evaluating the simulation results. 28) Distribution restoration cost: Because this is dependent on the business structure of sp ecific utilities, this metric will not be used in evaluating the simulation results. 29) Distribution losses: This metric measures the distribution losses; both series and shunt losses are included. Series losses due to overhead lines, underground lines, t ransformers, and triplex lines are included. Shunt losses due to underground lines and transformers are included. For the purposes of this metric all losses are combined into a single value but some plots will be provided that break the losses into the v arious components. 30) Distribution power factor: The distribution power factor is the power factor as calculated at the substation. 31) Truck tolls avoided: Because this is dependent on the operational procedures of specific utilities, this metric will not be used in evaluating the simulation results. 32) SAIFI: As defined in IEEE standard 1366 SAIFI is the system average interruption frequency index. SAIFI i ndicated how often the average customer experiences a sustained interruption and is calculated by dividing the sum of the total number of customers interrupted by the total number of customers served. 33) SAIDI/CAIDI: As defined in IEEE standard 1366 SAIDI is the system average interruption duration index. SAIDI in dicates the total duration of interruption for the average customers and is calculated by dividing the sum of the customer interruption durations by the total number of customers served. As defin ed in IEEE standard 1366 CAIDI is the customer average interruption duration index. C AIDI represents the average time required to restore service and is calculated by dividing the sum of the customer interruption durations by the total number of customers interrupted. 34) MAIFI: As defined in IEEE standard 1366 MAIFI is the momentary average interruption frequency index. MAIFI is the average frequency of momentary interruptions and is calculated by dividing the sum of the total number of customer momentary interruptions by the total number of customers served. 35) Outage response time: When a fault occurs on the system there are several important times. How long to identify the existence of a fault, how long to locate the fault, and how long to repair the fau lt. The outage response time is the time between the occurrence o f the fault and the time to identify the existence of the fault.

### 115 

### 36) Major event information: Major events generally impact a large geographic area which includes multiple distribution substat ions and the interconnecting transmission or sub

transmission system. Since this report is looking primarily at individual feeders this metric will not be used. 37) Number of high impedance faults cleared: This metric is based on the occurrence of high imped ance faults in a specific system. The occurrence of faults is only handled in the fault detection identification and restoration technology; high impedance faults are not specifically examined. 38) Distribution operations vehicle miles: Because this is dependent on the operational procedures of specific utilities, this metric will not be used in evaluating the simulation results. 39) CO2 emissions: This metric measures the CO2 emissions required to supply the demand as measured at the subs tations. 40) Pollutant emissions: This metric measures the SOx, NOx, and PM

- 10 emissions required to supply the demand as measured at the substations.

### 116 

# Appendix B: Taxonomy of Prototypical Distribution Feeders

As part of the DOE

- OE Modern Grid Initiative ( MGI ) efforts of 2008 , a Taxonomy of Prototypical Distri bution Feeders was developed [2] . The feeders within this taxonomy were designed to provide researchers with an openly available set of distribution feeder models which are representative of those see n in the continental United States. To construct these representative feeder models, actual feeder models were obtained from utilities across the country and their fundamental characteristics were examined. A detailed statistical analysis was conducted t o determine the optimal subset of feeders that could effectively represent the entire data set. The development of the complete Taxonomy of feeder was an extensive process and is fully documented in the report titled “Modern Grid Initiative Distribution T axonomy Final Report” [2]. Because climate and energy consumption are closely coupled, the prototypical feeders were divided into five climate regions, Figure B. 1 , based on the U.S DOE handbook (1980) providing design guidance for energy

- efficient small o ffice buildings [21]

.

Figure B. 1 : Climate Zones Used for Development of Prototypical Feeders 

### Within each of the climate zones, there are a set of feeders that are approximations of the types of feeders that are seen within that zone. Table B.1 gives a summary of the 24 prototypical feeders, including feeder name, base voltage, peak load, and a qualitative description. The peak loading used for the SGIG project analysis is slightly different than the original values from th e 2008 report

. The difference in peak load is due to improved modeling methods used to represent the end

- use load. These methods will be discussed in Sections B.2.1. and B.2.2

.

### 117 Table B. 1 : Summary of prototypical feeders



### Feeder Base kV Peak kVA Description

R1

- 12.47

- 1 12.5 4,300 Moderate suburban and rural R1

- 12.47

- 2 12.47 2,400 Moderate suburban and light rural R1

- 12.47

- 3 12.47 1,800 Small urban center R1

- 12.47

- 4 12.47 4,900 Heavy suburban R1

- 25.00

- 1 24.9 2,300 Light rural R2

- 12.47

- 1 12.47 6,700 Light urban R2

- 12.47

- 2 12.47 6,700 Moderate suburban R2

- 12.47

- 3 12.47 4,800 Light suburban R2

- 25.00

- 1 24.9 21,300 Moderate urban R2

- 35.00

- 1 34.5 6,900 Light rural R3

- 12.47

- 1 12.47 11,600 Heavy urban R3

- 12.47

- 2 12.47 4,000 Moderate urban R3

- 12.47

- 3 12.47 9,400 Heavy suburban R4

- 12.47

- 1 13.8 6,700 Heavy urban with rural spur R4

- 12.47

- 2 12.5 2,100 Light suburban and moderate urban R4

- 25.00

- 1 24.9 1,000 Light rural R5

- 12.47

- 1 13.8 10,800 Heavy suburban and moderate urban R5

- 12.47

- 2 12.47 4,200 Moderate suburban and heavy urban R5

- 12.47

- 3 13.8 4,800 Moderate rural R5

- 12.47

- 4 12.47 6,200 Moderate suburban and urban R5

- 12.47

- 5 12.47 8,500 Moderate suburban and light urban R5

- 25.00

- 1 22.9 9,300 Heavy suburban and moderate urban R5

- 35.00

- 1 34.5 12,100 Moderate suburban and light urban GC

- 12.47

- 1 12.47 5,400 Single large commercial or industrial



### The original prototypical feeders were modeled in detail from the substation to the end

- use point of interconnection, but did not include detailed load models. To use these feeders for an accurate analytic assessment of the SGIG projects, it was necessary to model t he end

- use load models in the appropriate level of detail as was done for the 20 10 report on Con servation Voltage Reduction [22]

. 

## B.1 End

- u se Load Models

The taxonomy of prototypical feeders accurately represents the electrical infrastructure of the distribution feeders, but not the end

- use loads. Since it is the end

- use loads that consu me the majority of the energy on a distribution feeder, it is critical to accurately represent their operation.

### 118 

### For the taxonomy of feeders to be of use the end

- use load s are classified into various categories. In 2010, an analysis of conservati on voltage reduction was conducted in GridLAB

- D that classified loads, as shown in Table B.2 [22] . Because the analysis of the SGIG projects includes technologies other than conservation voltage reduction, a more complete handling of end

- use load classificat ions is necessary and will be discussed in detail in section B.2. This is especially true of technologies such as demand response where the physical characteristics of the buildings are fundamental.

Table B. 2 : End

- use load cla ssifications


### Load Class Description Residential 1 Pre

- 1980 <2000 sqft. Residential 2 Post

- 1980 <2000 sqft. Residential 3 Pre

- 1980 >2000 sqft. Residential 4 Post

- 1980 >2000 sqft. Residential 5 Mobile Homes Residential 6 Apartment Complex Commercial 1 >35 kVA Commercial 2 <35 kVA Industrial All Industrial



### Regardless of how end

- use loads are classified, the component end

- use loads are modeled as a combination of ZIP models and multi

- state physical models . The ZIP load model and the multi

state mode l are described in the following Sections

. 

## B.1.1 ZIP Loads

ZIP models are two state models, energized and de

- energized. When energized there is only a single operational state and the energy consumption can be determined using (B1) for real power, (B2) for reactive power, and (B3) as a constraint [24]

.



# ( ) ( ) ( )        

⋅ ⋅ + ⋅ ⋅ ⋅ + ⋅ ⋅ ⋅ = θ θ θ 

### P P S I I S V V Z Z S V V P n n
n a
n

n a i 

### cos cos cos % % % 2 2 

### (B1)



# ( ) ( ) ( )        

⋅ ⋅ + ⋅ ⋅ ⋅ + ⋅ ⋅ ⋅ = θ θ θ 

### P P S I I S V V Z Z S V V Q n n
n a
n

n a i 

### sin sin sin % % % 2 2 

### (B2)

% % % 

## 100 P I Z + + = (B3)

### 119 

### where:

P i 

### : real power consumption of the i th 

### load Q i 

### : reactive power consumption of the i th 

### load V

a 

### : actual terminal voltage Vn : nominal terminal voltage S n 

### : apparent Power consumption at nominal voltage Z % 

### : percent of load that is constant impedance I

% 

### : percent o f load that is constant current P % 

### : percent of load that is constant power Z

θ 

### : phase angle of constant impedance component I

θ 

### : phase angle of constant current component P θ 

### : phase angle of constant power component

In a time

- variant load representation, the coefficients of the ZIP model, V n 

### , S n 

### , Z % 

### , I % 

### , P % 

### , Z θ 

### , I

θ 

### , and P θ , 

### remain constant, but the power consumption, P i and 

### Q i , 

### of the i th 

### load varies with the actual terminal voltage, V a 

### . The ZIP model is similar to the polynomial representation used in many commercial software packages. In the polynomial representation of the ZIP load, the constant coefficient is equivalent to P % 

### , the linear coefficient is equivalent to I % 

### , and the qua dratic coefficient is equivalent to Z % 

### . The ZIP model only varies the power consumption as a function of actual terminal voltage, V a 

### . In (B1) and (B2), there are six constants that define the voltage dependent behavior of the ZIP load: Z % 

### , I % 

### , P % 

### , Z θ 

### , I θ 

### , and P θ 

### . Because the actual value of the distribution feeder voltage continually changes, it is critical to understand how the energy consumption of end

- use loads will vary. Specifically, what are the six constants that accuratel y reflect vari ous end

- use loads? For loads such as a heating element, it is clear that the load is 100% Z, but for more complicated loads such as a Liquid Crystal Display (LCD) or Compact Florescent Light (CFL), the proper ratios are not as apparent.

As part of the 20 10 report on conservation voltage reduction a number of laboratory tests were conducted to determine the six constants for various end

- use loads; these values have been incorporated into the end

- use load models for this study. Figure B.2 is an example of the laboratory testing that was conducted on a 13W compact florescent light bulb.

### 120 Figure B. 2 : Voltage dependent energy consumption of 13W CFL


### ZIP Values Z

- % I

- % P

- % Z

- pf I

- pf P

- pf CFL

- 13W 40.85% 0.67% 58.49%

- 0.88 0.42

- 0.78



### In traditional distribution analysis ZIP models are generally not developed for every individual load, instead models are develop ed for load classes such as residential, commercial, and industrial. Every load within a given load class then uses the same ZIP values with the exception of the apparent power consumption at nominal voltage, S n 

### . The value of S n 

### for each load may change at 1

- hour intervals to generate a daily load profile at the feeder level. The use of similar ZIP values for each load class, which only change at 1

- hour intervals, is not able to represent coincidental load peaks that occur at th e distribution level.


## B.1.2 Single

- State Detailed Physical Models

When the energy consumption of an end

- use load is a function of variables other than terminal voltage, the use of a ZIP model is not adequate. This is true of any load with an external control system or an internal control loop. To illustrate this issue, the air conditioning system of a single family residence will be examined while in the cooling mode. As with the ZIP model, an air conditioning system is a two state model (ON or OFF), but onl y has a single operational state. Because a cooling system operates to maintain internal air temperature within a band, parameters such as near term history of operation, time of year, outside air temperature, building construction, and terminal voltage will impact the instantaneous power consumption, as well as the energy consumption. To examine these issues, a physical model of the cooling system and the structure of the building, is constructed using an equivalent thermal parameter (ETP) model [24]. Because the ETP model has been shown to be an accurate representation of residential and small commercial building instantaneous power draw, as well as energy consumption, it will be used for the formulation of the physical model. 0 2 4 6 8 10 12 14

100 105 110 115 120 125Watts

Volts 

### Real Power Comparison

Pe
Pm

- 9 - 8 - 7 - 6 - 5 - 4 - 3 - 2 - 1 0

100 105 110 115 120 125VAR

Volts 

### Reactive Power Comparison

Qe Qm

### 121 

### Figure B. 3 is a diagram showing the heat flow for the ETP model of a single family residence, i.e., a house. While the heating/cooling system can be one of any numerous types, for the purposes of this paper, it is assumed that the system is a heat pump in the cooling mode. In addition to the heat removal of the heat pump while cooling and the heat gain through the building exterior, there are two additional significant flows of heat within a house: incident solar radiation and internal gains from waste heat generated by end

- use loads. These sources and sinks of heat constitute the total heat energy exchange in the house. This flow of heat is then divided between the air in the house and the mass of the house, i.e., walls and furniture. A portion of the incident solar energy s hining through a window will heat the interior air of the house, while the remaining incident energy will be absorbed by the walls, floors, and furniture. The same division occurs with the waste heat from end

- use loads. The internal air temperature of the house is thermally coupled to the internal mass temperature, and the internal air temperature is then thermally coupled to the outside air temperature through the thermal envelope of the house.

Internal Gains Solar HVAC

Total Heat



### T air 

### T out 

### T mass



### C air 

### C mass 

### T set



### UA env 

### UA mass Q

mass 

### Q air Q solar 

### Q gains Q

hvac

Figure B. 3 : The ETP mode of a residential heating/cooling system



### where,

C

air : air heat capacity (Btu/°F) C

mass : mass heat capacity (Btu/°F) UA env : external gain/heat loss coefficient (Btu/°F

- h) UA mass : internal gain/heat loss coefficient (Btu/°F

- h) T

out : air temperature outside the house (°F)

### 122 T

air : air temperature inside the house (°F) T

mass : mass temperature inside the house (°F) T

set : temperature set points of HVAC system (°F) Q air : heat rate to house air (Btu/h) Q gains : heat rate from appliance was te heat (Btu/h), Q hvac : heat rate from HVAC system (Btu/h), Q mass : heat rate to house mass (Btu/h), and Q solar : heat rate from solar gains (Btu/h).



### Equation (B4) is the second order differential equation that describes the heat flows shown in Figure B. 3 [24] . Its solution determines the time

- varying temperature of the house, both air and mass, given the thermal inputs. With the inside air temperature, T air 

### , known, the thermal behavior of the heat pump system in response to the defined thermostatic set point, T set 

### , can be determined.



## d cT dt dT b dt

T d a air air air 

## = + + 2 2 

### (B4)

Where,

mass air mass 

## UA C C a ⋅ = 

# ( )

air

mass mass env mass 

## C UA UA UA C b + + ⋅ =

env 

# UA c = 

# ( ) out env air mass 

# T UA Q Q d

⋅ + + =

With the temperature of the house known from (B4) and the occupant

- controlled set point fixed, the operation of the cooling system can be determined. Based on these values, the cooling system will operate long enough to remove the heat necessary to maint ain the inside air temperature, T air 

### , within the desired range. The electrical input energy to the motor, S comp
- motor 

### , necessary to provide the thermal heat energy, is a function of two elements: the heat flow through the cooling unit, Q hvac 

### , and the elec trical losses of the compressor motor, S losses 

### ; as shown in (B5) [23]

- [24]

.

### 123 

# ( ) ( ) 

# [ ] T losses T out hvac motor comp 

## V S COP V T Q S + =

− 

## , , (B5)

The coefficient of performance ( COP ) is a scalar value that relates the cooling rate of the heat pump unit to the mechanical power delivered by the compressor as a function of temperature and operation time. A higher value of COP indicates less electrical power is necessary to remove a given amount of heat from the air. V T 

### is the terminal voltage of the system compressor motor. Additionally, it should be noted that Q hvac 

### is expressed in terms of British thermal units (Btu) consistent with the conventions of the heating/cooling industry in t he United States and the derivation of the ETP model of [24] , while S losses 

### is expressed in SI units. As a result, the two terms of (B5) must be converted using the conversion of 1.0 Btu/h = 0.2931 W.

Because both of the elements of (B5) are voltage dep endent, changes in line voltage will cause a change in power consumption. The cooling system's heat removal rate, Q hvac 

### , can be solved using heat transfer equations based on the available mechanical torque of the compressor [24]

. The motor losses, S losse s 

### , can be determined using the traditional split phase motor model of [23] and [24] . When (B5) is implemented in a time

- series simulation, the result is a model that determines the energy consumption, both real and reactive, of the cooling system as a fun ction of the outside air temperature, the inside air temperature, equipment parameters, terminal voltage, and occupant

- controlled set point.

Unlike ZIP models that apply the same values to each load in a given load class, physical models are specific to each individual load. The values of physical models vary on a 1 second or 1 minute basis to capture the true time

- variant nature of the end

- use load.

The previous example of a physical model has examined a heat pump in the cooling mode, which is one of multiple operational states. Because of the design of heat pumps, their energy consumption varies according to their current operational state. To properly capture the energy consumption it is necessary to construct a multi

- state load model.


## B.1.3 Multi

- State Detailed Physical Models

A multi

- state time

- variant load model uses more than one state to describe the energy consumption of an end

- use load. Each state is governed either by a ZIP model and/or a physical model, with transitions between states determined by either internal state transition rules or external signals. For example, a typical heat pump has four normal operating states: State 1 ( off ), State 2 ( cooling ), State 3 ( heating

- normal ), and State 4 ( heating

- emergency ). State 2 operates as described in the previous section, and State 3 follows a similar description but with different values that represent the change in the heating cycle, i.e., heat is added instead of removed. State 4 operates as State 3, except that the COP is 1.0 and the load is a ZIP model. There are other abnormal states such as “stalled compressor motor” or "low refrigerant charge", but they will not

### 124 

### be examined in this paper. Additionally, there are numerous heat pump types and many differing t hermostatic controllers that are commercially available, but this paper will discuss a “typical” design. Because a heat pump has two heat

- flow configurations, the value of T set 

### must be split into a heating set point, T low 

### , and a cooling set point, T high 

### . These set points determine the mode of operation of the heat pump system at any given time: off , cooling , heating

- normal , or heating

emergency , as shown in Figure B. 4

.

For a simple single state simulation, the heat pump system would be operating to eith er heat or cool the house, as discussed in the previous section. For a time

- series simulation, the multi

- state model captures the transitions between states. While a heat pump system may not transition through all operational states in a single day, it i s likely that it will transition through more than one state in any given day. For example, on a mild autumn night, the heat pump may operate to heat the house, then as the sun heats the house during the day, it may be necessary to switch to cooling.

State 1 : Off State 2 : Cooling

State 3 : Heating

- Normal State 4 : Heating Emergency

Figure B. 4 : Multi

- state load model 

### To be in States 2, 3, or 4, the heat pump unit must be turned “on” with defined set points, both occupant

- controlled and internal. The occupant

- controlled set points a re T high 

### and T low 

### . If the internal air temperature T air 

### rises above T high 

### plus a dead band, DB high 

### , then the heat pump will start cooling. If T air 

### decreases below T low 

### minus a dead band, DB low 

### then the heat pump will start heating normally. If T out 

### decreases to a temperature, T aux 

### , where the heat pump efficiency becomes too low to effectively heat the home, the system will start heating in the emergency state using resistive heating elements. In addition to the internal control parameters of T aux 

### , the DB low 

### and DB high 

### are internal parameters that are not occupant

- controlled, but are included to prevent the heat pump from cycling excessively. Table B.3 gives the logic for the allowable state transitions shown in Figure B. 4

.

