PNNL-20792

Prepared for the U.S. Department of Energy

under Contract DE-AC05-76RL01830

Evaluation of Representative Smart 

Grid Investment Grant Project 

Technologies: Distributed 

Generation

R Singh

B Vyakaranam

February 2012
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 (9/2003)
PNNL-20792

Evaluation of Smart Grid 

Investment Grant Project 

Technologies: Distributed 

Generation

R Singh

B Vyakaranam

February 2012 

Prepared for the U.S. Department of Energy 

under Contract DE-AC05-76RL01830 

Pacific 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 TECHNOLOGIES..........................................................................................3

1.1.2 ENABLING TECHNOLOGIES.......................................................................................................................4

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

1.1.2.2 COMMUNICATIONS INFRASTRUCTURE ..............................................................................................5

1.1.2.3 HUMAN MACHINE INTERFACE..............................................................................................................5

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

2 DISTRIBUTED GENERATION TECHNOLOGY AREAS..................................................................................6

2.1 PHOTOVOLTAIC (PV) MODEL IMPLEMENTATION ..................................................................................7

2.1.1 RESIDENTIAL PVS ........................................................................................................................................8

2.1.1.1 SGIG IMPACT METRICS AFFECTED BY RESIDENTIAL PVS .............................................................9

2.1.1.2 SPECIFIC IMPLEMENTATION OF RESIDENTIAL PVS.......................................................................10

2.1.1.3 OVERALL RESIDENTIAL PVS SIMULATION RESULTS....................................................................11

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

2.1.1.5 ANNUAL ENERGY CONSUMPTION......................................................................................................14

2.1.1.6 ANNUAL SYSTEM LOSSES.....................................................................................................................17

2.1.1.7 POWER FACTOR IMPACT.......................................................................................................................19

2.1.1.8 ANNUAL RESIDENTIAL PV OUTPUT...................................................................................................21

2.1.1.9 ANNUAL CO2 EMISSIONS.......................................................................................................................22

2.1.2 COMMERCIAL PVS .....................................................................................................................................25

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

2.1.2.2 SPECIFIC IMPLEMENTATION OF COMMERCIAL PV........................................................................26

2.1.2.3 HIGH LEVEL COMMERCIAL PV SIMULATION RESULTS................................................................26

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

2.1.2.5 ANNUAL ENERGY CONSUMPTION......................................................................................................29

2.1.2.6 ANNUAL SYSTEM LOSSES.....................................................................................................................31

2.1.2.7 POWER FACTOR IMPACT.......................................................................................................................33

2.1.2.8 ANNUAL COMMERCIAL PV OUTPUT..................................................................................................34

2.1.2.9 ANNUAL CO2 EMISSIONS.......................................................................................................................35

2.1.3 ADDITION OF BOTH COMMERCIAL AND RESIDENTIAL (COMBINED) PVS..................................37

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

2.1.3.2 SPECIFIC IMPLEMENTATION OF COMBINED PV..............................................................................38

2.1.3.3 HIGH LEVEL COMBINED PV SIMULATION RESULTS......................................................................39

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

2.1.3.5 ANNUAL ENERGY CONSUMPTION......................................................................................................42

2.1.3.6 ANNUAL SYSTEM LOSSES.....................................................................................................................44

2.1.3.7 POWER FACTOR IMPACT.......................................................................................................................46

2.1.3.8 ANNUAL COMBINED PV OUTPUT........................................................................................................47
iv

2.1.3.9 ANNUAL CO2 EMISSIONS.......................................................................................................................48

2.2 ADDITION OF COMMERCIAL WTG............................................................................................................50

2.2.1 SGIG METRICS AFFECTED BY COMMERCIAL WTG ...........................................................................51

2.2.2 SPECIFIC IMPLEMENTATION OF COMMERCIAL WTG.......................................................................51

2.2.3 HIGH LEVEL COMMERCIAL WTG RESULTS.........................................................................................52

2.2.3.1 ANNUAL PEAK LOAD .............................................................................................................................53

2.2.3.2 ANNUAL ENERGY CONSUMPTION......................................................................................................55

2.2.3.3 ANNUAL SYSTEM LOSSES.....................................................................................................................57

2.2.3.4 ANNUAL WIND POWER OUTPUT .........................................................................................................59