### 125 Table B. 3 : Heat pump state transition logic


### From State To State Transition Rule

1 2 T air > ( T high + DB high )

1 3 T air < ( T low – DB low )

1 4 T air < ( T low – DB low ) & T out < T aux
2 1 T air < ( T high – DB high )

3 1 T air > ( T low + DB low )

3 4 T out < T aux 

### 4 1 T air 

### > ( T low 

### + DB low 

### )



### Each of the four discrete states of operation has a different set of characteristics that determine the instantaneous power consumption. In State 1, there is no power draw because the system is off. In State 2 and State 3, there is an electric fan motor plus a compressor motor. Similar to State 3, State 4 provides heating with an associated electric fan for ventilation, but with the difference that heating is provided by resistive heating elements and not a heat pump. The instan taneous power draw of the four states shown in Figure B.4 is given by (B6)

- (B9).

State 1: Off 

# 0 = HVAC 

# S (B6)

State 2: Cooling

motor comp motor fan HVAC 

## S S S

− − 

## + = (B7)

State 3: Heating

- Normal

motor comp motor fan HVAC 

## S S S

− − 

## + = (B8)

State 4: Heating

- Emergency

### 126 elements T motor fan HVAC 

# R V S S 2 

# + =

− 

### (B9)

where, S fan

- motor : apparent power of ventilation fan motor (VA) S comp
- motor : apparent power of compressor motor (VA) V

T : terminal voltage of the heat pump unit (V) R elements : resistance of the heating coil elements (Ω)



### While the power consumption for State 2 and State 3, given by (B7) and (B8) respectively appear to be the same, there are different internal models for Q hvac 

### , particularly with respect to the COPs. With the instantaneous power draw determined by (B6)

- (B9), the time necessary to heat or cool the ho use to within the occupant

- controlled set points is determined by the solution to (B4). The result is that variations in temperature, voltage, and efficiency are translated into a variable duty cycle of the heat pump. This information can then be used to determine the instantaneous power demand and the energy consumption of the heat pump over time.


## B.2 Model Extraction and Population

Section B.1 discussed the physical infrastructure of the distribution feeders and gave an overview of the level of detail that is modeled at the end

- use . This section describes how the detailed end

- use models are populated onto the prototypical distribution feeders.

The taxonomy of prototypical feeders was originally populated with a series of spot loads representing a standard peak load study. Each spot load was classified as residential, commercial, agricultural, or industrial. In this analysis, due to the broad nature of industrial and agricultural loads and the difficulty in accurately representing these loads, each of the se loads

was re
- classified as commercial, leaving only residential and commercial loads. Each load was replaced with building models appropriate to the region of the United States where the prototypical feeder was located. The representative commercial a nd residential models will be described here.


## B.2.1 Residential Loads

At each triplex node, the residential spot load was replaced with a number of residential house models, which under peak conditions approximately matched the original spot load. The number of house models replacing the original peak load depended upon a scaling factor unique to each taxonomy feeder model and was used to calibrate the populated feeder model to the peak load study. For example, if the original spot load was 10 kVA and the feede r scaling factor was determined to be 5 kVA / house, the spot load would be replaced with two house models. In all cases, the number of homes was rounded to the nearest integer, while the residual from the

### 127 

### rounding was used as a weighting factor. For exa mple, if the same 10 kVA load was used with a scaling factor of 5.5 kVA / home, the number of homes would be 1.82. The number was rounded to two homes and the difference of 0.18 was used as a weighting factor on the square footage of the homes populated a t that location, creating two house models with a slightly lower than the average square footage. The scaling factor was used to calibrate the new feeder model to the peak load study. Multiple annual simulations were run on each feeder until the peak loa d for the annual simulation approximately equaled that of the peak load study.

The parameters of each home were determined by the climate region the feeder was located in. Data from the Energy Information Administration’s (EIA) 2005 Residential Energy Con sumption Survey [25] was used to create a population of homes for each feeder which contained the average characteristics from that region. The EIA divides the country into ten regions, while the U.S. DOE Handbook providing design guidance for energy

- efficient small office b uildings [13], which was used to create the taxonomy feeders, only uses five. Table B.4 shows the weighting factors used to map the characteristics between the two sets of regional data.

Table B. 4 : Table of weighting factors fo r mapping regional parameters


### Taxonomy Feeder Climate Regions Building Survey Climate Region Weighting

1 West Coast 1 Pacific

2 Northern 0.5 Mountain 1 W N Central 1 E N Central 1 Mid Atlantic 1 New England
3 S outhwest 0.5 Mountain 0.33 W S Central

4 Mid

- Atlantic 0.33 W S Central 0.5 E S Central 0.5 S Atlantic

5 Southern 0.33 W S Central 0.5 E S Central 0.5 S Atlantic



### From the EIA data and the weighting factors, a set of key, average building parameters were created as a basis for the population of each feeder. The residential building models were broken into three types: single family homes, apartments, and mobile hom es. The age of the home was used to create a set of thermal integrity levels for each housing age and type, from poorly insulated to well insulated, and key parameters were assigned by region and age of home. Table B.5 shows the average thermal integrity properties by age of the single family homes,

### 128 

### apartments, and mobile homes. Each of these parameters was then randomized, where appropriate, around the average value with either a normal or uniform distribution to create a diversified population which ap proximately represents the average household characteristics in that region. More details on the randomizations used can be found in the feeder generator script found on the open source repository [4].

Table B

. 6 , Table B.7 , and Table B

. 8 provide a break down of the percentage of single family homes, apartments, and mobile homes, and their corresponding ages, used in creating the randomized population of buildings per region. In addition, other average parameter values were extracted from the EIA document ation, including square footage, cooling and heating set points, heating type, air conditioning penetration, electric water heater penetration, and pool pump penetration. These are listed in Table B.9 through Table B. 11

. Table B. 5 : Residential thermal integrity values by age of home

R Roof R Wall R Floor Glass Layers Glass Type Glazing Treatment Window Frame R Door Air Infiltration COP High COP Low Single Family

Pre

- 1940 16 10 10 1 Glass Clear Alum. 3 0.75 2.8 2.4 1940

- 1949 19 11 12 2 Glass Clear Alum. 3 0.75 3.0 2.5 1950

- 1959 19 14 16 2 Glass Clear Alum. 3 0.50 3.2 2.6 1960

- 1969 30 17 19 2 Glass Clear TB 3 0.50 3.4 2.8 1970

- 1979 34 19 20 2 Glass Clear TB 3 0.50 3.6 3.0 1980

- 1989 36 22 22 2 Low

- e Clear TB 5 0.25 3.8 3.0 1990

- 2005 48 28 30 3 Low

- e Abs. Ins. 11 0.25 4.0 3.0 Apartment Pre

- 1960 13 12 9 1 Glass Clear Alum. 2 0.75 2.8 1.9 1960

- 1989 20 12 13 2 Glass Abs. TB 3 0.25 3.0 2.0 1990

- 2005 29 14 13 2 Low

- e Refl. Ins. 6 0.13 3.2 2.1 Mobile Home

1960

- 1989 13 9 12 1 Glass Clear Alum. 2 0.75 2.8 1.9 1990

- 2005 24 12 18 2 Low

- e Clear TB 3 0.75 3.5 2.2 Note 1: R is in units of °F.sf.h/BTU, air infiltration is in units of air changes / hour, COP is in units of BTU/kWh Note 2: Low

- e refers to low emissivity glass, Abs. refers to absorptive glass, Refl. refers to reflective glass, Alum. refers to an aluminum frame, TB refers to thermal break insulation, Ins. refers to insulated

Table B. 6 : Percentage of single family homes in total population by age and region



### Pre

- 1940 1940

- 1949 1950

- 1959 1960

- 1969 1970

- 1979 1980

- 1989 1990

- 2005 Region 1 8.05 7.24 10.90 8.67 13.84 12.64 12.97 Region 2 15.74 7.02 12.90 9.71 9.41 7.44 15.32 Region 3 4.48 2.52 8.83 8.43 11.85 13.15 24.11 Region 4 5.26 3.37 8.06 8.27 10.81 12.49 25.39 Region 5 5.26 3.37 8.06 8.27 10.81 12.49 25.39

### 129 Table B. 7 : Percentage of apartments in total population by age and region



### Pre

- 1960 1960

- 1989 1990

- 2005 Region 1 3.56 12.23 2.56 Region 2 4.81 8.87 3.03 Region 3 1.98 11.59 4.78 Region 4 2.17 10.91 5.02 Region 5 2.17 10.91 5.02

Table B. 8 : Percentage of mobile homes in total population by age and region


### 1960

- 1989 1990

- 2005 Region 1 5.54 1.81 Region 2 8.87 3.03 Region 3 5.24 3.02 Region 4 4.91 3.33 Region 5 4.91 3.33

Table B. 9 : Percentage of key building parameters by region
Heating Fuel Type With Air Conditioner With Electric Water Heater With Pool Pump* One

- Story Home* Non

- Electric Heat Pump Resistance Region 1 70.51 3.21 26.28 43.48 25.45 9.04 68.87 Region 2 89.27 1.77 8.96 75.28 25.15 5.91 52.10 Region 3 67.23 5.59 27.18 52.59 34.80 8.18 77.45 Region 4 44.25 19.83 35.92 96.73 64.28 6.57 70.43 Region 5 44.25 19.83 35.92 96.73 64.28 6.57 70.43

*Note: Percentage with pool pumps and one

- story homes was only applied to single family homes.

### 130 Table B. 10 : Percentage of nighttime heating and cooling set points by housing type 

### Single Family Apartment Mobile Home Set point (°F) Cooling 65

- 69 9.8 15.5 13.8 70

- 70 14.0 20.7 17.2 71

- 73 16.6 10.3 17.2 74

- 76 30.6 31.0 27.6 77

- 79 20.6 15.5 13.8 80

- 85 8.4 6.9 10.3 Heating 59

- 63 14.1 8.5 12.9 64

- 66 20.4 13.2 17.7 67

- 69 23.1 14.7 16.1 70

- 70 16.3 27.9 27.4 71

- 73 12.0 10.9 8.1 74

- 79 14.1 24.8 17.7

Table B. 11 : Average square footage by building type and region 

### Single Family Apartment Mobile Home Region 1 2209 820 1054 Region 2 2951 798 1035 Region 3 2370 764 1093 Region 4 2655 901 1069 Region 5 2655 901 1069



### Of note is the cooling and heating set points found in Table B. 10 . Heating and cooling set points bins were chosen randomly and independently, except to require that the heating set point be below the cooling set point. Within each bin a uniform distribution was used to determine the actual nighttime set point for eac h home. Additionally, data from the surveys showed average daytime versus nighttime offsets. Offsets were uniformly distributed between zero and twice the average offset, and the time at which the offsets occurred was randomized across the population. F igure B. 5 provides a few examples of the diversity of cooling set points established through this methodology, while Figure B. 6 shows the average cooling set point on a summer day of all the residential homes within the R1

- 12.47

- 2 feeder.

### 131 Figure B. 5 : Exemplary cooling set points diversified with time and daytime and nighttime offsets

Figure B. 6 : Average cooling set points of entire population of R1

- 12.47

- 2


### It is important to note that the populated building models were not designed to represent any particular feeder circuit or city in the United States, but rather as a blended average of large climate regions within the United States. While this will not perfectly capture the behavior of any particu lar city or utility, it is designed as a representative analysis. Additional methods exist where a utility can provide very specific load data which is much more representative of the local population, and design an analysis which is much more suited to t hat particular application.

### 132 

### The parameter values, in conjunction with estimated demand, were used to describe the state models of the hot water heater, HVAC system, and pool pump. However, additional loads were represented as scheduled ZIP loads. “Applia nces” such as refrigerators and lights were divided into two categories: responsive and unresponsive loads. Responsive loads indicate that the customer is able to modify the behavior of the appliance due to a price signal, while unresponsive loads indicat e that the customer is typically not willing or able to modify the behavior without investment in additional technologies (e.g. demand response enabled appliances). Responsive loads included lights, plug loads, clothes washers, clothes dryers, dishwashers , cooking ranges, and microwaves, while unresponsive loads included refrigerator and freezer loads. These were divided in anticipation of demand response studies and the shift of customer behavior that is associated with Time

- of

- Use or Critical Peak prici ng. ELCAP load data [18] was used to create a base hourly load profile for responsive and unresponsive loads, with adjustments made for 20 years of increased efficiency and increased or decreased demand, and included seasonal and weekday versus weekend ef fects, as shown in Figure B. 7 and Figure B. 8 . Additionally, loads were scaled as a function of square footage using a regression, again using ELCAP data. The proper scalar from the regression is shown in (B10):



## 8760 / 1000 * * 9

. 324 442

. 

## area floor k = (B10)



### The scalar was then randomized +/

- 20% over a uniform distribution. While this provided no single home with a load shape representative of a time

- series of an actual home , the aggregate load shape was representative of an entire population of homes, and internal loading of each home provided internal heat gains appropriate to that size of home.

### 133 Figure B. 7 : Average energy consumption of responsiv e loads

Figure B. 8 : Average energy consumption of unresponsive loads

### 134 

## B.2.2 Commercial Loads

At this time, a fully implemented, multi

- zone commercial building model is not available within GridLAB

- D. However, to represent the “zones” of a commercial building, multiple house models were created to represent the commercial load. These loads were cr eated using very generic commercial building characteristics and load patterns. The commercial loads (and the

re
- classified industrial and agricultural loads) were divided into three types: office buildings, large retail “box” buildings, and small retail strip malls. The key characteristics of these models were developed through federally

- supported building codes and end

- use metering studies, and are not based on regional differences as the residential models were [19

- 20]. Population of the prototypical feeders and the three types of buildings was performed by size of the original load and the number of phases the load was attached to. Similar to the residential loading, a scalar was used to calibrate the loading on each feeder model, modifying the numbe r of loads and size of each load.

Office buildings were represented by a three

- story, fifteen

- zone model as shown in Figure B. 9

. These replaced loads within the taxonomy feeder that were three

- phase and “larger”, as defined by the scaling factor. The av erage square footage was 40,000 sf., with a uniform deviation of 50%, while maintaining the geometrical relationship of each zone. Each of the zones has identical parameter values, except square footage, aspect ratio, external wall area, external floor

ar ea, and external ceiling area. Assumptions are made in this model to better represent the zonal attributes of a commercial building. It is assumed that the adjacent zone has approximately the same air and mass temperature as the current zone, so that the re is no heat transfer across the boundaries. This means that the internal wall, ceiling, or floor areas do not lose or gain heat from adjacent zones, and can therefore be ignored when defining the thermal envelope of the building. For example, Zone 5 on the second floor in Figure B. 9 will have an external wall area of 0 sf., an external floor area of 0 sf., and an external ceiling area of 0 sf. This zone would only have heat added (or removed) through end

- use loads and the HVAC system. Zone 2 on the th ird floor will have an external wall area equal to one

- half its total wall area, and external floor area also equal to 0 sf., and an external ceiling area equal to its floor area, allowing additional heat flows across the external boundaries. By defining each zone within the constraints of the geometrical model, then defining where heat transfer across boundaries is allowed and not allowed, a zonal model can be roughly represented. Notice that Figure B. 9 contains a variable ‘x’. This variable would be ad justed by the randomly chosen square footage so that 3*1.5*x 2 

### equaled the total square footage, while all other parameters except for the widths of Zones 1

- 4 adjusted within the geometrical constraints. The other building type zones were defined in a simi lar manner. Table B. 12 shows the key parameters used to define the office building zones. Additionally, since the office building is considered a larger, single owner, customer billing was performed as an aggregate of all the “zones”.

### 135 Figure B. 9 : Office zonal floor plan representing 1 of 3 identical floors

Table B. 12 : Key parameters for commercial buildings


### Office Big Box Strip Mall Square Footage 40,000 +/

- 50% 20,000 +/

- 50% 2400 +/ 30% Ceiling Height 13 14 12 Air Infiltration 0.69 1.5 1.76 R Roof 19 19 19 R Wall 18.3 18.3 18.3 R Floor 46 46 40 R Door 3 3 3 Glazing Layers 2 2 2

Glass Type Glass Glass Glass Glazing Treatment* Low S Low S Low S

Window Frame None None None No. of Doors* 0 0 / 1 / 24 1 Window to Wall Ratio 0 / 0.33 0 / 0.76 0.03 / 0.05 Internal Gains (W/sf) 3.24 3.6 3.6 Cooling COP 3 +/

- 20% 3 +/

- 20% 3 +/

- 20% *Note: Low S refers to low solar glazing. *Note: Number of doors refers to the number of doors externally exposed, and is translated into a wall area used by the doors

- 24 doors refers to the surface area used by 24 doors. Office accounts for door area in the window area.

### 136 

### Big box retail buildings were represented as a one

- story, six

- zone model as shown in Figure B. 10 , and were used to replace “larger” two

- phase loads and “smaller” three

- phase loads, as defined by the scaling factor. The overall square footage was defined as 20,000 sf., with a uniform deviation of 50%. Table B. 12 shows the key parameters u sed to define the retail big box building zones. Again, this building was considered a single occupant and customer billing was performed on the aggregate of all the “zones”.

Figure B. 10 : Retail “big box” zonal floor plan 

### A retail strip mall model was used to represent all other loads, including all one

- phase loads and “smaller” two

- or three

- phase loads. These were represented by one

- story, single

- zone models connected in series as shown in Figure B. 11 . Individual zones were defined as 1200 or 2400 sf., with a uniform deviation of 30%. Table B. 12 shows the key parameters used to define the retail strip mall building zones. In this case, ownership was considered on a per

- zone basis, so customer billing was also performed on a per
- zone basis.

### 137 Figure B. 11 : Retail strip mall zonal floor plan with N zones depending upon scaling factor



### Additionally, it was assumed that all commercial buildings had both heating and cooling systems and heating was al ways represented by a gas heating unit rather than a heat pump or resistive heat unit. Again, internal loads are very important drivers for both heating and cooling of the space, displacing heating load while adding cooling load. Commercial building load is highly occupant driven, and is typically very recurring. Data from end

- use metering projects was used to create average end

- use load shapes for weekdays and weekends [21]. Again, certain loads were slightly scaled up or down to reflect changes in eff iciencies or standard usage. Weekdays are assumed to be Mon

- Fri for office buildings, Mon.

- Sun. for big box buildings, and Mon.

- Sat. for strip malls. Average load shapes are shown in Figure B. 12 through Figure B. 15

. Notice that the y

- axis is in units of W/sf. The load shape applied to each zone is scaled as a function of square footage then randomized on a zonal basis by +/

- 20% over a uniform distribution. In addition to the magnitude randomization, the load shape was also randomly “skewed” in time. Each of the zones within the building were considered to be on the same schedule, however, across the population of buildings, not all started and ended at the same time. The load shapes were temporally shifted from those shown in Figure B. 12 through Figu re B. 15 in 30

- minute blocks using a normal distribution of average of 0 minutes and standard deviation of 30 minutes. This produced a more diversified load across the entire population.

### 138 Figure B. 12 : Average office end

- use load shape (weekday)

Figure B. 13 : Average office end

- use load shape (weekend)

### 139 Figure B. 14 : Average big box and strip mall end

- use load shape (weekday)

Figure B. 15 : Average big box and strip mall end

- use load shape (weekend)

### 140 

### Finally, there were a number of loads on the prototypical feeders that were far smaller than could be described by a building model at peak load, often less than 1 kVA. While there are a number of options for re presentation of these loads, such as traffic lights or a small espresso stand, it was determined that without data to indicate what these loads represented they would be best represented by street lighting loads. These small loads were converted to a sche duled one

- , two

- , or three

- phase load, depending on the original load and the full rated load was applied during dark hours and zero load was applied during daylight hours. While it is understood that this is not an accurate representation of true street light loading and operation, the loads were small enough and infrequent enough that a simple scheduled load had little to no effect on the overall operation of the feeder circuits.


## B.3 Taxonomy Feeder Emission Profiles

Increasing operational efficiency of the electrical power system can lead to a reduction in pollutant emissions. Peak load reduction or peak shifting has been shown to reduce emissions, mainly due to reducing the need to use “peaker” units. These are typ ically older, less efficient generators, designed for quick start

- up and shutdown, and are often single cycle natural gas turbine generators or petroleum fired plants. Reduction in overall energy consumption or shifting of production to more efficient ene rgy sources can also reduce emissions by reducing the amount of fuel burned for electricity production. Solutions for the amount of emissions created are traditionally performed at the transmission level, using optimal power flow and economic dispatch, an d are typically not well

- suited for distribution level simulation. The following section is a brief description of how GridLAB

- D estimates emissions impacts at the distribution level.

To capture the emissions level benefits to the system, generation mixes were assumed in each region and the nine most heavily consumed fuels for electrical generation in the U.S. were used. In each region, the fuels are dispatched in order from first to last by capacity factor, as shown in Table B. 13 . Exceptions are made fo r a number of the renewable resources, such as wind, solar, and biomass, as they are assumed to be dispatched when available. The level of penetration by each fuel type was determined for each region by month as shown in Table B. 14

- Table B. 18

. These values were determined from the EIA’s Annual Electric Generator Report [25] , utilizing state

- by

- state breakdowns of annual energy production.

### 141 Table B. 13 : Dispatch order of fuel by region 

### Region 1 2 3 4 5

Order of dispatch Nuclear Nuclear Nuclear Nuclear Nuclear Solar Solar Solar Solar Solar Biomass Biomass Biomass Biomass Biomass

Wind Wind Wind Wind Wind Hydroelectric Coal Coal Coal Natural Gas

Natural Gas Natural Gas Natural Gas Natural Gas Coal

Coal Hydroelectric Hydroelectric Hydroelectric Hydroelectric

Geothermal Geothermal Geothermal Geothermal Geothermal
Petroleum Petroleum Petroleum Petroleum Petroleum

Table B. 14 : Percent of energy consumed, broken down by fuel type and month in region 1 

### Region 1 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Nuclear 9.86 8.68 11.47 13.08 10.63 9.73 10.68 8.93 10.09 8.5 9.83 10.41 Solar 0.01 0.08 0.18 0.23 0.25 0.24 0.25 0.24 0.21 0.15 0.09 0.04 Biomass 0.58 0.78 0.77 0.72 0.73 0.73 0.67 0.65 0.72 0.82 0.81 0.73 Wind 2.37 1.86 4.39 4.57 4.63 5.44 4.07 4.66 3.55 3.64 3.17 1.44 Hydroelectric 43.43 37.29 38.84 49.88 56.78 58.39 36.88 29.63 26.32 31.09 36.02 36.29 Natural Gas 34.61 41.6 34.96 25.6 22.89 21.1 41.38 48.31 51.24 45.88 42.02 42.13 Coal 5.44 5.77 5.42 2.14 0.45 0.86 2.88 4.09 4.38 5.97 4 5.14 Geothermal 3.29 3.49 3.51 3.35 3.29 3.1 2.84 3.09 3.11 3.54 3.63 3.35 Petroleum 0.43 0.45 0.45 0.43 0.35 0.41 0.36 0.38 0.39 0.4 0.44 0.47

### 142 Table B. 15 : Percent of energy consumed, broken down by fuel type and month in region 2 

### Region 2 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Nuclear 26.47 26.9 27.74 25.27 28.52 27.95 26.33 24.75 27.04 25.09 25.63 25.42 Solar 0 0 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0 0 Biomass 0.64 0.72 0.82 0.9 0.92 0.84 0.82 0.76 0.83 0.85 0.89 0.75 Wind 2.23 2.71 2.9 3.34 2.79 1.7 1.41 1.6 1.73 2.82 3.22 2.99 Coal 49.62 49.36 46.7 46.31 44.39 45.54 47.18 46.33 46.05 49.04 49.05 50.69 Natural Gas 12.31 13.49 14.19 14.67 13.43 14.47 16.33 19.87 17.97 15.73 14.51 13.22 Hydroelectric 6.11 5.99 6.92 9.11 9.51 9.05 7.42 6.08 5.98 6.13 6.34 6.43 Geothermal 0.07 0.07 0.08 0.08 0.08 0.07 0.07 0.07 0.08 0.07 0.08 0.08 Petroleum 2.55 0.74 0.64 0.32 0.34 0.37 0.43 0.6 0.33 0.27 0.28 0.43

Table B. 16 : Percent of energy consumed, broken down by fuel type and month in region 3 

### Region 3 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Nuclear 9.82 8.88 10.24 11.6 10.83 9.72 8.65 8.5 7.13 8.62 9.63 9.38 Solar 0.01 0.05 0.13 0.16 0.17 0.13 0.13 0.14 0.13 0.1 0.06 0.03 Biomass 0.22 0.28 0.29 0.29 0.25 0.25 0.23 0.21 0.25 0.27 0.29 0.26 Wind 2.13 3.08 3.26 3.77 2.8 2.45 2.05 2.2 2.34 3.55 3.02 2.77 Coal 50.18 43.95 41.77 42.34 43.59 41.52 40.24 41.42 43.7 47.9 49.94 46.58 Natural Gas 32.79 37.12 37.34 33.17 33.92 37.88 41.67 41.48 40.32 33.07 31.29 34.43 Hydroelectric 2.89 4.75 4.95 6.72 6.68 6.4 5.58 4.59 4.47 4.74 3.76 4.6 Geothermal 1.63 1.62 1.7 1.67 1.53 1.4 1.25 1.26 1.42 1.52 1.79 1.7 Petroleum 0.32 0.26 0.32 0.28 0.24 0.25 0.2 0.2 0.22 0.24 0.22 0.24

Table B. 17 : Percent of energy consumed, broken down by fuel type and month in region 4 

### Region 4 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Nuclear 23.16 23.97 23.95 24.4 24.92 22.45 23.15 21.91 23.58 24.33 23.99 22.77 Solar 0 0 0 0 0 0 0 0 0 0 0 0 Biomass 0.21 0.19 0.21 0.25 0.21 0.18 0.18 0.18 0.21 0.22 0.22 0.18 Wind 0.69 0.88 1.03 1.16 0.78 0.64 0.53 0.6 0.59 1.13 1.18 1.04 Coal 61.55 60.14 57.45 58.24 57.41 56.92 56.89 57.14 56.06 58.36 58.48 59.96 Natural Gas 9.98 11.44 12.86 11.25 11.38 16.04 16.75 17.49 16.14 10.51 9.83 10.19 Hydroelectric 3.37 2.67 3.71 4.21 4.73 3.32 2.05 2.2 3.09 5.09 5.96 5.51 Geothermal 0 0 0 0 0 0 0 0 0 0 0 0 Petroleum 1.04 0.71 0.8 0.49 0.56 0.45 0.45 0.48 0.36 0.36 0.34 0.36

### 143 Table B. 18 : Percent of energy consumed, broken down by fuel type and month in region 5 

### Region 5 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Nuclear 18.26 18.55 18.53 17.36 14.67 13.53 13.74 13.85 13.65 12.7 14.94 16.41 Solar 0 0 0 0 0 0 0 0 0 0 0 0 Biomass 0.46 0.45 0.48 0.46 0.3 0.31 0.31 0.33 0.34 0.39 0.46 0.46 Wind 2.14 2.6 2.7 2.95 1.91 1.74 1.44 1.48 1.43 2.52 2.63 2.26 Natural Gas 38.8 41.01 45.26 44.78 47.26 51.29 51.75 51.68 51.03 47.55 43.83 41.73 Coal 37.3 34.53 29.66 30.82 32.04 30.37 30.38 30.17 30.72 33.46 35.06 35.97 Hydroelectric 1.42 0.86 1.57 1.51 1.61 0.78 0.58 0.63 0.99 1.75 2.12 2.35 Geothermal 0 0 0 0 0 0 0 0 0 0 0 0 Petroleum 1.62 2 1.79 2.12 2.2 1.96 1.8 1.86 1.84 1.62 0.95 0.82



### At each 15

- minute measurement interval, the energy consumed over the previous interval is used to determine the amount of energy delivered by each fuel source. The peak load of the base case for each month is used to scale the percentages. Figure B. 16 sh ows an example of how this is performed in GridLAB

- D using June in Region 3. It can be seen that the peak load for that month would utilize all the generation fuels at the levels shown in Table B. 16 . At the shown 15

- minute period, the base case load is a pproximately 95% of the peak for June for this particular feeder. During the same 15

- minute period, the representative technology case is only 87% of the base case peak feeder loading. This results in a reduction of generation by approximately 3% for hyd roelectric and 5% for natural gas. This calculation is performed at every 15

- minute interval to determine the energy consumed by each fuel type over the course of the entire annual simulation of 1

- minute intervals

.

### 144 Figure B. 16 : 15

- minute interval comparing fuel dispatch for the peak load versus the base case load versus a technology modified load. 

### Assumed average thermal efficiencies are then used to convert the energy delivered to the amount of fuel used, where applicable. The values used are shown in Table B. 19 . Finally, assumed average values for conversion efficiencies are used to convert from fuel used to emissions levels for carbon dioxide, sulfur dioxide, and nitrous oxides. The conversion values assumed are shown in Ta ble B. 20 . These values are not indicative of any single plant, but rather broad averages across the U.S. While this is a very simplified means of dispatching and assigning generation, ignoring complex issues such as inefficiencies due to warm

- up cycles, maintenance periods, and economic or optimal dispatching, it should provide a general indication of how changes in operation of a distribution circuit can reduce pollutant emissions.

### 145 Table B. 19 : Average thermal efficiencies by fuel type.


### MBTUs / MWh Nuclear 10.46 Solar N/A Biomass 12.93 Wind N/A Natural Gas 8.16 Coal 10.41 Hydroelectric N/A Geothermal 21.02 Petroleum 11

Table B. 20 : Pollutant production per BTU of fuel (lbs./MBTU)


### CO 2 

### SO 2 

### NO x 

### PM

- 10 Nuclear 0 0 0 0.017157 Solar 0 0 0 0.03 Biomass 195 0 0.08 0.0232 Wind 0 0 0 0 Natural Gas 117.08 0.001 0.0075 0 Coal 205.57 0.1 0.06 0 Hydroelectric 0 0 0 0 Geothermal 120 0.2 0 0 Petroleum 225.13 0.1 0.04 0



## B.4 Taxonomy Feeder Descriptions

The previous sections have described the details of how each of the prototypical feeders is populated with end

- use loads. This section is a reproduction of the individual prototypical feeder descriptions from [3] which describ es the characteristics of the primary distribution system.


## B.4.1 Feeder 1: GC

- 12.47

- 1

This feeder is representative of a single large commercial or industrial load, such as a very large shopping mall or a small lumber mill. These feeders may supply the load through a single large transformer or a group of smaller units. While there may be a couple of smaller loads the behavior of the feeder is primarily determined by the single large customer. This is a 12.47 kV feeder with a peak load of approximately 5,40 0 kVA.

### 146 

## B.4.2 Feeder 2: R1

- 12.47

- 1

This feeder is a representation of a moderately populated suburban and rural area. This is composed mainly of single family residences with small amounts of light commercial. Approximately 60% of the circuit

- feet are overhead and 40% are underground. It would be expected that this feeder is connected to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 60% to ensure the ability to transfer load from other feeder s, and vice versa. The majority of the load is located relatively near the substation. This is a 12.5 kV feeder with a peak load of approximately 4,300 kVA.


## B.4.3 Feeder 3: R1

- 12.47

- 2

This feeder is a representation of a moderately populated suburban and lig htly populated rural area. This is composed mainly of single family residences with small amounts of light commercial. Approximately 70% of the circuit

- feet are overhead and 30% underground. It would not be expected that this feeder is connected to adja cent feeders through normally open switches. Even though there are not adjacent feeders for transferring the load, the total feeder loading is low because of the sparse rural loading. In this model an urban substation is feeding a rural load through a lo ng primary circuit. The majority of the load is located relatively distant with respect to the substation. This is a 12.47 kV feeder with a peak load of approximately 2,400 kVA.


## B.4.4 Feeder 4: R1

- 12.47

- 3

This feeder is a representation of a moderately populat ed urban area. This is composed mainly of mid

- sized commercial loads with some residences, mostly multi

- family. Approximately 85% of the circuit

- feet are overhead and 15% underground. It would be expected that this feeder is connected to adjacent feeder s through normally open switches. For this reason it would be common to limit the feeder loading to 60% to ensure the ability to transfer load from other feeders, and vice versa. Since this is a small urban core the loading of the feeder is well below 60 %. The majority of the load is located relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 1,800 kVA.


## B.4.5 Feeder 5: R1

- 12.47

- 4

This feeder is a representation of a heavily populated suburban area. This is composed m ainly of single family homes and heavy commercial loads. None of the circuit

- feet are overhead and 100% are underground. It would be expected that this feeder is connected to adjacent feeders through normally open switches. The majority of the load is l ocated relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 4,900 kVA.

### 147 

## B.4.6 Feeder 6: R1

- 25.00

- 1

This feeder is a representation of a lightly populated rural area. This is composed of a mixture of residential, light c ommercial, industrial, and agricultural loads. Approximately 60% of the circuit

- feet are overhead and 40% underground. It would be expected that this feeder is not connected to adjacent feeders through normally open switches. Due to rural location and l ow population density the feeder is not heavily loaded. The low population density and wide are covered are why this feeder is operated at 24.9 kV. The majority of the load is located relatively distant with respect to the substation. This is a 24.9 kV feeder with a peak load of approximately 2,300 kVA.


## B.4.7 Feeder 7: R2

- 12.47

- 1

This feeder is a representation of a lightly populated urban area. This is composed of single family homes, moderate commercial loads, light industrial loads, and some agricultural loads. This feeder supplies a college and an airport. Approximately 25 % of the circuit

- feet are overhead and 75 % underground . It would be expected that this feeder is connected to adjacent feeders through normally open switches. For this reason it wou ld be common to limit the feeder loading to 60% to ensure the ability to transfer load from other feeders, and vice versa. The majority of the load is located relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 6,7 00 kVA.


## B.4.8 Feeder 8: R2

- 12.47

- 2

This feeder is a representation of a moderately populated suburban area. This is composed mainly of single family homes with some light commercial loads. Approximately 80 % of the circuit

- feet are overhead and 20 % undergroun d . It would be expected that this feeder is connected to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 60% to ensure the ability to transfer load from other feeders, and vice versa. Th e majority of the load is located relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 6,700 kVA.


## B.4.9 Feeder 9: R2

- 12.47

- 3

This feeder is a representation of a lightly populated suburban area. This is composed of sing le family homes, light commercial loads, light industrial loads, and some agricultural loads. Approximately 20 % of the circuit

- feet are overhead and 80 % underground . It would be expected that this feeder is connected to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 60% to ensure the ability to transfer load from other feeders, and vice versa. The majority of the load is located relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 4,800 kVA.

### 148 

## B.4.10 Feeder 10: R2

- 25.00

- 1

This feeder is a representation of a moderately populated suburban area. This is composed mainly of single family homes with some light and moderate commercial loads. Approximately 60 % of the circuit

- feet are overhead and 40 % underground . It would be expected that this feeder is connected to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 60% to ensure the ability to t ransfer load from other feeders, and vice versa. This is a heavily loaded feeder, well over 60%, with the majority of the load is located relatively near the substation. This is a 24.9 kV feeder with a peak load of approximately 21,300 kVA.


## B.4.11 Feeder 11: R2

- 35.00

- 1

This feeder is a representation of a lightly populated rural area. This is composed mainly of single family homes with some light and moderate commercial loads. Approximately 90 % of the circuit

- feet are overhead and 10 % underground . It would be expected that this feeder is connected to adjacent feeders through normally open switches. But due to the long distances significant portions of the load cannot be shifted to adjacent feeders. In this model a single substation is serving a large geogr aphic area, this is the reason for the higher voltage level; voltage regulators are used on this system. The majority of the load is located relatively distant with respect to the substation. This is a 34.5 kV feeder with a peak load of approximately 6,9 00 kVA.


## B.4.12 Feeder 12: R3

- 12.47

- 1

This feeder is a representation of a heavily populated urban area. This is composed of single family homes, heavy commercial loads, and a small amount of light industrial loads. Approximately 25 % of the circuit

- feet are ov erhead and 75 % underground . It would be expected that this feeder is connected to adjacent feeders through normally open switches. Due to the heavy commercial loads it would be expected that this feeder would be loaded to a high percentage of its rating. The majority of the load is located relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 11,600 kVA.


## B.4.13 Feeder 13: R3

- 12.47

- 2

This feeder is a representation of a moderately populated urban area. This is composed of single family homes, light commercial loads, and a small amount of light industrial loads. Approximately 33 % of the circuit

- feet are overhead and 67 % underground . It would be expected that this feeder is connected to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 60% to ensure the ability to transfer load from other feeders, and vice versa. The majority of the load is located relatively near the substation. This is a 12.47 kV feeder with a peak load of approximately 4,000 kVA.

### 149 

## B.4.14 Feeder 14: R3

- 12.47

- 3

This feeder is a representation of a heavily populated suburban area. This is composed mainly of single family homes with some light agricultural loads. Approximately 75 % of the circuit

- feet are overhead and 25 % underground . It would be expected that this feeder has limited connections to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 75% to ensure the ability t o transfer some loads from other feeders, and vice versa. Due to the low density of suburban loads the majority of the load is located relatively distant with respect to the substation. This is a 12.45 kV feeder with a peak load of approximately 9,400 kV A.


## B.4.15 Feeder 15: R4

- 12.47

- 1

This feeder is a representation of a heavily populated urban area with the primary feeder extending into a lightly populated rural area. In the urban areas the load is composed of moderate commercial loads with single and multi

- family residences. On the rural spur the load is primarily single family residences. Approximately 92 % of the circuit

- feet are overhead and 8 % underground . This feeder has connections to adjacent feeders in the urban area, but limited connections in the rural areas. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most of the loads from other feeders, and vice versa. Most of the urban load is located near the substation while the rural load is loca ted at a substantial distance. This is a 13.8 kV feeder with a peak load of approximately 6,700 kVA.