2.2.3.5 ANNUAL CO2 EMISSIONS.......................................................................................................................59

3 DETAILED INDIVIDUAL PROTOTYPICAL FEEDER 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 IMPACT METRICS .........................................................................................................82

4.2 COMMERCIAL PV IMPACT METRICS ........................................................................................................88

4.3 COMBINED PV IMPACT METRICS..............................................................................................................93

4.4 COMMERCIAL WTG IMPACT METRICS ....................................................................................................98

5 CONCLUSION AND OBSERVATIONS..........................................................................................................103

5.1 DISTRIBUTED GENERATION OBSERVATIONS AND CONCLUSIONS ...............................................103

5.2 DISTRIBUTED GENERATION OBSERVATIONS AND CONCLUSIONS SUMMARY..........................104

5.2.1 OBSERVATIONS AND CONCLUSIONS SUMMARY FOR PV INTEGRATION..................................105

5.2.2 OBSERVATIONS AND CONCLUSIONS SUMMARY FOR WTG INTEGRATION..............................106

APPENDIX A: SGIG PROGRAM IMPACT METRICS......................................................................................107

APPENDIX B: TAXONOMY OF PROTOTYPICAL DISTRIBUTION FEEDERS...........................................116

B.1 END-USE LOAD MODELS..................................................................................................................................117

B.1.1 ZIP LOADS ....................................................................................................................................................118

B.1.2 SINGLE-STATE DETAILED PHYSICAL MODELS ..............................................................................................120

B.1.3 MULTI-STATE DETAILED PHYSICAL MODELS ...............................................................................................123

B.2 MODEL EXTRACTION AND POPULATION ..........................................................................................................126

B.2.1 RESIDENTIAL LOADS.....................................................................................................................................126

B.2.2 COMMERCIAL LOADS ....................................................................................................................................134

B.3 TAXONOMY FEEDER EMISSION PROFILES ........................................................................................................140

B.4 TAXONOMY FEEDER DESCRIPTIONS.................................................................................................................145

B.4.1 FEEDER 1: GC-12.47-1..................................................................................................................................145

B.4.2 FEEDER 2: R1-12.47-1...................................................................................................................................146

B.4.3 FEEDER 3: R1-12.47-2...................................................................................................................................146
v

B.4.4 FEEDER 4: R1-12.47-3...................................................................................................................................146

B.4.5 FEEDER 5: R1-12.47-4...................................................................................................................................146

B.4.6 FEEDER 6: R1-25.00-1...................................................................................................................................147

B.4.7 FEEDER 7: R2-12.47-1...................................................................................................................................147

B.4.8 FEEDER 8: R2-12.47-2...................................................................................................................................147

B.4.9 FEEDER 9: R2-12.47-3...................................................................................................................................147

B.4.10 FEEDER 10: R2-25.00-1...............................................................................................................................148

B.4.11 FEEDER 11: R2-35.00-1...............................................................................................................................148

B.4.12 FEEDER 12: R3-12.47-1...............................................................................................................................148

B.4.13 FEEDER 13: R3-12.47-2...............................................................................................................................148

B.4.14 FEEDER 14: R3-12.47-3...............................................................................................................................149

B.4.15 FEEDER 15: R4-12.47-1...............................................................................................................................149

B.4.16 FEEDER 16: R4-12.47-2...............................................................................................................................149

B.4.17 FEEDER 17: R4-25.00-1...............................................................................................................................149

B.4.18 FEEDER 18: R5-12.47-1...............................................................................................................................150

B.4.19 FEEDER 19: R5-12.47-2...............................................................................................................................150

B.4.20 FEEDER 20: R5-12.47-3...............................................................................................................................150

B.4.21 FEEDER 21: R5-12.47-4...............................................................................................................................150

B.4.22 FEEDER 22: R5-12.47-5...............................................................................................................................151

B.4.23 FEEDER 23: R5-25.00-1...............................................................................................................................151

B.4.24 FEEDER 24: R5-35.00-1...............................................................................................................................151

APPENDIX C: SIMULATION TECHNOLOGY AND METHODOLOGY........................................................152

APPENDIX D: PLOTS FOR INDIVIDUAL FEEDER RESULTS ......................................................................155

D.1 ADDITION OF RESIDENTIAL PVS PLOTS............................................................................................................155

D.1.1 DETAILED RESIDENTIAL PV PLOTS FOR R1-12.47-1.....................................................................................155

D.1.2 DETAILED RESIDENTIAL PV PLOTS FOR R1-12.47-2.....................................................................................155

D.1.3 DETAILED RESIDENTIAL PV PLOTS FOR R1-12.47-3.....................................................................................158

D.1.4 DETAILED RESIDENTIAL PV PLOTS FOR R1-12.47-4.....................................................................................160

D.1.5 DETAILED RESIDENTIAL PV PLOTS FOR R1-25.00-1.....................................................................................163

D.1.6 DETAILED RESIDENTIAL PV PLOTS FOR R2-12.47-1.....................................................................................165

D.1.7 DETAILED RESIDENTIAL PV PLOTS FOR R2-12.47-2.....................................................................................168

D.1.8 DETAILED RESIDENTIAL PV PLOTS FOR R2-12.47-3.....................................................................................170

D.1.9 DETAILED RESIDENTIAL PV PLOTS FOR R2-25.00-1.....................................................................................173

D.1.10 DETAILED RESIDENTIAL PV PLOTS FOR R2-35.00-1...................................................................................175

D.1.11 DETAILED RESIDENTIAL PV PLOTS FOR R3-12.47-1...................................................................................178

D.1.12 DETAILED RESIDENTIAL PV PLOTS FOR R3-12.47-3...................................................................................181

D.1.13 DETAILED RESIDENTIAL PV PLOTS FOR R4-12.47-1...................................................................................183

D.1.14 DETAILED RESIDENTIAL PV PLOTS FOR R4-12.47-2...................................................................................186

D.1.15 DETAILED RESIDENTIAL PV PLOTS FOR R4-25.00-1...................................................................................188

D.1.16 DETAILED RESIDENTIAL PV PLOTS FOR R5-12.47-1...................................................................................191

D.1.17 DETAILED RESIDENTIAL PV PLOTS FOR R5-12.47-2...................................................................................194

D.1.18 DETAILED RESIDENTIAL PV PLOTS FOR R5-12.47-3...................................................................................196

D.1.19 DETAILED RESIDENTIAL PV PLOTS FOR R5-12.47-4...................................................................................199

D.1.20 DETAILED RESIDENTIAL PV PLOTS FOR R5-12.47-5...................................................................................201

D.1.21 DETAILED RESIDENTIAL PV PLOTS FOR R5-25.00-1...................................................................................204

D.1.22 DETAILED RESIDENTIAL PV PLOTS FOR R5-35.00-1...................................................................................206
vi

D.2 ADDITION OF COMMERCIAL PV PLOTS ............................................................................................................209

D.2.1 DETAILED COMMERCIAL PV PLOTS FOR GC-12.47-1_R1 ............................................................................209

D.2.2 DETAILED COMMERCIAL PV PLOTS FOR R1-12.47-1....................................................................................209

D.2.3 DETAILED COMMERCIAL PV PLOTS FOR R1-12.47-2....................................................................................212

D.2.4 DETAILED COMMERCIAL PV PLOTS FOR R1-12.47-3....................................................................................214

D.2.5 DETAILED COMMERCIAL PV PLOTS FOR R1-12.47-4....................................................................................217

D.2.6 DETAILED COMMERCIAL PV PLOTS FOR R1-25.00-1....................................................................................219

D.2.7 DETAILED COMMERCIAL PV PLOTS FOR GC-12.47-1_R2 ............................................................................222

D.2.8 DETAILED COMMERCIAL PV FOR R2-12.47-1...............................................................................................224

D.2.9 DETAILED COMMERCIAL PV FOR R2-12.47-2...............................................................................................227

D.2.10 DETAILED COMMERCIAL PV FOR R2-25.00-1.............................................................................................229

D.2.11 DETAILED COMMERCIAL PV FOR R2-35.00-1.............................................................................................232

D.2.12 DETAILED COMMERCIAL PV FOR GC-12.47-1_R3 .....................................................................................234

D.2.13 DETAILED COMMERCIAL PV PLOTS FOR R3-12.47-1..................................................................................237

D.2.14 DETAILED COMMERCIAL PV PLOTS FOR R3-12.47-2..................................................................................239