## B.4.16 Feeder 16: R4

- 12.47

- 2

This feeder is a representation of a lightly populated suburban area with a moderately populated urban area. The lightly populate d suburban area is composed mostly of single family residences. The commercial complex is a single facility. Approximately 92 % of the circuit

- feet are overhead and 8 % underground . This feeder has connections to adjacent feeders in the commercial complex , but limited connections in the rural areas. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most of the loads from other feeders, and vice versa. Most of the suburban load is located near the sub station while the commercial load is located at a substantial distance. This is a 12.5 kV feeder with a peak load of approximately 2,100 kVA.


## B.4.17 Feeder 17: R4

- 25.00

- 1

This feeder is a representation of a lightly populated rural area. The load is composed of single family residences with some light commercial. Approximately 88 % of the circuit

- feet are overhead and 12 % underground . This feeder has connections to adjacent feeders. This combined with the low load density ensures the ability to transfer most of the loads from other feeders, and vice versa. Most of the load is located at a substantial distance from the substation,

### 150 

### as is common for higher voltages in rural areas. This is a 24.9 kV feeder with a peak load of approximately 1,000 kVA.


## B.4.18 Feeder 1 8: R5

- 12.47

- 1

This feeder is a representation of a heavily populated suburban area and a moderate urban center. This is composed mainly of single family homes and moderate commercial loads. Approximately 95 % of the circuit

- feet are overhead and 5 % underg round . It would be expected that this feeder has connections to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most loads from other feeders, and vi ce versa. The suburban load is near the substation while the commercial load is at the end of the feeder. This is a 13.8 kV feeder with a peak load of approximately 10,800 kVA.


## B.4.19 Feeder 19: R5

- 12.47

- 2

This feeder is a representation of a moderate suburba n area with a heavy urban area. This is composed mainly of heavy commercial and single family residences. Approximately 38 % of the circuit

- feet are overhead and 62 % underground . It would be expected that this feeder has connections to adjacent feeders t hrough normally open switches. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most loads from other feeders, and vice versa. The heavy commercial load is near the substation while the single family residences are at the end of the feeder. This is a 12.47 kV feeder with a peak load of approximately 4,200 kVA.


## B.4.20 Feeder 20: R5

- 12.47

- 3

This feeder is a representation of a moderately populated rural area. This is composed mainly of single family reside nces with some light commercial. Approximately 92 % of the circuit

- feet are overhead and 8 % underground . It would be expected that this feeder has limited connections to adjacent feeders through normally open switches. Due to the low load density of the large rural area the feeder is less than 50% loaded. The majority of the load is located relatively distant with respect to the substation. Voltage regulators are used on this feeder. This is a 13.8 kV feeder with a peak load of approximately 4,800 kVA.


## B.4.21 Feeder 21: R5

- 12.47

- 4

This feeder is a representation of a moderately populated suburban and urban area. This is composed mainly of single family residences with some moderate commercial loads. Approximately 37 % of the circuit

- feet are overhead and 63 % underground . It would be expected that this feeder has connections to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most of the loads from other feeders, and vice versa. Most of the commercial load is near the

### 151 

### substation and the residential load is spread out along the length of the entire feeder. This is a 12.47 kV feeder with a peak load of approximately 6,200 kVA.


## B.4.22 Feeder 22: R5

- 12.47

- 5

This feeder is a representation of a moderately populated suburban area with a lightly populated urban area. This is composed mainly of single family residences with some light commercial loads. Approximately 48 % of the circuit

- feet are overhead and 52 % unde rground

. It would be expected that this feeder has connections to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most of the loads from other feede rs, and vice versa. The residential load is spread out across the entire length of the feeder. The primary feeder extends a significant distance before there is any significant load, an express configuration. This is a configuration that can be seen in a well

- established area when a new feeder must be routed through an existing area in order to reach areas of new load growth. This is a 12.47 kV feeder with a peak load of approximately 8,500 kVA.


## B.4.23 Feeder 23: R5

- 25.00

- 1

This feeder is a representation of a heavily populated suburban area with a moderately populated urban area. This is composed mainly of single family residences with some moderate commercial loads. Approximately 35 % of the circuit

- feet are overhead and 65 % underground

. It would be expec ted that this feeder has connections to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 66% to ensure the ability to transfer most of the loads from other feeders, and vice versa. The re sidential load is spread out across the entire length of the feeder with the moderate commercial center near the substation. This is a 22.9 kV feeder with a peak load of approximately 9,300 kVA.


## B.4.24 Feeder 24: R5

- 35.00

- 1

This feeder is a representation of a moderately populated suburban area with a lightly populated urban area. This is composed mainly of single family residences with some moderate commercial loads. Approximately 10 % of the circuit

- feet are overhead and 90 % underground

. It would be expecte d that this feeder has connections to adjacent feeders through normally open switches. For this reason it would be common to limit the feeder loading to 50% to ensure the ability to transfer most of the loads from other feeders, and vice versa. The resi dential load is spread out across the entire length of the feeder with the moderate commercial center near the substation. This feeder is representative of a substation that is built in a “green field” where significant load growth is expected. The first feeders must go a significant distance before they reach the load, over time the load moves towards the substation and past it. This is a 34.5 kV feeder with a peak load of approximately 12,100 kVA.

### 152 

# Appendix C: Simulation Technology and Methodology

Simulations 

### of the different project technologies and programs were accomplished using the GridLAB

- D software. GridLAB

- D provides an agent

- based multi

- disciplinary environment for the examination and evaluation of emerging technologies. By providing a mu lti

- disciplinary simulation environment , it is possible to bring together diverse teams of experts from multiple fields of study to holisti cally examine complex systems.

GridLAB

- D has been developed through funding from the Department of Energy, Office of Electricity. Through $5.5 million of direct funding and supporting projects from DOE

- OE, GridLAB

- D has developed significant capabilities for analyzing smart grid deployments. The capabilities center on the functionality needed to simulate a distribution feeder power flow and attached loads. The development has included: unbalanced three

- phase power flow solvers; detailed end

- use models, particularly of a residential home’s thermal integrity, HVAC cycles and water heater cycles; and a transactive market that supports double auction bidding. Different combinations of these capabilities enabled simulations of the various technologies and programs evaluated in th 0 is report.

GridLAB

- D conducts time

- series simulations with variable time steps. The solution at each time step is a quasi

- steady state solution for each of the modules. Convergence is achieved within each module and convergence across modules is coordinated via the GridLAB core as illustrated in Figure C. 1

.

Figure C. 1 : GridLAB

- D architecture

### 153 

### Time steps are also coordinated by the GridLAB

- D core. This is necessary because the various modules in the simulation will generally have different time step requirements. At the end of a time step, every object in the model returns a ‘sync’ time that indicates how long the object will remain constant without outside influence. The GridLAB core then examines every object and determines what the smallest sync time is; this then becomes length of the next step. This process i s performed at every time step so that the system has a variable step size. For a given state variable an example of the variable step sizes are shown in Figure C. 2

.

Figure C. 2 : Variable step sizes in GridLAB

- D simulation



### Whe n analyzing operations at the distribution level, the major dynamics of interest are mid

- term and occur on the order of minutes to hours. For the purposes of this analysis, a minimum time step of one minute was enforced. For operations that occur at inte rvals of less than one minute, such as a 45

- second delay on a voltage regulator, the operation is aggregated up to the one minute time step; multiple operations cannot occur during the enforced minimum of one minute. Because of the large number of objects and the forced minimum, the simulation proceeded at one

- minute time steps for the majority of the simulations. As a result, there are approximately 500,000 time steps in an annual simulation of a single prototypical feeder.

Since the simulations for the SGIG analysis are being conducted over a one year period the minimum step size has been set to one minute. Even with a minimum one minute step size there is the possibility of 525,600 time steps in a single simulation. If a one second minimum step size w ere used there would be no significant increase in accuracy because most of the dynamic behavior has a time constant greater than one minute. Additionally, the number of time steps would increase by a factor of sixty resulting in significantly more comput ing time.

### 154

### 155 

# Appendix D: Plots for Individual Feeder Results This appendix contains the individual plots for each of the prototypical feeds for each technology, where necessary. Depending on the technology, different values will be plotted, consistent with those shown in Section 3

. 

## D.1 Addition of Residential PVs plots

Consistent with the plots shown in Section 3 .1.1, peak monthly demand, monthly energy consumption, monthly PV output and monthly CO 2 

### emi ssions plot ‘Base Case’ and ‘ResPV ’. Mont hly losses plots “Base’ and ‘ResPV ’ for 4 different loss types; losses in overhead lines ‘OHL’, underground lines ‘UGL’, transformers ‘TFR’, and tripl ex lines ‘TPL’ are shown in this section.


## D.1.1 Detailed Residential PV Plots for R1

- 12.47

- 1

The plots for this feeder were already presented in Section 3 .1.1.


## D.1.2 Detailed Residential PV Plots for R1

- 12.47

- 2

Figure D. 1 : Comparison of peak load by month for R1

- 12.47

- 2

### 156 Figure D. 2 : Comparison of energy consumption by month for R1

- 12.47

- 2

Figure D. 3 : Comparison of losses by month for R1

- 12.47

- 2

### 157 Figure D. 4 : Comparison of PV output by month for R1

- 12.47

- 2

Figure D. 5 : Comparison of CO 2 emissions by month for R1

- 12.47

- 2

### 158 

## D.1.3 Detailed Residential PV Plots f or R1

- 12.47

- 3

Figure D. 6 : Comparison of peak load by month for R1

- 12.47

- 3

Figure D. 7 : Comparison of energy consumption by month for R1

- 12.47

- 3

### 159 Figure D. 8 : Comparison of losses by month for R1

- 12.47

- 3

Figure D. 9 : Comparison of PV output by month for R1

- 12.47

- 3

### 160 Figure D. 10 : Comparison of CO 2 emission s by month for R1

- 12.47

- 3


## D.1.4 Detailed Residential PV Plots for R1

- 12.47

- 4

Figure D. 11 : Comparison of peak load by month for R1

- 12.47

### 161 Figure D. 12 : Comparison of energy consumption by month for R1

- 12.47

- 4

Figure D. 13 : Comparison of losses by month for R1

- 12.47

- 4

### 162 Figure D. 14 : PV output by month for R1

- 12.47

- 4

Figure D. 15 : Comparison of CO 2 emissions by month for R1

- 12.47

- 4

### 163 

## D.1.5 Detailed Residential PV Plots for R1

- 25.00

- 1

Figure D. 16 : Comparison of peak load by month for R1

- 25.00

- 1

Figure D. 17 : Comparison of energy consumption by month for R1

- 25.00

- 1

### 164 Figure D. 18 : Comparison of losses by month for R1

- 25.00

- 1

Figure D. 19 : PV output by month for R1

- 25.00

- 1

### 165 Figure D. 20 : Comparison of CO 2 emission s by month for R1

- 25.00

- 1 

## D.1.6 Detailed Residential PV Plots for R2

- 12

. 47

- 1

Figure D. 21 : Comparison of peak load by month for R2

- 12.47

- 1

### 166 Figure D. 22 : Comparison of energy consumption by month for R2

- 12.47

- 1

Figure D. 23 : Comparison of losses by month for R2

- 12.47

- 1

### 167 Figure D. 24 : PV output by month for R2

- 12.47

- 1

Figure D. 25 : Comparison of CO 2 emissions by month for R2

- 12.47

- 1

### 168 

## D.1.7 Detailed Residential PV Plots for R2

- 12.47

- 2

Figure D. 26 : Comparison of peak load by month for R2

- 12.47

- 2

Figure D. 27 : Comparison of energy consumption by month for R2

- 12.47

- 2

### 169 Figure D. 28 : Comparison of losses by month for R2

- 12.47

- 2

Figure D. 29 : PV output by month for R2

- 12.47

- 2

### 170 Figure D. 30 : Comparison of CO 2 emission s by month for R2

- 12.47

- 2


## D.1.8 Detailed Residential PV Plots for R2

- 12.47

- 3

Figure D. 31 : Comparison of peak load by month for R2

- 12.47

- 3

### 171 Figure D. 32 : Comparison of energy consumption by month for R2

- 12.47

- 3

Figure D. 33 : Comparison of losses by month for R2

- 12.47

- 3

### 172 Figure D. 34 : PV output by month for R2

- 12.47

- 1

Figure D. 35 : Comparison of CO 2 emissions by month for R2

- 12.47

- 3

### 173 

## D.1.9 Detailed Residential PV Plots for R2

- 25.00

- 1

Figure D. 36 : Comparison of peak load by month for R2

- 25.00

- 1

Figure D. 37 : Comparison of energy consumption by month for R2

- 25.00

- 1

### 174 Figure D. 38 : Comparison of losses by month for R2

- 25.00

- 1

Figure D. 39 : PV output by month for R2

- 25

. 00

- 1

### 175 Figure D. 40 : Comparison of CO 2 emissions by month for R2

- 25.00

- 1 

## D.1.10 Detailed Residential PV Plots for R2

- 35.00

- 1

Figure D. 41 : Comparison of peak load by month for R2

- 35.00

- 1

### 176 Figure D. 42 : Comparison of energy consumption by month for R2

- 35.00

- 1

Figure D. 43 : Comparison of losses by month for R2

- 35.00

- 1

### 177 Figure D. 44 : PV output by month for R2

- 35.00

- 1

Figure D. 45 : Comparison of CO 2 emissions by month for R2

- 35.00

- 1

### 178 

## D.1.11 Detailed Residential PV Plots for R3

- 12.47

- 1

Figure D. 46 : Comparison of peak load by month for R 3

- 12.47

- 1

### 179 Figure D. 47 : Comparison of energy consumption by month for R3

- 12.47

- 1

Figure D. 48 : Comparison of losses by month for R3

- 12.47

- 1

### 180 Figure D. 49 : PV output by month for R3

- 12.47

- 1

Figure D. 50 : Comparison of CO 2 emissions by month for R3

- 12.47

- 1

### 181 

## D.1.12 Detailed Residential PV Plots for R3

- 12.47

- 3

Figure D. 51 : Comparison of peak load by month for R3

- 12.47

- 3

Figure D. 52 : Comparison of energy consumption by month for R3

- 12.47

- 3

### 182 Figure D. 53 : Comparison of losses by month for R3

- 12.47

- 3

Figure D. 54 : PV output by month for R3

- 12.47

- 3

### 183 Figure D. 55 : Comparison of CO 2 emissions by month for R3

- 12.47

- 3



## D.1.13 Detailed Residential PV Plots for R4

- 12.47

- 1

Figure D. 56 : Comparison of peak load by month for R4

- 12.47

- 1

### 184 Figure D. 57 : Comparison of energy consum ption by month for R4

- 12.47

- 1

Figure D. 58 : Comparison of losses by month for R4

- 12.47

- 1

### 185 Figure D. 59 : PV output by month for R4

- 12.47

- 1

Figure D. 60 : Comparison of CO 2 emissions by month for R4

- 12.47

- 1

### 186 

## D.1.14 Detailed Residential PV Plots for R4

- 12.47

- 2

Figure D. 61 : Comparison of peak load by month for R4

- 12.47

- 2

Figure D. 62 : Comparison of energy consumption by month for R4

- 12.47

- 2

### 187 Figure D. 63 : Comparison of losses by month for R4

- 12.47

- 2

Figure D. 64 : PV output by month for R4

- 12.47

- 2

### 188 Figure D. 65 : Comparison of CO 2 emissions by month for R3

- 12.47

- 1



## D.1.15 Detailed Residential PV Plots for R4

- 25.00

- 1

Figure D. 66 : Comparison of peak load by month for R4

- 25.00

- 1

### 189 Figure D. 67 : Comparison of energy consumption by month for R 4

- 25.00

- 1

Figure D. 68 : Comparison of losses by month for R4

- 25.00

- 1

### 190 Figure D. 69 : PV output by month for R4

- 25.00

- 1

Figure D. 70 : Comparison of CO 2 emissions by month for R4

- 25.00

- 1

### 191 

## D.1.16 Detailed Residential PV Plots for R5

- 12.47

- 1

Figure D. 71 : Comparison of peak load by month for R5

- 12.47

- 1

### 192 Figure D. 72 : Comparison of energy consumption by month for R5

- 12.47

- 1

Figure D. 73 : Comparison of losses by month for R5

- 12.47

- 1

### 193 Figure D. 74 : PV output by month for R5

- 12.47

- 1

Figure D. 75 : Comparison of CO 2 emissions by month for R5

- 12.47

- 1

### 194 

## D.1.17 Detailed Residential PV Plots for R5

- 12.47

- 2

Figure D. 76 : Comparison of peak load by month for R5

- 12.47

- 2

Figure D. 77 : Comparison of energy consumption by month for R5

- 12.47

- 2

### 195 Figure D. 78 : Comparison of losses by month for R5

- 12.47

- 2

Figure D. 79 : PV output by month for R5

- 12.47

- 2

### 196 Figure D. 80 : Comparison of CO 2 emissions by month for R5

- 12.47

- 2 

## D.1.18 Detailed Residential PV Plots for R5

- 12.47

- 3

Figure D. 81 : Comparison of peak load by month for R5

- 12.47

- 3

### 19 7 Figure D. 82 : Comparison of energy consumption by month for R5

- 12.47

- 3

Figure D. 83 : Comparison of losses by month for R5

- 12.47

- 3

### 198 Figure D. 84 : PV output by month for R5

- 12.47

- 3

Figure D. 85 : Comparison of CO 2 emissions by month for R5

- 12.47

- 3

### 199 

## D.1.19 Detailed Residential PV Plots for R5

- 12.47

- 4

Figure D. 86 : Comparison of peak load by month for R5

- 12.47

- 4

Figure D. 87 : Comparison of energy consumption by month for R5

- 12.47

- 4

### 200 Figure D. 88 : Comparison of losses by month for R5

- 12.47

- 4

Figure D. 89 : PV output by month for R5

- 12.47

- 4

### 201 Figure D. 90 : Comparison of CO 2 emissions by month for R5

- 12.47

- 4 

## D.1.20 Detailed Residential PV Plots for R5

- 12.47

- 5

Figure D. 91 : Comparison of peak load by month for R5

- 12.47

- 5

### 202 Figure D. 92 : Comparison of energy consumption by month for R5

- 12.47

- 5

Figure D. 93 : Comparison of losses by month for R5

- 12.47

- 5

### 203 Figure D. 94 : PV output by month for R5

- 12.47

- 5

Figure D. 95 : Comparison of CO 2 emissions by month for R5

- 12.47

- 5

### 204 

## D.1.21 Detailed Residential PV Plots for R5

- 25.00

- 1

Figure D. 96 : Comparison of peak load by month for R5

- 25.00

- 1

Figure D. 97 : Comparison of energy consumption by month for R5

- 25.00

- 1

### 205 Figure D. 98 : Comparison of losses by month for R5

- 25.00

- 1

Figure D. 99 : PV output by month for R5

- 25.00

- 1

### 206 Figure D. 100 : Comparison of CO 2 emissions by month for R5

- 25.00

- 1


## D.1.22 Detailed Residential PV Plots for R5

- 35.00

- 1

Figure D. 101 : Comparison of peak load by month for R5

- 35.00

- 1

### 207 Figure D. 102 : Comparison of energy consumption by month for R5

- 35.00

- 1

Figure D. 103 : Comparison of losses by month for R5

- 35.00

- 1

### 208 Figure D. 104 : PV output by month for R5

- 35.00

- 1

Figure D. 105 : Comparison of CO 2 emissions by month for R5

- 35.00

- 1

### 209 

## D.2 Addition of Commercial PV Plots

D.2.1 Detailed Commercial PV Plots for GC

- 12.47

- 1_R1

The plots for this feeder were already presented in Section 3 .2.1.