D.2.15 DETAILED COMMERCIAL PV PLOTS FOR R3-12.47-3..................................................................................242

D.2.16 DETAILED COMMERCIAL PV PLOTS FOR GC-12.47-1_R4 ..........................................................................245

D.2.17 DETAILED COMMERCIAL PV PLOTS FOR R4-12.47-1..................................................................................247

D.2.18 DETAILED COMMERCIAL PV PLOTS FOR R4-12.47-2..................................................................................250

D.2.19 DETAILED COMMERCIAL PV PLOTS FOR R4-25.00-1..................................................................................253

D.2.20 DETAILED COMMERCIAL PV PLOTS FOR GC-12.47-1_R5 ..........................................................................256

D.2.21 DETAILED COMMERCIAL PV PLOTS FOR R5-12.47-1..................................................................................258

D.2.22 DETAILED COMMERCIAL PV PLOTS FOR R5-12.47-2..................................................................................261

D.2.23 DETAILED COMMERCIAL PV PLOTS FOR R5-12.47-3..................................................................................263

D.2.24 DETAILED COMMERCIAL PV PLOTS FOR R5-12.47-4..................................................................................266

D.2.25 DETAILED COMMERCIAL PV PLOTS FOR R5-12.47-5..................................................................................268

D.2.26 DETAILED COMMERCIAL PV PLOTS FOR R5-25.00-1..................................................................................271

D.2.27 DETAILED COMMERCIAL PV PLOTS FOR R5-35.00-1..................................................................................274

D.3 ADDITION OF COMBINED PV............................................................................................................................276

D.3.1 DETAILED COMBINED PV PLOTS FOR R1-12.47-1 ........................................................................................276

D.3.2 DETAILED COMBINED PV PLOTS FOR R1-12.47-2 ........................................................................................278

D.3.3 DETAILED COMBINED PV PLOTS FOR R1-12.47-3 ........................................................................................281

D.3.4 DETAILED COMBINED PV PLOTS FOR R1-12.47-4 ........................................................................................283

D.3.5 DETAILED COMBINED PV PLOTS FOR R1-25.00-1 ........................................................................................286

D.3.6 DETAILED COMBINED PV PLOTS FOR R2-12.47-1 ........................................................................................288

D.3.7 DETAILED COMBINED PV PLOTS FOR R2-12.47-2 ........................................................................................291

D.3.8 DETAILED COMBINED PV PLOTS FOR R2-25.00-1 ........................................................................................293

D.3.9 DETAILED COMBINED PV PLOTS FOR R2-35.00-1 ........................................................................................296

D.3.10 DETAILED COMBINED PV PLOTS FOR R3-12.47-1 ......................................................................................298

D.3.11 DETAILED COMBINED PV PLOTS FOR R3-12.47-3 ......................................................................................301

D.3.12 DETAILED COMBINED PV PLOTS FOR R4-12.47-1 ......................................................................................303

D.3.13 DETAILED COMBINED PV PLOTS FOR R4-12.47-2 ......................................................................................306

D.3.14 DETAILED COMBINED PV PLOTS FOR R4-25.00-1 ......................................................................................308

D.3.15 DETAILED COMBINED PV PLOTS FOR R5-12.47-1 ......................................................................................311

D.3.16 DETAILED COMBINED PV PLOTS FOR R5-12.47-2 ......................................................................................313

D.3.17 DETAILED COMBINED PV PLOTS FOR R5-12.47-3 ......................................................................................316

D.3.18 DETAILED COMBINED PV PLOTS FOR R5-12.47-4 ......................................................................................318

D.3.19 DETAILED COMBINED PV PLOTS FOR R5-12.47-5 ......................................................................................321
vii

D.3.20 DETAILED COMBINED PV PLOTS FOR R5-25.00-1 ......................................................................................323

D.3.21 DETAILED COMBINED PV PLOTS FOR R5-35.00-1 ......................................................................................326

D.4 ADDITION OF COMMERCIAL WTG...................................................................................................................326

D.4.1 DETAILED COMMERCIAL WTG PLOTS FOR GC-12.47-1_R1.........................................................................326

D.4.2 DETAILED COMMERCIAL WTG PLOTS FOR GC-12.47-1_R2.........................................................................329

D.4.3 DETAILED COMMERCIAL WTG PLOTS FOR GC-12.47-1_R3.........................................................................329

D.4.4 DETAILED COMMERCIAL WTG PLOTS FOR GC-12.47-1_R4.........................................................................331

D.4.5 DETAILED COMMERCIAL WTG PLOTS FOR GC-12.47-1_R5.........................................................................334

APPENDIX E: INDIVIDUAL FEEDER IMPACT METRICS ............................................................................335

E.1 INDIVIDUAL PERFORMANCE METRICS FOR BASE CASE ....................................................................................335

E.2 INDIVIDUAL RESIDENTIAL PV PERFORMANCE METRICS ..................................................................................341

E.3 INDIVIDUAL COMMERCIAL PV PERFORMANCE METRICS .................................................................................345

E.4 INDIVIDUAL COMBINED PV PERFORMANCE METRICS......................................................................................350

E.5 INDIVIDUAL COMMERCIAL WTG PERFORMANCE METRICS .............................................................................355

REFERENCES ......................................................................................................................................................361
viii

Table of Figures

Figure 2.1. Climate Zones Used for Development of Prototypical Feeders.............................................................10

Figure 2.2: Comparison of 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 annual 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 annual power factor by feeder...................................................................21

Figure 2.13: Annual residential PV power output by feeder....................................................................................22

Figure 2.14: Comparison of total annual CO2 emission by feeder...........................................................................23

Figure 2.15: Change in total annual CO2 emissions by feeder (tons) ......................................................................24

Figure 2.16: Change in total annual CO2 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 annual power factor by feeders...................................................................34

Figure 2.28: Annual commercial PV output by feeder ............................................................................................35

Figure 2.29 : Change in total annual CO2 emissions by feeder (kilotons) ...............................................................36

Figure 2.30: Change in total annual CO2 emissions by feeder (tons) ......................................................................36

Figure 2.31: Change in total annual CO2 emissions by feeder (%)..........................................................................37

Figure 2.32 Climate Zones 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 feeder (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 CO2 emission by feeder...................................................................................49

Figure 2.46: Change in annual CO2 emission by feeder (kilotons)..........................................................................49
ix

Figure 2.47: Change in annual CO2 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 CO2 emission by feeders................................................................................60

Figure 2.59 : Change in annual CO2 emission by feeders (tons)..............................................................................60

Figure 2.60 : Change in annual CO2 emission by feeders (%).................................................................................61

Figure 3.1: Comparison of peak load by month for R1-12.47-1..............................................................................63

Figure 3.2: Comparison of energy consumption by month for R1-12.47-1.............................................................64

Figure 3.3: Comparison of losses by month for R1-12.47-1....................................................................................65

Figure 3.4: Comparison of PV output by month for R1-12.47-1 .............................................................................66

Figure 3.5: Comparison of CO2 emissions by month for R1-12.47-1......................................................................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-12.47-1_R1 .....................................................69

Figure 3.8: Comparison of losses by month for GC-12.47-1_R1 ............................................................................70

Figure 3.9: PV output by month for GC-12.47-1_R1 ..............................................................................................71

Figure 3.10: Comparison of CO2 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 CO2 emissions by month for R5-35.00-1....................................................................77

Figure 3.16: Comparison of peak load by month for GC-12.47-1_R2 ....................................................................78

Figure 3.17: Comparison of energy consumption by month for GC-12.47-1_R2 ...................................................78

Figure 3.18: Comparison of losses by month for GC-12.47-1_R2 ..........................................................................79

Figure 3.19: Comparison of WTG output by month for GC-12.47-1_R2................................................................80

Figure 3.20: Comparison of CO2 by month for GC-12.47-1_R2 .............................................................................81

Figure B.1: Climate Zones Used for Development of Prototypical Feeders..........................................................116

Figure B.2: Voltage dependent 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 R1-12.47-2..........................................................131

Figure B.7: Average energy consumption of 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 zonal 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 R1-12.47-2 ...........................................................................155

Figure D.2: Comparison of energy consumption by month for R1-12.47-2 ..........................................................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 CO2 emissions by month for R1-12.47-2...................................................................157

Figure D.6: Comparison of peak load by month for R1-12.47-3 ...........................................................................158