## D.2.2 Detailed Commercial PV Plots for R1

- 12.47

- 1

Figure D. 106 : Comparison of peak load by month for R1

- 12.47

- 1

### 210 Figure D. 107 : Comparison of energy consumption by month for R1

- 12.47

- 1

Figure D. 108 : Comparison of losses by month for R1

- 12.47

- 1

### 211 Figure D. 109 : PV output by month for R1

- 12.47

- 1

Figure D. 110 : Comparison of CO 2 by month for R1

- 12.47

- 1

### 212 

## D.2.3 Detailed Commercial PV Plots for R1

- 12.47

- 2

Figure D. 111 : Comparison of peak load by month for R1

- 12.47

- 2

Figure D. 112 : Comparison of energy consumption by month for R1

- 12.47

- 2

### 213 Figure D. 113 : Comparison of losses by month for R1

- 12.47

- 2

Figure D. 114 : PV output by month for R1

- 12.47

- 2

### 214 Figure D. 115 : Comparison of CO 2 by month for R1

- 12.47

- 2


## D.2.4 Detailed Commercial PV Plots for R1

- 12.47

- 3

Figure D. 116 : Comparison of peak load by month for R1

- 12.47

- 3

### 215 Figure D. 117 : Comparison of energy consumption by month for R1

- 12.47

- 3

Figure D. 118 : Comparison of losses by month for R1

- 12.47

- 3

### 216 Figure D. 119 : PV output by month for R1

- 12.47

- 3

Figure D. 120 : Comparison of CO 2 by month for R1

- 12.47

- 3

### 217 

## D.2.5 Detailed Commercial PV Plots for R1

- 12.47

- 4

Figure D. 121 : Comparison of peak load by month for R1

- 12.47

- 4

Figure D. 12 2 : Comparison of energy consumption by month for R1

- 12.47

- 4

### 218 Figure D. 123 : Comparison of losses by month for R1

- 12.47

- 4

Figure D. 124 : PV output by month for R1

- 12.47

- 4

### 219 Figure D. 125 : Comparison of CO 2 by month for R1

- 12.47

- 4 

## D.2.6 Detailed Commercial PV Plots for R1

- 25.00

- 1

Figure D. 126 : Comparison of peak load by month for R1

- 25.00

- 1

### 220 Figure D. 127 : Comparison of energy consumption by month for R1

- 25.00

- 1

Figure D. 128 : Comparison of losses by month for R1

- 25.00

- 1

### 221 Figure D. 129 : PV output by month for R1

- 25.00

- 1

Figure D. 130 : Comparison of CO 2 by month for R1

- 25.00

- 1

### 222 

## D.2.7 Detailed Commercial PV Plots for GC

- 12.47

- 1_R2

Figure D. 131 : Comparison of peak load by month for GC

- 12.47

- 1_R2

Figure D. 132 : Comparison of energy consumption by month for GC

- 12.47

- 1_R2

### 223 Figure D. 133 : Comparison of losses by month for GC

- 12.47

- 1_R2

Figure D. 134 : PV output by month for GC

- 12.47

- 1_R2

### 224 Figure D. 135 : Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R2


## D.2.8 Detailed Commercial PV for R2

- 12.47

- 1

Figure D. 136 : Comparison of peak load by month for R2

- 12.47

- 1

### 225 Figure D. 137 : Comparison of energy consumption by month for R2

- 12.47

- 1

Figure D. 138 : Comparison of losses by month for R2

- 12.47

- 1

### 226 Figure D. 139 : PV output by month for R2

- 12.47

- 1

Figure D. 140 : Comparison of CO 2 emissions by month for R2

- 12.47

- 1

### 227 

## D.2.9 Detailed Commercial PV for R2

- 12.47

- 2

Figure D. 141 : Comparison of peak load by month for R2

- 1 2.47

- 2

Figure D. 142 : Comparison of energy consumption by month for R2

- 12.47

- 2

### 228 Figure D. 143 : Comparison of losses by month for R2

- 12.47

- 2

Figure D. 144 : PV output by month for R2

- 12.47

- 2

### 229 Figure D. 145 : Comparison of CO 2 emissions by month for R2

- 12.47

- 2


## D.2.10 Detailed Commercial PV for R2

- 25.00

- 1

Figure D. 146 : Comparison of peak load by month for R2

- 25.00

- 1

### 230 Figure D. 147 : Comparison of energy consumption by month for R2

- 25.00

- 1

Figure D. 148 : Comparison of losses by month for R2

- 25.00

- 1

### 231 Figure D. 149 : PV output by month for R2

- 25.00

- 1

Figure D. 150 : Comparison of CO 2 by month for R2

- 25.00

- 1

### 232 

## D.2.11 Detailed Commercial PV for R2

- 35.00

- 1

Figure D. 151 : Comparison of peak load by month for R2

- 35.00

- 1

Figure D. 152 : Comparison of energy consumption by month for R2

- 35.00

- 1

### 233 Figure D. 153 : Comparison of losses by month for R2

- 35.00

- 1

Figure D. 154 : PV output by month for R2

- 35.00

- 1

### 234 Figure D. 155 : Comparison of CO 2 emissions by month for R2

- 35.00

- 1


## D.2.12 Detailed Commercial PV for GC

- 12.47

- 1_R3

Figure D. 156 : Comparison of peak load by month for GC

- 12.47

- 1_R3

### 235 Figure D. 157 : Comparison of energy consumption by month for GC

- 12.47

- 1_R3

Figure D. 158 : Comparison of losses by month for GC

- 12.47

- 1_R3

### 236 Figure D. 159 : PV output by month for GC

- 12.47

- 1_R3

Figure D. 160 : Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R3

### 237 

## D.2.13 Detailed Commercial PV Plots for R3

- 12.47

- 1

Figure D. 161 : Comparison of peak load by month for R3

- 12.47

- 1

Figure D. 162 : Comparison of energy consumption by month for R3

- 12.47

- 1

### 238 Figure D. 163 : Comparison of losses by month for R3

- 12.47

- 1

Figure D. 164 : PV output by month for R3

- 12.47

- 1

### 239 Figure D. 165 : Comparison of CO 2 emissions by month for R3

- 12.47

- 1


## D.2.14 Detailed Commercial PV Plots for R3

- 12.47

- 2

### 240 Figure D. 166 : Comparison of peak load by month for R3

- 12.47

- 2

Figure D. 167 : Comparison of energy consumption by month for R3

- 12.47

- 2

### 241 Figure D. 168 : Comparison of losses by month for R3

- 12.47

- 2

Figure D. 169 : PV output by month for R3

- 12.47

- 2

### 242 Figure D. 170 : Comparison of CO 2 emissions by month for R3

- 12.47

- 2 

## D.2.15 Detailed Commercial PV Plots for R3

- 12.47

- 3

Figure D. 171 : Comparison of peak load by month for R3

- 12.47

- 3

### 243 Figure D. 172 : Comparison of energy consumption by month for R3

- 12.47

- 3

Figure D. 173 : Comparison of losses by month for R3

- 12.47

- 3

### 244 Figure D. 174 : PV output by month for R3

- 12.47

- 3

Figure D. 175 : Comparison of CO 2 emissions by month for R3

- 12.47

- 3

### 245 

## D.2.16 Detailed Commercial PV Plots for GC

- 12.47

- 1_R4

Figure D. 176 : Comparison of peak load by month for GC

- 12.47

- 1_R4

Figure D. 177 : Comparison of energy consumption by month for GC

- 12.47

- 1_R4

### 246 Figure D. 178 : Comparison of losses by month for GC

- 12.47

- 1_R4

Figure D. 179 : PV output by month for GC

- 12.47

- 1_R4

### 247 Figure D. 180 : Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R4


## D.2.17 Detailed Commercial PV Plots for R4

- 12.47

- 1

### 248 Figure D. 181 : Comparison of peak load by month for R4

- 12.47

- 1

Figure D. 182 : Comparison of energy consumption by month for R4

- 12.47

- 1

### 249 Figure D. 183 : Comparison of losses by month for R4

- 12.47

- 1

Figure D. 184 : PV output by month for R4

- 12.47

- 1

### 250 Figure D. 185 : Comparison of CO 2 emissions by month for R4

- 12.47

- 1 

## D.2.18 Detailed Commercial PV Plots for R4

- 12.47

- 2

Figure D. 186 : Comparison of peak load by month for R4

- 12.47

- 2

### 251 Figure D. 187 : Comparison of energy consumption by month for R4

- 12.47

- 2

Figure D. 188 : Comparison of losses by month for R4

- 12.47

- 2

### 252 Figure D. 189 : PV output by month for R4

- 12.47

- 2

Figure D. 190 : Comparison of CO 2 emissions by month for R4

- 12.47

- 2

### 253 

## D.2.19 Detailed Commercial PV Plots for R4

- 25.00

- 1

Figure D. 191 : Comparison of peak load by month for R4

- 25.00

- 1

Figure D. 192 : Comparison of energy consumption by month for R4

- 25.00

- 1

### 254 Figure D. 193 : Comparison of losses by month for R4

- 25.00

- 1

Figure D. 194 : PV output by month for R4

- 25.00

- 1

### 255 Figure D. 195 : Comparison of CO 2 emissions by month for R4

- 25.00

- 1

### 256 

## D.2.20 Detailed Commercial PV Plots for GC

- 12.47

- 1_R5

Figure D. 196 : Comparison of peak load by month for GC

- 12.47

- 1_R5

Figure D. 197 : Comparison of energy consumption by month for GC

- 12.47

- 1_R5

### 257 Figure D. 198 : Comparison of losses by month for GC

- 12.47

- 1_R5

Figure D. 199 : PV output by month for GC

- 12.47

- 1_R5

### 258 Figure D. 200 : Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R5


## D.2.21 Detailed Commercial PV Plots for R5

- 12.47

- 1

Figure D. 201 : Comparison of peak load by month for R5

- 12.47

- 1

### 259 Figure D. 202 : Comparison of energy consumption by month for R5

- 12.47

- 1

Figure D. 203 : Comparison of losses by month for R5

- 12.47

- 1

### 260 Figure D. 204 : PV output by month for R5

- 12.47

- 1

Figure D. 205 : Comparison of CO 2 emissions by month for R5

- 12.47

- 1

### 261 

## D.2.22 Detailed Commercial PV Plots for R5

- 12.47

- 2

Figure D. 206 : Comparison of peak load by month for R5

- 12.47

- 2

Figure D. 207 : Comparison of energy consumption by month for R5

- 12.47

- 2

### 262 Figure D. 208 : Comparison of losses by month for R5

- 12.47

- 2

Figure D. 209 : PV output by month for R5

- 12.47

- 2

### 263 Figure D. 210 : Comparison of CO 2 emissions by month for R5

- 12.47

- 2


## D.2.23 Detailed Commercial PV Plots for R5

- 12.47

- 3

Figure D. 211 : Comparison of peak load by month for R5

- 12.47

- 3

### 264 Figure D. 212 : Comparison of energy consumption by month for R5

- 12.47

- 3

Figure D. 213 : Comparison of losses by month for R5

- 12.47

- 3

### 265 Figure D. 214 : PV output by month for R5

- 12.47

- 3

Figure D. 215 : Comparison of CO 2 emissions by month for R5

- 12.47

- 3

### 266 

## D.2.24 Detailed Commercial PV Plots for R5

- 12.47

- 4

Figure D. 216 : Comparison of peak load by month for R5

- 12.47

- 4

Figure D. 217 : Comparison of energy consumption by month for R5

- 12.47

- 4

### 267 Figure D. 218 : Comparison of losses by month for R5

- 12.47

- 4

Figure D. 219 : PV output by month for R5

- 12.47

- 4

### 268 Figure D. 220 : Comparison of CO 2 emissions by month for R5

- 12.47

- 4 

## D.2.25 Detailed Commercial PV Plots for R5

- 12.47

- 5

### 269 Figure D. 221 : Comparison of peak load by month for R5

- 12.47

- 5

Figure D. 222 : Comparison of energy consumption by month for R5

- 12.47

- 5

### 270 Figure D. 223 : Comparison of losses by month for R5

- 12.47

- 5

Figure D. 224 : PV output by month for R5

- 12.47

- 5

### 271 Figure D. 225 : Comparison of CO 2 emissions by month for R5

- 12.47

- 5


## D.2.26 Detailed Commercial PV Plots for R5

- 25.00

- 1

### 272 Figure D. 226 : Comparison of peak load by month for R5

- 25.00

- 1

Figure D. 227 : Comparison of energy consumption by month for R5

- 25.00

- 1

### 273 Figure D. 228 : Comparison of losses by month for R5

- 25.00

- 1

Figure D. 229 : Comparison of CO 2 emissions by month for R5

- 25.00

- 1

### 274 

## D.2.27 Detailed Commercial PV Plots for R5

- 35.00

- 1

Figure D. 230 : Comparison of peak load by month for R5

- 35.00

- 1

Figure D. 231 : Comparison of en ergy consumption by month for R5

- 35.00

- 1

### 275 Figure D. 232 : Comparison of losses by month for R5

- 35.00

- 1

Figure D. 233 : Comparison of CO 2 emissions by month for R5

- 35.00

- 1

### 276 

## D.3 Addition of Combined PV
D.3.1 Detailed Combined PV Plots for R1

- 12.47

- 1

Figure D. 234 : Comparison of peak load by month for R1

- 12.47

- 1

Figure D. 235 : Comparison of energy consumption by month for R1

- 12.47

- 1

### 277 Figure D. 236 : Comparison of losses by month for R1

- 12.47

- 1

Figure D. 237 : PV output by month for R1

- 12.47

- 1

### 278 Figure D. 238 : Comparison of CO 2 emissions by month for R1

- 12.47

- 1


## D.3.2 Detailed Combined PV Plots for R1

- 12.47

- 2

Figure D. 239 : Comparison of p eak load by month for R1

- 12.47

- 2

### 279 Figure D. 240 : Comparison of energy consumption by month for R1

- 12.47

- 2

Figure D. 241 : Comparison of losses by month for R1

- 12.47

- 2

### 280 Figure D. 242 : PV output by month for R1

- 12.47

- 2

Figure D. 243 : Comparison of CO 2 emissions by month for R1

- 12.47

- 2

### 281 

## D.3.3 Detailed Combined PV Plots for R1

- 12.47

- 3

Figure D. 244 : Comparison of peak load by month for R1

- 12.47

- 3

Figure D. 245 : Comparison of energy consumption by month for R1

- 12.47

- 3

### 282 Figure D. 246 : Comparison of losses by month for R1

- 12.47

- 3

Figure D. 247 : PV output by month f or R1

- 12.47

- 3

### 283 Figure D. 248 : Comparison of CO 2 emissions by month for R1

- 12.47

- 3


## D.3.4 Detailed Combined PV Plots for R1

- 12.47

- 4

Figure D. 249 : Comparison of peak load by month for R1

- 12.47

- 4

### 284 Figure D. 25 0 : Comparison of energy consumption by month for R1

- 12.47

- 4

Figure D. 251 : Comparison of losses by month for R1

- 12.47

- 4

### 285 Figure D. 252 : PV output by month for R1

- 12.47

- 4

Figure D. 253 : Comparison of CO 2 emissions by month for R1

- 12.47

- 4

### 286 

## D.3.5 Detailed Combined PV Plots for R1

- 25.00

- 1

Figure D. 254 : Comparison of peak load by month for R1

- 25.00

- 1

Figure D. 255 : Comparison of energy consumption by month for R1

- 25.00

- 1

### 287 Figure D. 256 : Comparison of losses by month for R1

- 25.00

- 1

Figure D. 257 : PV output by month for R1

- 25.00

- 1

### 288 Figure D. 258 : Comparison of CO 2 emissions by month for R1

- 25.00

- 1 

## D.3.6 Detailed Combined PV Plots for R2

- 12.47

- 1

Figure D. 259 : Comparison of peak load by month for R2

- 12.47

- 1

### 289 Figure D. 260 : Comparison of energy consum ption by month for R2

- 12.47

- 1

Figure D. 261 : Comparison of losses by month for R2

- 12.47

- 1

### 290 Figure D. 262 : PV output by month for R2

- 12.47

- 1

Figure D. 263 : Comparison of CO 2 emissions by month for R2

- 12.47

- 1

### 291 

## D.3.7 Detailed Combined PV Plots for R2

- 12.47

- 2

Figure D. 264 : Comparison of peak load by month for R2

- 12.47

- 2

Figure D. 265 : Comparison of energy consumption by month for R2

- 12.47

- 2

### 292 Figure D. 266 : Comparison of losses by month for R2

- 12.47

- 2

Figure D. 267 : PV output by month for R2

- 12.47

- 2

### 293 Figure D. 268 : Comparison of CO 2 emissions by month for R2

- 12.47

- 2 

## D.3.8 Detailed Combined PV Plots for R2

- 25.00

- 1

Figure D. 269 : Comparison of peak load by month for R2

- 25.00

- 1

### 294 Figure D. 270 : Comparison of energy consumption by month for R2

- 25.00

- 1

Figure D. 271 : Comparison of losses by month for R2

- 25.00

- 1

### 295 Figure D. 272 : PV output by month for R2

- 25.00

- 1

Figure D. 273 : Comparison of CO 2 emissions by month for R2

- 25.00

- 1

### 296 

## D.3.9 Detailed Combined PV Plots for R2

- 35.00

- 1

Figure D. 274 : Comparison of peak load by month for R2

- 35.00

- 1

Figure D. 275 : Comparison of energy consumption by month for R2

- 35.00

- 1

### 297 Figure D. 276 : Comparison of losses by month for R2

- 35.00

- 1

Figure D. 277 : PV output by month for R2

- 35.00

- 1

### 298 Figure D. 278 : Comparison of CO 2 emissions by month for R2

- 35.00

- 1 

## D.3.10 Detailed Combined PV Plots for R3

- 12.47

- 1

Figure D. 279 : Comparison of peak load by month for R3

- 12.47

- 1

### 299 Figure D. 280 : Comparison of energy consumption by month for R3

- 12.47

- 1

Figure D. 281 : Comparison of losses by month for R3

- 12.47

- 1

### 300 Figure D. 282 : PV output by month for R3

- 12.47

- 1

Figure D. 283 : Comparison of CO 2 emissions by month for R3