Figure D.7: Comparison of energy consumption by month for R1-12.47-3 ..........................................................158

Figure D.8: Comparison of losses by month for R1-12.47-3.................................................................................159

Figure D.9: Comparison of PV output by month for R1-12.47-3 ..........................................................................159

Figure D.10: Comparison of CO2 emissions by month for R1-12.47-3.................................................................160

Figure D.11: Comparison of peak load by month for R1-12.47 ............................................................................160

Figure D.12: Comparison of energy consumption by month for R1-12.47-4 ........................................................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 CO2 emissions by month for R1-12.47-4.................................................................162

Figure D.16: Comparison of peak load 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 CO2 emissions by month for R1-25.00-1.................................................................165

Figure D.21: Comparison of peak load by month for R2-12.47-1 .........................................................................165

Figure D.22: Comparison of energy consumption by month for R2-12.47-1 ........................................................166

Figure D.23: Comparison of losses by month for R2-12.47-1...............................................................................166

Figure D.24: PV output by month for R2-12.47-1.................................................................................................167

Figure D.25: Comparison of CO2 emissions by month for R2-12.47-1.................................................................167

Figure D.26: Comparison of peak load by month for R2-12.47-2 .........................................................................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 CO2 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 R2-12.47-1.................................................................................................172

Figure D.35: Comparison of CO2 emissions by month for R2-12.47-3.................................................................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: Comparison 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 CO2 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: Comparison 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 CO2 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: Comparison of losses by month for R3-12.47-1...............................................................................179

Figure D.49: PV output by month for R3-12.47-1.................................................................................................180

Figure D.50: Comparison of CO2 emissions by month for R3-12.47-1.................................................................180

Figure D.51: Comparison of peak load by month for R3-12.47-3 .........................................................................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 R3-12.47-3.................................................................................................182

Figure D.55: Comparison of CO2 emissions by month for R3-12.47-3.................................................................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 CO2 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 CO2 emissions by month for R3-12.47-1.................................................................188

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 CO2 emissions by month 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 CO2 emissions by month 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 CO2 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 CO2 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: Comparison 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 CO2 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 CO2 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 CO2 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: Comparison 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 CO2 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 of 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 CO2 by month for R1-12.47-1................................................................................211

Figure D.111: Comparison 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 CO2 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 CO2 by month for R1-12.47-3................................................................................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 CO2 by month for R1-12.47-4................................................................................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 output by month for R1-25.00-1...............................................................................................221

Figure D.130: Comparison of CO2 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 output by month for GC-12.47-1_R2 .......................................................................................223

Figure D.135: Comparison of CO2 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 CO2 emissions by month for R2-12.47-1...............................................................226

Figure D.141: Comparison of peak load by month for R2-12.47-2 .......................................................................227

Figure D.142: Comparison of energy consumption by month for R2-12.47-2 ......................................................227

Figure D.143: Comparison 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 CO2 emissions by month for R2-12.47-2...............................................................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: Comparison 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 CO2 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: Comparison 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 CO2 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: Comparison 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 CO2 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 R3-12.47-1 ......................................................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 CO2 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 CO2 emissions by month 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 CO2 emissions by month 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 CO2 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 output by month for R4-12.47-1...............................................................................................249

Figure D.185: Comparison of CO2 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: Comparison 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 CO2 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: Comparison 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 CO2 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 CO2 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: Comparison 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 CO2 emissions by month for R5-12.47-1...............................................................260

Figure D.206: Comparison of peak load by month for R5-12.47-2 .......................................................................261

Figure D.207: Comparison of 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 CO2 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 CO2 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 CO2 emissions by month 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 CO2 emissions by month 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: Comparison of CO2 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 CO2 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.............................................................................277

Figure D.237: PV output by month for R1-12.47-1...............................................................................................277

Figure D.238: Comparison of CO2 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 CO2 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 CO2 emissions 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 output by month for R1-12.47-4...............................................................................................285

Figure D.253: Comparison of CO2 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: Comparison 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 CO2 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: Comparison 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 CO2 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 CO2 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 of 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 CO2 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 of 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 CO2 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 CO2 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 CO2 emissions by month 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 CO2 emissions by month 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 output by month for R4-25.00-1...............................................................................................310