- 12.47

- 1

### 301 

## D.3.11 Detailed Combined PV Plots for R3

- 12.47

- 3

Figure D. 284 : Comparison of peak load by month for R3

- 12.47

- 3

Figure D. 285 : Comparison of energy consumption by month for R3

- 12.47

- 3

### 302 Figure D. 286 : Comparison of losses by month for R3

- 12.47

- 3

Figure D. 287 : PV output by month for R3

- 12.47

- 3

### 303 Figure D. 288 : Comparison of energy consumption by month for R3

- 12.47

- 3 

## D.3.12 Detailed Combined PV Plots for R4

- 12.47

- 1

Figure D. 289 : Comparison of peak load by month for R4

- 12.47

- 1

### 304 Figure D. 290 : Comparison of energy consumption by month for R4

- 12.47

- 1

Figure D. 291 : Comparison of losses by month for R4

- 12. 47

- 1

### 305 Figure D. 292 : PV output by month for R4

- 12.47

- 1

Figure D. 293 : Comparison of CO 2 emissions by month for R4

- 12.47

- 1

### 306 

## D.3.13 Detailed Combined PV Plots for R4

- 12.47

- 2

Figure D. 294 : Comparison of peak load by month for R4

- 12.47

- 2

Figure D. 295 : Comparison of energy consumption by month for R4

- 12.47

- 2

### 307 Figure D. 296 : Comparison of losses by month for R4

- 12.47

- 2

Figure D. 297 : PV output by month for R4

- 12.47

- 2

### 308 Figure D. 298 : Comparison of CO 2 emissions by month for R4

- 12.47

- 2 

## D.3.14 Detailed Combined PV Plots for R4

- 25.00

- 1

Figure D. 299 : Comparison of peak load by month for R4

- 25.00

- 1

### 309 Figure D. 300 : Comparison of energy consumption by month for R4

- 25.00

- 1

Figure D. 301 : Comparison of losses by month for R4

- 25.00

- 1

### 310 Figure D. 302 : PV output by month for R4

- 25.00

- 1

Figure D. 303 : Comparison of CO 2 emissions by month for R4

- 25.00

- 1

### 311 

## D.3.15 Detailed Combined PV Plots for R5

- 12.47

- 1

Figure D. 304 : Comparison of peak load by month for R5

- 12.47

- 1

Figure D. 305 : Comparison of energy consumption by month for R5

- 12.47

- 1

### 312 Figure D. 306 : Comparison of losses by month for R5

- 12.47

- 1

Figure D. 307 : PV output by month for R5

- 12.47

- 1

### 313 Figure D. 308 : Comparison of CO 2 emissions by month for R5

- 12.47

- 1 

## D.3.16 Detailed Combined PV Plots for R5

- 12.47

- 2

Figure D. 309 : Comparison of peak load by month for R5

- 12.47

- 2

### 314 Figure D. 310 : Comparison of energy consumption by month for R5

- 12.47

- 2

Figure D. 311 : Comparison of losses by month for R5

- 12.47

- 2

### 315 Figure D. 312 : PV output by month for R5

- 12.47

- 2

Figure D. 313 : Comparison of CO 2 emissions by month for R5

- 12.47

- 2

### 316 

## D.3.17 Detailed Combined PV Plots for R5

- 12.47

- 3

Figure D. 314 : Comparison of peak load by month for R5

- 12.47

- 3

Figure D. 315 : Comparison of energy consumption by month for R5

- 12.47

- 3

### 317 Figure D. 316 : Comparison of losses by month for R5

- 12.47

- 3

Figure D. 317 : PV output by month for R5

- 12.47

- 3

### 318 Figure D. 318 : Comparison of CO 2 emissions by month for R5

- 12.47

- 3 

## D.3.18 Detailed Combined PV Plots for R5

- 12.47

- 4

Figure D. 319 : Comparison of peak load by month for R5

- 12.47

- 4

### 319 Figure D. 320 : Comparison of energy consumption by month for R5

- 12.47

- 4

Figure D. 321 : Comparison of losses by month for R5

- 12.47

- 4

### 320 Figure D. 322 : PV output by month for R5

- 12.47

- 4

Figure D. 323 : Comparison of CO 2 emissions by month for R5

- 12.47

- 4

### 321 

## D.3.19 Detailed Combined PV Plots for R5

- 12.47

- 5

Figure D. 324 : Comparison of peak load by month for R5

- 12.47

- 5

Figure D. 325 : Comparison of energy consumption by month for R5

- 12.47

- 5

### 322 Figure D. 326 : Comparison of losses by month for R5

- 12.47

- 5

Figure D. 327 : PV output by month for R5

- 12.47

- 5

### 323 Figure D. 328 : Comparison of CO 2 emissions by month for R5

- 12.47

- 5 

## D.3.20 Detailed Combined PV Plots for R5

- 25.00

- 1

Figure D. 329 : Comparison of peak load by month for R5

- 25.00

- 1

### 324 Figure D. 330 : Comparison of energy consumption by month for R5

- 25.00

- 1

Figure D. 331 : Comparison of losses by month for R5

- 25.00

- 1

### 32 5 Figure D. 332 : PV output by month for R5

- 25.00

- 1

Figure D. 333 : Comparison of CO 2 emissions by month for R5

- 25.00

- 1

### 326 

## D.3.21 Detailed Combined PV Plots for R5

- 35.00

- 1

The detailed plots for this feeder is already given in Section


## D.4 Addition of Commercial WTG
D.4.1 Detailed Commercial W TG plots for GC

- 12.47

- 1_R1

Figure D. 334 : Comparison of peak load by month for GC

- 12.47

- 1_R1

### 327 Figure D. 335 : Comparison of energy consumption by month for GC

- 12.47

- 1_R1

Figure D. 336 : Comparison of losses by month for GC

- 12.47

- 1_R1

### 328 Figure D. 337 : Co mparison of WTG output by month for GC

- 12.47

- 1_R1

Figure D. 338 : Comparison of CO 2 output by month for GC

- 12.47

- 1_R1

### 329 

## D.4.2 Detailed Commer cial WTG plots for GC

- 12.47

- 1_R2

The plots are already given in Section 3

. 

## D.4.3 Detailed Commercial WTG plots for GC

- 12.47

- 1_R 3

Figure D. 339 : Comparison of peak load by month for GC

- 12.47

- 1_R2

Figure D. 340 : Comparison of energy consumption by month for GC

- 12.47

- 1_R3

### 330 Figure D. 341 : Comparison of losses by month for GC

- 12.47

- 1_R3

Figure D. 342 : Comparison of WTG output by month for GC

- 12.47

- 1_R3

### 331 Figure D. 343 : Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R3 

## D.4.4 Detailed Commercial WTG plots for GC

- 12.47

- 1_R4

Figure D. 344 : Comparison of peak load by month for GC

- 12.47

- 1_R4

### 332 Figure D. 345 : Comparison of energy consumption by month for GC

- 12.47

- 1_R4

Figure D. 346 : Comparison of losses by month for GC

- 12.47

- 1_R4

### 333 Figure D. 347 : Comparison of WTG output by month for GC

- 12.47

- 1_R4

Figure D. 348 : Comparis on of CO 2 emissions by month for GC

- 12.47

- 1_R4

### 334 

## D.4.5 Detailed Commercial WTG plots for GC

- 12.47

- 1_R5

Figure D. 349 : Comparison of peak load by month for GC

- 12.47

- 1_R5

Figure D. 350 : Comparison of energy consumption by month for GC

- 12.47

- 1_R5

### 335 Figure D. 351 : Comparison of losses by month for GC

- 12.47

- 1_R 5

Figure D. 352 : Comparison of WTG output by month for GC

- 12.47

- 1_R5

### 336 Figure D. 353 : Comparison of CO 2 emissions by month for GC

- 12.47

- 1_R5

### 335 

# Appendix E: Individual Feeder Impact Metrics

This appendix contains the raw performance metric values for each technology on each of the prototypical distribution feeders. The impact matrices in Section 4 .1, 4.2, and 4.3 are calculated from the raw values in this appendix.


## E.1 Individual Performance Metrics for Base Case

### 336 Table E. 1 : Base case performance metrics for region 1



### Index Metric UnitsGC-12.47-1 R1R1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh 2,083 2,692 992 435 1,948 875
2 Monthly Customer Electricity Usage MWh 1,521 1,965 724 317 1,422 639 Peak Generation kW 5,313 7,329 2,675 1,261 5,050 2,317 Nuclear % 10.68 10.68 10.68 10.68 10.09 10.68 Solar % 0.25 0.25 0.25 0.25 0.21 0.25 Bio % 0.67 0.67 0.67 0.67 0.72 0.67 Wind % 4.07 4.07 4.07 4.07 3.55 4.07 Coal % 2.88 2.88 2.88 2.88 4.38 2.88 Hydroelectric % 36.88 36.88 36.88 36.88 26.32 36.88 Natural Gas % 41.38 41.38 41.38 41.38 51.24 41.38 Geothermal % 2.84 2.84 2.84 2.84 3.11 2.84 Petroleum % 0.35 0.35 0.35 0.35 0.38 0.35 Distributed Solar PV % 0.00 0.00 0.00 0.00 0.00 0.00 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,288 7,085 2,590 1,247 4,924 2,261 Controllable load % 0.00 0.00 0.00 0.00 0.00 0.00
7 Annual Electricity Production MWh 18,290 24,196 8,964 3,829 17,276 7,776 12 CO2 Emissions Tons 1,783 2,273 818 392 1,774 752 SOx Emissions Tons 0.03 0.03 0.01 0.01 0.04 0.01 NOx Emissions Tons 0.24 0.28 0.10 0.05 0.22 0.10 PM-10 Emissions Tons 0.25 0.32 0.12 0.06 0.25 0.11 16 ou y Custo e Electricity Usage* kWh 0 0 0 0 0 0 17 Annual Storage Dispatch* kWh 0 0 0 0 0 0
18 Average Energy Storage Efficiency* % 0 0 0 0 0 0 Feeder Real Load MW 2,088 2,762 1,023 437 1,972 888 Feeder Reactive Load MVAR 68 -284 -200 11 62 -70 29 Distribution Losses % 0.23 2.54 3.05 0.56 1.21 1.44 30 Distribution Power Factor pf 0.9994 0.9925 0.9678 0.9997 0.9995 0.9666 39 CO2 Emissions Tons 1,787 2,332 844 394 1,796 763 SOx Tons 0.03 0.03 0.01 0.01 0.04 0.01 NOx Tons 0.24 0.29 0.11 0.05 0.22 0.10 PM-10 Tons 0.25 0.33 0.12 0.06 0.26 0.11 3

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### 337 Table E. 2 : Base case performance metrics for region 2



### Index Metric UnitsGC-12.47-1 R2R2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh 2,169 2,268 1,970 2,975 6,342 4,576
2 Monthly Customer Electricity Usage MWh 1,584 1,656 1,438 2,171 4,630 3,340 Peak Generation kW 5,749 6,287 5,777 8,555 16,840 12,676 Nuclear % 26.33 26.33 26.33 27.95 26.33 26.33 Solar % 0.01 0.01 0.01 0.01 0.01 0.01 Bio % 0.82 0.82 0.82 0.84 0.82 0.82 Wind % 1.41 1.41 1.41 1.70 1.41 1.41 Coal % 47.18 47.18 47.18 45.54 47.18 47.18 Hydroelectric % 7.42 7.42 7.42 9.05 7.42 7.42 Natural Gas % 16.33 16.33 16.33 14.47 16.33 16.33 Geothermal % 0.07 0.07 0.07 0.07 0.07 0.07 Petroleum % 0.43 0.43 0.43 0.37 0.43 0.43 Distributed Solar PV % 0.00 0.00 0.00 0.00 0.00 0.00 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,720 6,166 5,647 8,360 16,622 12,533 Controllable load % 0.00 0.00 0.00 0.00 0.00 0.00
7 Annual Electricity Production MWh 19,050 20,128 17,588 26,686 56,091 40,417 12 CO2 Emissions Tons 8,419 9,246 8,417 12,627 26,866 17,434 SOx Emissions Tons 3.81 4.21 3.88 5.82 12.33 7.86 NOx Emissions Tons 2.43 2.67 2.46 3.69 7.81 5.02 PM-10 Emissions Tons 1.25 1.37 1.25 1.87 3.99 2.58 16 ou y Custo e Electricity Usage* kWh 0 0 0 0 0 0 17 Annual Storage Dispatch* kWh 0 0 0 0 0 0
18 Average Energy Storage Efficiency* % 0 0 0 0 0 0 Feeder Real Load MW 2,175 2,298 2,008 3,046 6,403 4,614 Feeder Reactive Load MVAR 92 116 146 -130 333 69 29 Distribution Losses % 0.25 1.27 1.87 2.36 0.96 0.82 30 Distribution Power Factor pf 0.9989 0.9987 0.9973 0.9973 0.9986 0.9996 39 CO2 Emissions Tons 8,440 9,365 8,578 12,932 27,125 17,579 SOx Tons 3.82 4.26 3.95 5.96 12.45 7.93 NOx Tons 2.44 2.71 2.51 3.78 7.88 5.06 PM-10 Tons 1.25 1.39 1.27 1.92 4.03 2.61 3

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### 338 Table E. 3 : Base case performance metrics for region 3



### Index Metric UnitsGC-12.47-1 R3R3-12.47-1R3-12.47-2R3-12.47-3

1 Hourly Customer Electricity Usage kWh 2,635 3,661 1,642 3,705
2 Monthly Customer Electricity Usage MWh 1,924 2,673 1,199 2,705 Peak Generation kW 6,594 9,315 4,422 8,417 Nuclear % 8.65 9.72 9.72 9.72 Solar % 0.13 0.13 0.13 0.13 Bio % 0.23 0.25 0.25 0.25 Wind % 2.05 2.45 2.45 2.45 Coal % 40.24 41.52 41.52 41.52 Hydroelectric % 5.58 6.40 6.40 6.40 Natural Gas % 41.67 37.88 37.88 37.88 Geothermal % 1.25 1.40 1.40 1.40 Petroleum % 0.20 0.25 0.25 0.25 Distributed Solar PV % 0.00 0.00 0.00 0.00 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 6,554 9,122 4,364 8,157 Controllable load % 0.00 0.00 0.00 0.00
7 Annual Electricity Production MWh 23,160 32,687 14,483 33,603 12 CO2 Emissions Tons 16,269 23,430 9,963 25,107 SOx Emissions Tons 7.03 10.24 4.25 11.14 NOx Emissions Tons 4.38 6.36 2.66 6.88 PM-10 Emissions Tons 2.42 3.49 1.48 3.74 16 ou y Custo e Electricity Usage* kWh 0 0 0 0 17 Annual Storage Dispatch* kWh 0 0 0 0
18 Average Energy Storage Efficiency* % 0 0 0 0 Feeder Real Load MW 2,644 3,731 1,653 3,836 Feeder Reactive Load MVAR 219 484 143 547 29 Distribution Losses % 0.33 1.87 0.69 3.40 30 Distribution Power Factor pf 0.9969 0.9904 0.99685 0.98973 39 CO2 Emissions Tons 16,323 23,877 10,032 25,991 SOx Tons 7.05 10.44 4.28 11.53 NOx Tons 4.39 6.48 2.67 7.12 PM-10 Tons 2.43 3.56 1.49 3.87 3

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### 339 Table E. 4 : Base case performance metrics for region 4



### Index Metric UnitsGC-12.47-1 R4R4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh 2,339 1,909 832 347
2 Monthly Customer Electricity Usage MWh 1,708 1,393 607 253 Peak Generation kW 6,221 4,798 2,205 945 Nuclear % 21.91 21.91 23.58 23.58 Solar % 0.00 0.00 0.00 0.00 Bio % 0.18 0.18 0.21 0.21 Wind % 0.60 0.60 0.59 0.59 Coal % 57.14 57.14 56.06 56.06 Hydroelectric % 2.20 2.20 3.09 3.09 Natural Gas % 17.49 17.49 16.14 16.14 Geothermal % 0.00 0.00 0.00 0.00 Petroleum % 0.48 0.48 0.33 0.33 Distributed Solar PV % 0.00 0.00 0.00 0.00 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 6,186 4,701 2,171 928 Controllable load % 0.00 0.00 0.00 0.00
7 Annual Electricity Production MWh 20,550 17,195 7,457 3,118 12 CO2 Emissions Tons 10,321 9,844 3,994 1,608 SOx Emissions Tons 4.91 4.72 1.92 0.77 NOx Emissions Tons 3.00 2.87 1.17 0.47 PM-10 Emissions Tons 1.54 1.47 0.60 0.24 16 ou y Custo e Electricity Usage* kWh 0 0 0 0 17 Annual Storage Dispatch* kWh 0 0 0 0
18 Average Energy Storage Efficiency* % 0 0 0 0 Feeder Real Load MW 2,346 1,963 851 356 Feeder Reactive Load MVAR 138 -413 98 45 29 Distribution Losses % 0.28 2.76 2.32 2.53 30 Distribution Power Factor pf 0.9982 0.9666 0.9934 0.9920 39 CO2 Emissions Tons 10,350 10,123 4,089 1,650 SOx Tons 4.93 4.86 1.96 0.79 NOx Tons 3.00 2.95 1.19 0.48 PM-10 Tons 1.54 1.51 0.61 0.25 40 3

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### 340 Table E. 5 : Base case performance metrics for region 5



### Index Metric UnitsGC-12.47-1 R5R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh 2,747 4,490 2,226 4,669 3,468 4,116 5,627 5,689
2 Monthly Customer Electricity Usage MWh 2,005 3,278 1,625 3,408 2,532 3,005 4,108 4,153 Peak Generation kW 5,841 9,451 4,992 10,384 7,531 9,041 12,282 12,428 Nuclear % 13.85 13.85 13.85 13.53 13.85 13.53 13.85 13.85 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.33 0.33 0.33 0.31 0.33 0.31 0.33 0.33 Wind % 1.48 1.48 1.48 1.74 1.48 1.74 1.48 1.48 Coal % 30.17 30.17 30.17 30.37 30.17 30.37 30.17 30.17 Hydroelectric % 0.63 0.63 0.63 0.78 0.63 0.78 0.63 0.63 Natural Gas % 51.68 51.68 51.68 51.29 51.68 51.29 51.68 51.68 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % 1.86 1.86 1.86 1.98 1.86 1.98 1.86 1.86 Distributed Solar PV % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,810 9,319 4,848 9,772 7,373 8,784 12,088 12,270 Controllable load % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
7 Annual Electricity Production MWh 24,144 39,806 19,900 42,781 30,976 36,921 49,992 50,486 12 CO2 Emissions Tons 9,364 15,419 7,414 15,195 11,809 13,594 18,504 18,904 SOx Emissions Tons 1.55 2.23 1.11 1.64 1.70 1.66 2.19 2.34 NOx Emissions Tons 1.38 2.11 1.04 1.82 1.61 1.72 2.31 2.41 PM-10 Emissions Tons 1.37 2.26 1.09 2.23 1.73 1.99 2.71 2.77 16 ou y Custo e Electricity Usage* kWh 0 0 0 0 0 0 0 0 17 Annual Storage Dispatch* kWh 0 0 0 0 0 0 0 0
18 Average Energy Storage Efficiency* % 0 0 0 0 0 0 0 0 Feeder Real Load MW 2,756 4,544 2,272 4,884 3,536 4,215 5,707 5,763 Feeder Reactive Load MVAR 248 542 242 -357 407 594 650 641 29 Distribution Losses % 0.33 1.19 2.02 4.41 1.92 2.34 1.39 1.28 30 Distribution Power Factor pf 0.9964 0.9937 0.9952 0.9779 0.9942 0.9913 0.9942 0.9944 39 CO2 Emissions Tons 9,395 15,605 7,567 15,895 12,040 13,919 18,766 19,150 SOx Tons 1.55 2.26 1.14 1.72 1.73 1.70 2.22 2.37 NOx Tons 1.39 2.14 1.06 1.91 1.65 1.76 2.34 2.44 PM-10 Tons 1.38 2.29 1.11 2.33 1.77 2.04 2.75 2.81 3

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### 341 

## E.2 Individual Residential PV Performance M etrics

Table E. 6 : Residential PV performance metrics for region 1



### Index Metric UnitsR1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh 2,665 981 427 1,923 866
2 Monthly Customer Electricity Usage MWh 1,945 716 312 1,404 632 Peak Generation kW 7,398 2,662 1,176 4,916 2,360 Nuclear % 10.56 9.96 10.14 10.74 10.49 Solar % 0.25 0.21 0.21 0.25 0.25 Bio % 0.66 0.71 0.72 0.67 0.66 Wind % 4.02 3.51 3.57 4.09 4.00 Coal % 2.85 4.33 4.40 2.90 2.83 Hydroelectric % 36.47 25.99 26.44 37.09 36.21 Natural Gas % 40.92 50.60 51.48 41.61 40.63 Geothermal % 2.81 3.07 1.20 1.10 2.79 Petroleum % 0.17 0.69 0.00 0.00 0.34 Distributed Solar PV % 1.30 0.93 1.84 1.55 1.81 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 4 Peak Load MW 7,072 2,544 1,168 4,817 2,261
7 Annual Electricity Production MWh 23,952 8,862 3,765 17,049 7,698 12 CO2 Emissions Tons 2,211 791 371 1,705 725 SOx Emissions Tons 0.03 0.01 0.00 0.03 0.01 NOx Emissions Tons 0.28 0.10 0.05 0.21 0.10 PM-10 Emissions Tons 0.31 0.11 0.05 0.24 0.10 Feeder Real Load MW 2,734 1,012 430 1,946 879 Feeder Reactive Load MVAR -284.80 -199.79 10.46 61.47 -70.59 29 Distribution Losses % 2.55 3.07 0.55 1.19 1.43 30 Distribution Power Factor pf 0.99 0.97 1.00 1.00 0.97 39 CO2 Emissions Tons 2,269 816 373 1,726 736 SOx Tons 0.03 0.01 0.00 0.03 0.01 NOx Tons 0.28 0.10 0.05 0.22 0.10 PM-10 Tons 0.32 0.12 0.05 0.24 0.10 3

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### 342 Table E. 7 : Residential PV performance metrics for region 2



### Index Metric UnitsR2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh 2,260 1,962 2,971 6,321 4,560
2 Monthly Customer Electricity Usage MWh 1,649 1,432 2,169 4,614 3,329 Peak Generation kW 6,094 5,968 8,464 16,871 12,382 Nuclear % 26.67 25.84 26.41 26.24 27.95 Solar % 0.01 0.01 0.01 0.01 0.01 Bio % 0.83 0.80 0.82 0.82 0.84 Wind % 1.43 1.38 1.41 1.41 1.70 Coal % 47.78 46.30 47.33 47.02 45.54 Hydroelectric % 6.18 7.28 7.44 7.39 8.94 Natural Gas % 16.54 16.03 16.38 16.27 14.47 Geothermal % 0.00 0.07 0.00 0.07 0.00 Petroleum % 0.00 1.79 0.00 0.27 0.00 Distributed Solar PV % 0.56 0.50 0.18 0.50 0.54 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 Peak Load MW 6,051 5,727 8,297 16,596 12,241 Annual Electricity Production MWh 20,049 17,513 26,651 55,904 40,275 12 CO2 Emissions Tons 9,187 8,347 12,595 26,709 17,338 SOx Emissions Tons 4.18 3.85 5.81 12.26 7.82 NOx Emissions Tons 2.66 2.44 3.68 7.77 4.99 PM-10 Emissions Tons 1.36 1.24 1.87 3.96 2.57 Feeder Real Load MW 2,289 1,999 3,042 6,382 4,598 Feeder Reactive Load MVAR 116.29 146.10 -130.05 332.65 68.90 29 Distribution Losses % 1.27 1.87 2.36 0.96 0.82 30 Distribution Power Factor pf 1.00 1.00 1.00 1.00 1.00 39 CO2 Emissions Tons 9,305 8,506 12,899 26,967 17,481 SOx Tons 4.24 3.92 5.95 12.38 7.89 NOx Tons 2.69 2.49 3.77 7.84 5.04 PM-10 Tons 1.38 1.26 1.91 4.00 2.59 3

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### 343 Table E. 8 : Residential PV performance metrics for region 3



### Index Metric UnitsR3-12.47-1R3-12.47-2R3-12.47-3

1 Hourly Customer Electricity Usage kWh 3,597 1,642 3,657
2 Monthly Customer Electricity Usage MWh 2,626 1,199 2,669 Peak Generation kW 8,985 4,422 8,338 Nuclear % 9.78 9.72 9.67 Solar % 0.13 0.13 0.13 Bio % 0.25 0.25 0.25 Wind % 2.47 2.45 2.44 Coal % 41.78 41.52 41.32 Hydroelectric % 5.15 6.40 6.37 Natural Gas % 38.12 37.88 37.70 Geothermal % 0.00 1.40 0.24 Petroleum % 0.00 0.25 0.00 Distributed Solar PV % 2.32 0.00 1.87 Distributed Wind % 0.00 0.00 0.00 Peak Load MW 8,848 4,364 8,038 Annual Electricity Production MWh 32,110 14,483 33,168 12 CO2 Emissions Tons 23,080 9,963 24,830 SOx Emissions Tons 10.17 4.25 11.07 NOx Emissions Tons 6.30 2.66 6.83 PM-10 Emissions Tons 3.44 1.48 3.70 Feeder Real Load MW 3,666 1,653 3,786 Feeder Reactive Load MVAR 482.96 142.77 546.53 29 Distribution Losses % 1.88 0.69 3.42 30 Distribution Power Factor pf 0.99 1.00 0.99 39 CO2 Emissions Tons 23,523 10,032 25,710 SOx Tons 10.36 4.28 11.46 NOx Tons 6.42 2.67 7.07 PM-10 Tons 3.51 1.49 3.83 3

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### 344 Table E. 9 : Residential PV performance metrics for region 4



### Index Metric UnitsR4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh 1,871 816 339
2 Monthly Customer Electricity Usage MWh 1,366 596 248 Peak Generation kW 4,607 2,175 892 Nuclear % 21.99 21.62 23.96 Solar % 0.00 0.00 0.00 Bio % 0.18 0.18 0.21 Wind % 0.60 0.59 0.60 Coal % 57.34 56.38 56.96 Hydroelectric % 0.00 0.93 0.00 Natural Gas % 16.59 17.26 15.71 Geothermal % 0.00 0.00 0.00 Petroleum % 0.00 0.00 0.00 Distributed Solar PV % 3.30 3.03 2.56 Distributed Wind % 0.00 0.00 0.00 Peak Load MW 4,527 2,112 889 Annual Electricity Production MWh 16,859 7,324 3,050 12 CO2 Emissions Tons 9,521 3,861 1,539 SOx Emissions Tons 4.58 1.86 0.74 NOx Emissions Tons 2.78 1.13 0.45 PM-10 Emissions Tons 1.42 0.58 0.23 Feeder Real Load MW 1,925 836 348 Feeder Reactive Load MVAR -413.97 97.85 44.67 29 Distribution Losses % 2.79 2.35 2.58 30 Distribution Power Factor pf 0.96 0.99 0.99 39 CO2 Emissions Tons 9,795 3,954 1,580 SOx Tons 4.71 1.90 0.76 NOx Tons 2.86 1.16 0.46 PM-10 Tons 1.46 0.59 0.24 3

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### 345 Table E. 10 : Residential PV performance metrics for region 5



### Index Metric UnitsR5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh 4,408 2,177 4,574 3,401 4,034 5,506 5,568
2 Monthly Customer Electricity Usage MWh 3,218 1,589 3,339 2,483 2,945 4,020 4,065 Peak Generation kW 9,224 4,979 10,502 7,232 8,887 11,791 11,849 Nuclear % 13.82 13.56 13.27 13.93 13.72 13.88 13.96 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.33 0.32 0.30 0.33 0.33 0.33 0.33 Wind % 1.48 1.45 1.71 1.49 1.47 1.48 1.49 Coal % 30.01 29.55 29.79 29.40 29.89 28.97 28.96 Hydroelectric % 0.00 0.17 0.77 0.00 0.19 0.00 0.00 Natural Gas % 51.58 50.61 50.32 51.97 51.21 51.80 52.08 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % 0.00 0.00 1.17 0.00 0.00 0.00 0.00 Distributed Solar PV % 2.78 4.34 2.67 2.88 3.20 3.53 3.17 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 9,074 4,732 9,691 7,115 8,549 11,626 11,783 Annual Electricity Production MWh 39,077 19,452 41,907 30,377 36,180 48,918 49,411 12 CO2 Emissions Tons 14,749 7,020 14,475 11,271 12,956 17,573 17,955 SOx Emissions Tons 1.94 0.95 1.36 1.47 1.40 1.81 1.95 NOx Emissions Tons 1.94 0.94 1.64 1.47 1.56 2.08 2.17 PM-10 Emissions Tons 2.16 1.03 2.12 1.65 1.90 2.57 2.63 Feeder Real Load MW 4,461 2,221 4,784 3,468 4,130 5,584 5,640 Feeder Reactive Load MVAR 540.52 239.98 -372.28 405.36 588.54 648.93 640.81 29 Distribution Losses % 1.19 1.98 4.38 1.92 2.33 1.39 1.29 30 Distribution Power Factor pf 0.99 0.99 0.98 0.99 0.99 0.99 0.99 39 CO2 Emissions Tons 14,926 7,161 15,138 11,492 13,265 17,821 18,189 SOx Tons 1.97 0.97 1.42 1.50 1.44 1.84 1.98 NOx Tons 1.96 0.96 1.72 1.50 1.60 2.11 2.19 PM-10 Tons 2.19 1.05 2.22 1.69 1.94 2.61 2.66 3

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## E.3 Individual Commercial PV Performance M etrics

### 346 Table E. 11 : Commercial PV performance metrics for region 1



### Index Metric UnitsGC-12.47-1 R1R1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh 2,067 2,659 984 431 1,940 869
2 Monthly Customer Electricity Usage MWh 1,509 1,941 718 315 1,417 634 Peak Generation kW 5,215 7,257 2,620 1,205 5,027 2,313 Nuclear % 10.71 10.64 10.73 10.86 10.09 10.62 Solar % 0.25 0.25 0.25 0.25 0.21 0.25 Bio % 0.67 0.67 0.67 0.68 0.72 0.67 Wind % 4.08 4.06 4.09 4.14 3.55 4.05 Coal % 2.89 2.87 2.89 2.93 4.38 2.86 Hydroelectric % 37.00 36.74 37.06 37.49 26.33 36.66 Natural Gas % 41.51 41.23 41.58 42.07 51.25 41.14 Geothermal % 1.68 1.84 1.64 0.32 3.06 2.42 Petroleum % 0.00 0.00 0.00 0.00 0.00 0.00 Distributed Solar PV % 1.20 1.71 1.07 1.26 0.41 1.33 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 4 Peak Load MW 5,207 6,987 2,549 1,211 4,902 2,244
7 Annual Electricity Production MWh 18,153 23,902 8,894 3,801 17,206 7,720 12 CO2 Emissions Tons 1,737 2,198 799 382 1,752 732 SOx Emissions Tons 0.02 0.03 0.01 0.01 0.03 0.01 NOx Emissions Tons 0.23 0.28 0.10 0.05 0.22 0.10 PM-10 Emissions Tons 0.24 0.31 0.11 0.05 0.25 0.10 Feeder Real Load MW 2,072 2,729 1,015 434 1,964 881 Feeder Reactive Load MVAR 67.76 -285.64 -199.74 10.42 61.56 -70.68 29 Distribution Losses % 0.23 2.55 3.07 0.56 1.21 1.42 30 Distribution Power Factor pf 1.00 0.99 0.97 1.00 1.00 0.97 39 CO2 Emissions Tons 1,741 2,255 824 385 1,773 743 SOx Tons 0.02 0.03 0.01 0.01 0.03 0.01 NOx Tons 0.23 0.28 0.10 0.05 0.22 0.10 PM-10 Tons 0.25 0.32 0.12 0.05 0.25 0.10 3

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### 347 Table E. 12 : Commercial PV performance metrics for region 2



### Index Metric UnitsGC-12.47-1 R2R2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh 2,156 2,263 1,969 2,975 6,325 4,571
2 Monthly Customer Electricity Usage MWh 1,574 1,652 1,437 2,171 4,617 3,337 Peak Generation kW 5,880 6,232 5,880 8,555 17,192 12,699 Nuclear % 25.88 26.40 26.09 27.95 27.52 27.65 Solar % 0.01 0.01 0.01 0.01 0.01 0.01 Bio % 0.81 0.82 0.81 0.84 0.83 0.83 Wind % 1.39 1.41 1.40 1.70 1.67 1.68 Coal % 46.38 47.31 46.74 45.54 44.84 45.06 Hydroelectric % 7.29 7.35 7.35 9.05 8.91 8.95 Natural Gas % 16.05 16.38 16.18 14.47 14.25 14.32 Geothermal % 0.07 0.00 0.07 0.07 0.07 0.07 Petroleum % 0.96 0.00 1.26 0.37 1.52 1.26 Distributed Solar PV % 1.16 0.31 0.09 0.00 0.37 0.17 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,751 6,128 5,696 8,360 16,711 12,421 Annual Electricity Production MWh 18,933 20,081 17,575 26,686 55,943 40,371 12 CO2 Emissions Tons 8,330 9,212 8,405 12,627 26,740 17,404 SOx Emissions Tons 3.77 4.19 3.88 5.82 12.27 7.85 NOx Emissions Tons 2.41 2.66 2.46 3.69 7.77 5.01 PM-10 Emissions Tons 1.24 1.37 1.25 1.87 3.97 2.58 Feeder Real Load MW 2,161 2,292 2,006 3,046 6,386 4,609 Feeder Reactive Load MVAR 91.50 116.25 146.32 -129.98 332.72 69.09 29 Distribution Losses % 0.25 1.27 1.87 2.36 0.96 0.82 30 Distribution Power Factor pf 1.00 1.00 1.00 1.00 1.00 1.00 39 CO2 Emissions Tons 8,350 9,331 8,565 12,932 26,999 17,548 SOx Tons 3.78 4.25 3.95 5.96 12.39 7.91 NOx Tons 2.41 2.70 2.50 3.78 7.85 5.05 PM-10 Tons 1.24 1.38 1.27 1.92 4.01 2.60 3

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### 348 Table E. 13 : Commercial PV performance metrics for region 3



### Index Metric UnitsGC-12.47-1 R3R3-12.47-1R3-12.47-2R3-12.47-3

1 Hourly Customer Electricity Usage kWh 2,615 3,580 1,629 3,594
2 Monthly Customer Electricity Usage MWh 1,909 2,614 1,189 2,624 Peak Generation kW 6,700 8,785 4,200 8,421 Nuclear % 9.58 9.85 9.93 8.26 Solar % 0.13 0.13 0.13 0.14 Bio % 0.25 0.25 0.26 0.20 Wind % 2.42 2.48 2.50 2.14 Coal % 40.94 42.07 42.40 40.26 Hydroelectric % 6.31 3.64 5.15 4.46 Natural Gas % 37.35 38.38 38.68 40.31 Geothermal % 1.38 0.00 0.00 0.81 Petroleum % 0.52 0.00 0.00 0.00 Distributed Solar PV % 1.12 3.20 0.96 3.42 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 6,567 8,707 4,231 7,923 Annual Electricity Production MWh 22,987 31,968 14,367 32,614 12 CO2 Emissions Tons 16,162 22,992 9,895 24,453 SOx Emissions Tons 7.00 10.15 4.23 10.97 NOx Emissions Tons 4.36 6.29 2.65 6.75 PM-10 Emissions Tons 2.41 3.43 1.47 3.65 Feeder Real Load MW 2,624 3,649 1,640 3,723 Feeder Reactive Load MVAR 218.30 482.33 142.52 545.26 29 Distribution Losses % 0.33 1.89 0.69 3.47 30 Distribution Power Factor pf 1.00 0.99 1.00 0.99 39 CO2 Emissions Tons 16,215 23,435 9,964 25,332 SOx Tons 7.02 10.34 4.26 11.36 NOx Tons 4.38 6.41 2.66 7.00 PM-10 Tons 2.41 3.49 1.48 3.78 3

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### 349 Table E. 14 : Commercial PV performance metrics for region 4



### Index Metric UnitsGC-12.47-1 R4R4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh 2,310 1,864 812 344
2 Monthly Customer Electricity Usage MWh 1,686 1,361 593 251 Peak Generation kW 6,067 4,530 2,063 943 Nuclear % 21.98 22.10 23.83 23.44 Solar % 0.00 0.00 0.00 0.00 Bio % 0.18 0.18 0.21 0.21 Wind % 0.60 0.61 0.60 0.59 Coal % 57.32 57.63 56.65 55.72 Hydroelectric % 0.52 0.00 0.00 2.57 Natural Gas % 17.55 15.54 14.26 16.04 Geothermal % 0.00 0.00 0.00 0.00 Petroleum % 0.00 0.00 0.00 0.00 Distributed Solar PV % 1.85 3.95 4.46 1.44 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 6,052 4,472 2,050 920 Annual Electricity Production MWh 20,292 16,804 7,286 3,093 12 CO2 Emissions Tons 10,090 9,467 3,822 1,583 SOx Emissions Tons 4.81 4.55 1.84 0.76 NOx Emissions Tons 2.93 2.76 1.12 0.46 PM-10 Emissions Tons 1.51 1.41 0.57 0.24 Feeder Real Load MW 2,316 1,918 832 353 Feeder Reactive Load MVAR 137.82 -413.75 97.67 44.73 29 Distribution Losses % 0.28 2.81 2.36 2.55 30 Distribution Power Factor pf 1.00 0.96 0.99 0.99 39 CO2 Emissions Tons 10,119 9,741 3,915 1,624 SOx Tons 4.83 4.68 1.88 0.78 NOx Tons 2.94 2.84 1.14 0.48 PM-10 Tons 1.51 1.45 0.58 0.24 3

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### 350 Table E. 15 : Commercial PV performance m etrics for region 5



### Index Metric UnitsGC-12.47-1 R5R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh 2,698 4,400 2,180 4,576 3,422 4,038 5,516 5,532
2 Monthly Customer Electricity Usage MWh 1,970 3,212 1,592 3,340 2,498 2,948 4,027 4,038 Peak Generation kW 5,975 9,110 4,951 9,981 7,457 8,849 11,978 11,909 Nuclear % 13.05 13.89 13.72 13.61 13.75 13.81 13.38 13.47 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.30 0.33 0.33 0.31 0.33 0.33 0.31 0.31 Wind % 1.68 1.48 1.47 1.75 1.47 1.48 1.72 1.73 Coal % 29.30 29.40 29.88 30.00 29.95 30.07 30.04 29.57 Hydroelectric % 0.75 0.00 0.62 0.00 0.63 0.36 0.77 0.00 Natural Gas % 49.48 51.82 51.19 51.60 51.30 51.52 50.73 51.06 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % 2.97 0.00 0.09 0.00 0.16 0.00 0.31 0.00 Distributed Solar PV % 2.48 3.07 2.71 2.73 2.41 2.44 2.74 3.86 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,733 9,003 4,759 9,429 7,245 8,565 11,655 11,698 Annual Electricity Production MWh 23,715 39,011 19,491 41,922 30,565 36,219 49,012 49,104 12 CO2 Emissions Tons 9,001 14,689 7,051 14,484 11,439 12,987 17,652 17,689 SOx Emissions Tons 1.41 1.92 0.96 1.36 1.54 1.42 1.85 1.85 NOx Emissions Tons 1.29 1.92 0.95 1.65 1.52 1.57 2.10 2.10 PM-10 Emissions Tons 1.32 2.15 1.03 2.12 1.68 1.90 2.58 2.59 Feeder Real Load MW 2,707 4,453 2,225 4,786 3,489 4,135 5,595 5,605 Feeder Reactive Load MVAR 247.51 538.14 240.55 -373.84 405.20 588.64 649.44 640.42 29 Distribution Losses % 0.33 1.19 2.01 4.39 1.92 2.34 1.41 1.31 30 Distribution Power Factor pf 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 39 CO2 Emissions Tons 9,030 14,865 7,196 15,149 11,663 13,298 17,904 17,925 SOx Tons 1.41 1.94 0.98 1.42 1.57 1.45 1.87 1.87 NOx Tons 1.30 1.94 0.97 1.72 1.55 1.61 2.13 2.13 PM-10 Tons 1.33 2.18 1.05 2.22 1.71 1.95 2.62 2.62 3

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## E.4 

## Individual Combined PV Performance M etrics

### 351 Table E. 16 : Combined PV performance m etrics for region 1



### Index Metric UnitsGC-12.47-1 R1R1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

1 Hourly Customer Electricity Usage kWh 2,083 2,632 972 427 1,918 861
2 Monthly Customer Electricity Usage MWh 1,521 1,921 710 312 1,400 628 Peak Generation kW 5,313 7,108 2,652 1,182 4,889 2,422 Nuclear % 10.68 10.68 9.94 10.15 10.75 10.33 Solar % 0.25 0.25 0.21 0.21 0.25 0.24 Bio % 0.67 0.67 0.71 0.72 0.67 0.65 Wind % 4.07 4.07 3.50 3.57 4.10 3.93 Coal % 2.88 2.88 4.32 4.41 2.90 2.78 Hydroelectric % 36.88 36.89 25.93 26.48 37.11 35.66 Natural Gas % 41.38 41.39 50.48 51.55 41.64 40.01 Geothermal % 2.84 0.19 3.06 1.79 0.62 2.75 Petroleum % 0.35 0.00 0.10 0.00 0.00 1.36 Distributed Solar PV % 0.00 2.98 1.75 1.12 1.97 2.30 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 4 Peak Load MW 5,288 6,865 2,529 1,175 4,793 2,286
7 Annual Electricity Production MWh 18,290 23,661 8,790 3,759 17,001 7,649 12 CO2 Emissions Tons 1,783 2,139 772 369 1,691 709 SOx Emissions Tons 0.03 0.03 0.01 0.00 0.03 0.01 NOx Emissions Tons 0.24 0.27 0.10 0.05 0.21 0.10 PM-10 Emissions Tons 0.25 0.30 0.11 0.05 0.24 0.10 Feeder Real Load MW 2,088 2,701 1,003 429 1,941 873 Feeder Reactive Load MVAR 67.75 -286.67 -199.94 10.38 61.25 -70.99 29 Distribution Losses % 0.23 2.55 3.09 0.55 1.19 1.41 30 Distribution Power Factor pf 1.00 0.99 0.97 1.00 1.00 0.97 39 CO2 Emissions Tons 1,787 2,195 797 371 1,711 719 SOx Tons 0.03 0.03 0.01 0.00 0.03 0.01 NOx Tons 0.24 0.28 0.10 0.05 0.22 0.10 PM-10 Tons 0.25 0.31 0.11 0.05 0.24 0.10 3

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### 352 Table E. 17 : Combined PV performance metrics for region 2



### Index Metric UnitsGC-12.47-1 R2R2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

1 Hourly Customer Electricity Usage kWh 2,169 2,254 1,960 2,975 6,302 4,557
2 Monthly Customer Electricity Usage MWh 1,584 1,645 1,431 2,171 4,601 3,327 Peak Generation kW 5,749 6,290 5,802 8,555 16,766 12,215 Nuclear % 26.33 26.22 26.20 27.95 26.23 26.74 Solar % 0.01 0.01 0.01 0.01 0.01 0.01 Bio % 0.82 0.82 0.82 0.84 0.82 0.83 Wind % 1.41 1.40 1.40 1.70 1.40 1.43 Coal % 47.18 46.98 46.96 45.54 47.00 47.91 Hydroelectric % 7.42 7.39 7.38 9.05 7.08 5.87 Natural Gas % 16.33 16.26 16.25 14.47 16.27 16.58 Geothermal % 0.07 0.07 0.07 0.07 0.00 0.00 Petroleum % 0.43 0.06 0.37 0.37 0.00 0.00 Distributed Solar PV % 0.00 0.79 0.53 0.00 1.19 0.62 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,720 6,142 5,644 8,360 16,485 12,262 Annual Electricity Production MWh 19,050 19,997 17,498 26,686 55,743 40,253 12 CO2 Emissions Tons 8,419 9,149 8,333 12,627 26,572 17,322 SOx Emissions Tons 3.81 4.17 3.84 5.82 12.20 7.82 NOx Emissions Tons 2.43 2.65 2.44 3.69 7.73 4.99 PM-10 Emissions Tons 1.25 1.36 1.24 1.87 3.94 2.57 Feeder Real Load MW 2,175 2,283 1,997 3,046 6,363 4,595 Feeder Reactive Load MVAR 91.62 116.11 146.06 -129.98 332.38 68.83 29 Distribution Losses % 0.25 1.27 1.87 2.36 0.96 0.82 30 Distribution Power Factor pf 1.00 1.00 1.00 1.00 1.00 1.00 39 CO2 Emissions Tons 8,440 9,266 8,492 12,932 26,829 17,465 SOx Tons 3.82 4.22 3.92 5.96 12.32 7.88 NOx Tons 2.44 2.68 2.48 3.78 7.81 5.03 PM-10 Tons 1.25 1.37 1.26 1.92 3.98 2.59 3

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### 353 Table E. 18 : Combined PV performance metrics for region 3



### Index Metric UnitsGC-12.47-1 R3R3-12.47-1R3-12.47-2R3-12.47-3

1 Hourly Customer Electricity Usage kWh 2,635 3,523 1,642 3,547
2 Monthly Customer Electricity Usage MWh 1,924 2,572 1,199 2,590 Peak Generation kW 6,594 8,775 4,422 8,081 Nuclear % 8.65 9.74 9.72 9.76 Solar % 0.13 0.13 0.13 0.13 Bio % 0.23 0.25 0.25 0.25 Wind % 2.05 2.45 2.45 2.46 Coal % 40.24 41.59 41.52 41.71 Hydroelectric % 5.58 2.42 6.40 4.51 Natural Gas % 41.67 37.94 37.88 38.05 Geothermal % 1.25 0.00 1.40 0.00 Petroleum % 0.20 0.00 0.25 0.00 Distributed Solar PV % 0.00 5.48 0.00 3.12 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 6,554 8,597 4,364 7,857 Annual Electricity Production MWh 23,160 31,462 14,483 32,198 12 CO2 Emissions Tons 16,269 22,668 9,963 24,162 SOx Emissions Tons 7.03 10.07 4.25 10.88 NOx Emissions Tons 4.38 6.23 2.66 6.70 PM-10 Emissions Tons 2.42 3.38 1.48 3.60 Feeder Real Load MW 2,644 3,592 1,653 3,676 Feeder Reactive Load MVAR 218.51 480.97 142.77 544.25 29 Distribution Losses % 0.33 1.89 0.69 3.49 30 Distribution Power Factor pf 1.00 0.99 1.00 0.99 39 CO2 Emissions Tons 16,323 23,105 10,032 25,035 SOx Tons 7.05 10.26 4.28 11.27 NOx Tons 4.39 6.35 2.67 6.94 PM-10 Tons 2.43 3.44 1.49 3.73 3

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### 354 Table E. 19 : Combined PV performance metrics for region 4



### Index Metric UnitsGC-12.47-1 R4R4-12.47-1R4-12.47-2R4-25.00-1

1 Hourly Customer Electricity Usage kWh 2,339 1,829 797 337
2 Monthly Customer Electricity Usage MWh 1,708 1,335 582 246 Peak Generation kW 6,221 4,488 2,149 860 Nuclear % 21.91 22.10 21.39 24.12 Solar % 0.00 0.00 0.00 0.00 Bio % 0.18 0.18 0.18 0.21 Wind % 0.60 0.61 0.59 0.60 Coal % 57.14 57.65 55.78 57.33 Hydroelectric % 2.20 0.00 0.00 0.00 Natural Gas % 17.49 14.67 15.82 12.94 Geothermal % 0.00 0.00 0.00 0.00 Petroleum % 0.48 0.00 0.00 0.00 Distributed Solar PV % 0.00 4.80 6.25 4.79 Distributed Wind % 0.00 0.00 0.00 0.00 Peak Load MW 6,186 4,431 2,064 863 Annual Electricity Production MWh 20,550 16,491 7,156 3,028 12 CO2 Emissions Tons 10,321 9,162 3,692 1,517 SOx Emissions Tons 4.91 4.41 1.77 0.73 NOx Emissions Tons 3.00 2.68 1.08 0.44 PM-10 Emissions Tons 1.54 1.37 0.55 0.23 Feeder Real Load MW 2,346 1,883 817 346 Feeder Reactive Load MVAR 137.98 -414.39 97.59 44.70 29 Distribution Losses % 0.28 2.85 2.40 2.59 30 Distribution Power Factor pf 1.00 0.96 0.99 0.99 39 CO2 Emissions Tons 10,350 9,431 3,782 1,557 SOx Tons 4.93 4.54 1.82 0.75 NOx Tons 3.00 2.76 1.11 0.46 PM-10 Tons 1.54 1.41 0.56 0.23 3

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### 355 Table E. 20 : Combined PV performance metrics for region 5



### Index Metric UnitsGC-12.47-1 R5R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

1 Hourly Customer Electricity Usage kWh 2,747 4,313 2,133 4,480 3,353 3,952 5,386 5,410
2 Monthly Customer Electricity Usage MWh 2,005 3,148 1,557 3,271 2,447 2,885 3,932 3,949 Peak Generation kW 5,841 8,898 5,014 9,786 7,263 8,486 11,554 11,480 Nuclear % 13.85 13.82 13.00 13.52 13.63 13.56 13.41 13.86 Solar % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bio % 0.33 0.33 0.30 0.31 0.32 0.31 0.31 0.33 Wind % 1.48 1.48 1.67 1.74 1.46 1.74 1.72 1.48 Coal % 30.17 26.58 29.17 27.29 27.17 27.24 27.89 25.12 Hydroelectric % 0.63 0.00 0.75 0.00 0.00 0.00 0.00 0.00 Natural Gas % 51.68 51.59 49.26 51.25 50.87 51.39 50.82 51.72 Geothermal % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Petroleum % 1.86 0.00 2.69 0.00 0.00 0.00 0.00 0.00 Distributed Solar PV % 0.00 6.20 3.16 5.89 6.54 5.76 5.85 7.49 Distributed Wind % 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Peak Load MW 5,810 8,749 4,671 9,168 6,992 8,247 11,255 11,331 Annual Electricity Production MWh 24,144 38,232 19,065 41,035 29,943 35,446 47,857 48,026 12 CO2 Emissions Tons 9,364 13,992 6,686 13,778 10,891 12,341 16,683 16,783 SOx Emissions Tons 1.55 1.63 0.81 1.09 1.31 1.16 1.47 1.50 NOx Emissions Tons 1.38 1.74 0.86 1.48 1.38 1.41 1.86 1.88 PM-10 Emissions Tons 1.37 2.05 0.98 2.01 1.60 1.81 2.44 2.46 Feeder Real Load MW 2,756 4,364 2,176 4,684 3,418 4,046 5,463 5,482 Feeder Reactive Load MVAR 247.93 535.92 239.07 -390.03 404.02 583.07 648.06 639.82 29 Distribution Losses % 0.33 1.19 1.98 4.36 1.92 2.34 1.41 1.32 30 Distribution Power Factor pf 1.00 0.99 0.99 0.98 0.99 0.99 0.99 0.99 39 CO2 Emissions Tons 9,395 14,160 6,821 14,406 11,104 12,636 16,921 17,007 SOx Tons 1.55 1.65 0.83 1.14 1.34 1.19 1.49 1.52 NOx Tons 1.39 1.76 0.87 1.54 1.40 1.44 1.89 1.90 PM-10 Tons 1.38 2.07 1.00 2.11 1.63 1.85 2.48 2.49 3

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## E.5 Individual Commercial WTG Performance Metrics

### 356 Table E. 21 : Commercial WTG performance metrics for region 1



### Index Metric UnitsGC-12.47-1 R1

1 Hourly Customer Electricity Usage kWh 1,617
2 Monthly Customer Electricity Usage MWh 1,180 Peak Generation kW 5,258 Nuclear % 10.43 Solar % 0.24 Bio % 0.65 Wind % 3.98 Coal % 2.68 Hydroelectric % 36.02 Natural Gas % 40.42 Geothermal % 0.00 Petroleum % 0.00 Distributed Solar PV % 0.00 Distributed Wind % 5.57 4 Peak Load MW 5,111
7 Annual Electricity Production MWh 14,200 12 CO2 Emissions Tons 1,069 SOx Emissions Tons 0.01 NOx Emissions Tons 0.18 PM-10 Emissions Tons 0.15 Feeder Real Load MW 1620.95 Feeder Reactive Load MVAR -126.92 29 Distribution Losses % 0.24 30 Distribution Power Factor pf 0.95 39 CO2 Emissions Tons 1,072 SOx Tons 0.01 NOx Tons 0.18 PM-10 Tons 0.15 3

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### 357 Table E. 22 : Commercial WTG performance metrics for region 2



### Index Metric UnitsGC-12.47-1 R2

1 Hourly Customer Electricity Usage kWh 1,718
2 Monthly Customer Electricity Usage MWh 1,254 Peak Generation kW 5,460 Nuclear % 26.61 Solar % 0.01 Bio % 0.83 Wind % 1.42 Coal % 47.67 Hydroelectric % 3.93 Natural Gas % 16.50 Geothermal % 0.00 Petroleum % 0.00 Distributed Solar PV % 0.00 Distributed Wind % 3.03 Peak Load MW 5,488 Annual Electricity Production MWh 15,094 12 CO2 Emissions Tons 5,939 SOx Emissions Tons 2.72 NOx Emissions Tons 1.74 PM-10 Emissions Tons 0.88 Feeder Real Load MW 1,723 Feeder Reactive Load MVAR -96.54 29 Distribution Losses % 0.26 30 Distribution Power Factor pf 0.93 39 CO2 Emissions Tons 5,954 SOx Tons 2.72 NOx Tons 1.74 PM-10 Tons 0.88 3

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### 358 Table E. 23 : Commercial WTG performance metrics for region 3



### Index Metric UnitsGC-12.47-1 R3

1 Hourly Customer Electricity Usage kWh 2,511
2 Monthly Customer Electricity Usage MWh 1,833 Peak Generation kW 6,519 Nuclear % 9.76 Solar % 0.13 Bio % 0.25 Wind % 2.46 Coal % 41.71 Hydroelectric % 6.43 Natural Gas % 38.05 Geothermal % 1.07 Petroleum % 0.00 Distributed Solar PV % 0.00 Distributed Wind % 0.12 Peak Load MW 6,510 Annual Electricity Production MWh 22,065 12 CO2 Emissions Tons 15,425 SOx Emissions Tons 6.73 NOx Emissions Tons 4.18 PM-10 Emissions Tons 2.30 Feeder Real Load MW 2,519 Feeder Reactive Load MVAR 166.36 29 Distribution Losses % 0.33 30 Distribution Power Factor pf 1.00 39 CO2 Emissions Tons 15,476 SOx Tons 6.75 NOx Tons 4.20 PM-10 Tons 2.30 3

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### 359 Table E. 24 : Commercial WTG performance metrics for region 4



### Index Metric UnitsGC-12.47-1 R4

1 Hourly Customer Electricity Usage kWh 2,066
2 Monthly Customer Electricity Usage MWh 1,508 Peak Generation kW 6,564 Nuclear % 22.43 Solar % 0.00 Bio % 0.20 Wind % 0.56 Coal % 53.33 Hydroelectric % 2.94 Natural Gas % 15.35 Geothermal % 0.00 Petroleum % 1.88 Distributed Solar PV % 0.00 Distributed Wind % 3.31 Peak Load MW 6,210 Annual Electricity Production MWh 18,147 12 CO2 Emissions Tons 8,528 SOx Emissions Tons 4.08 NOx Emissions Tons 2.49 PM-10 Emissions Tons 1.27 Feeder Real Load MW 2,072 Feeder Reactive Load MVAR 23.58 29 Distribution Losses % 0.29 30 Distribution Power Factor pf 0.97 39 CO2 Emissions Tons 8553.05 SOx Tons 4.09 NOx Tons 2.49 PM-10 Tons 1.28 3

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### 360 Table E. 25 : Commercial WTG performance metrics for region 5



### Index Metric UnitsGC-12.47-1 R5

1 Hourly Customer Electricity Usage kWh 2,345
2 Monthly Customer Electricity Usage MWh 1,712 Peak Generation kW 5,784 Nuclear % 13.54 Solar % 0.00 Bio % 0.32 Wind % 1.45 Coal % 28.83 Hydroelectric % 0.00 Natural Gas % 50.53 Geothermal % 0.00 Petroleum % 0.00 Distributed Solar PV % 0.00 Distributed Wind % 5.33 Peak Load MW 5,624 Annual Electricity Production MWh 20,610 12 CO2 Emissions Tons 7,022 SOx Emissions Tons 0.81 NOx Emissions Tons 0.88 PM-10 Emissions Tons 1.03 Feeder Real Load MW 2,353 Feeder Reactive Load MVAR 79.49 29 Distribution Losses % 0.33 30 Distribution Power Factor pf 0.98 39 CO2 Emissions Tons 7,045 SOx Tons 0.81 NOx Tons 0.88 PM-10 Tons 1.03 3

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### 361 

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