Figure D.303: Comparison of CO2 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 output by month for R5-12.47-1...............................................................................................312

Figure D.308: Comparison of CO2 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: Comparison 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 CO2 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: Comparison 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 CO2 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: Comparison of losses by month for R5-12.47-4.............................................................................319

Figure D.322: PV output by month for R5-12.47-4...............................................................................................320

Figure D.323: Comparison of CO2 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 CO2 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 CO2 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: Comparison 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 CO2 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 CO2 emissions by month for GC-12.47-1_R3 .......................................................331

Figure D.344: Comparison of 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 CO2 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 CO2 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 region.......................................................................................................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: Residential 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 region 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 region 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 for 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 region 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 in 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

Table 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.............................................................................349

Table E.15: Commercial PV performance metrics for region 5.............................................................................350

Table E.16: Combined PV performance 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 metrics 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 performance 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 to 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. While 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 from 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 Introduction

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 recipients 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 has 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 technologies. 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 

structure 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 5th report uses the results of the four technical reports to 

generate a policy level examination of the various technologies. 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 available 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 interested 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 infrastructure 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 that 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 

technology. 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 they 

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. 

Within 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 Management 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: TOU 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 network. 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 infrastructure, 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 infinite 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 Interfaces (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 increased. 

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 

representative 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 the 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 Gas 

Foundation concluded that this number could be as high as 30% [5]. Distributed Generation 

(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 service meter. A review of the 

SGIG projects indicated that the inclusion of DG was in a relatively 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 continual 

shift towards increased penetration level of renewable resources in the power grid [6]. Currently, 

there are 24 states which have RPS policies in place and these 24 states account for more than 

half the sales of electricity in the US [6]. There are numerous advantages to integrating 

renewable resources. For utilities, it might mean less dependence on purchasing high cost power 

during peak periods from spot markets, increasing their generation mix options, taking advantage 

of subsidies by increasing their renewable portfolio, or increasing the reliability of the power 

grid. For customers, having renewable resources supplying their energy needs means the 

potential for smaller electricity bills and the potential to reduce their carbon footprint. The 

addition 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 renewable resources presents 

significant challenges and opportunities.

In the United States, the most commonly 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 commercial PV deployment. It was 

also observed that some of the utilities are deploying PV units and WTGs as their own utilitywide initiative. This report examines the effects, at the distribution level, of adding residential

scale PVs, commercial scale PVs, combined residential and commercial scale PVs, and 

commercial level wind turbine generator deployment.

The studies 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 impact 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 generator 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, giving insight into the effect of large scale wind integration on the distribution 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 their specific implementations. Section 2.2 will examine the technology associated with 

WTGs and its specific 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 systems with name plate values of 

600 Volts and 200 Watts is used for all residential, commercial, and industrial applications. For 

the SGIG analysis, the PVs are assumed to be made of single crystalline silicon material with an 

efficiency factor of 0.2 [10]. The number of modules in these applications 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:

PDC : DC power output of the solar panel (kW)

PSTC: DC power output of the solar panel under standard test conditions (kW)

GSTC: Solar irradiance at STC of 1000 W/m2

TC: nominal terminal voltage (°C)

TSTC: STC temperature of the solar cell, 25 °C

CT: power temperature coefficient (for single crystal silicon = -0.00437/K)

Ta: 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 given 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 

[ ] C STC T

STC

A

DC STC T T C

G

G P = P * * 1+ ( − ) (2.1)

800 /

( 20 ) 2 W m

NOCT C T T G

o

C a A

− = + (2.2)
8

the time since rainfall. For 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 kept 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 this 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 constant 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 residential PV unit ranges

from 3kW-5kW and can be easily mounted on roof-tops of residential end-users [10] [11].

While there have been numerous studies of PV integration to the grid, limited work has been 

done to analyze the effects at the distribution level of residential PV for different climate regions 

within the United States [12][13]. It is necessary to analyze the impact 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 adding residential PVs to the grid can be examined on 

the relevant prototypical feeders. Utilities, regulators, vendors and other stakeholders interested 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 metrics 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, residential 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 

dependent 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 Prototypical 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 taxonomy 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 high 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 prototypical distribution feeders. At this level of examination, the data will not be divided 

into monthly values; only annual values for each feeder will be examined. Simulation results 

for 22 of the 28 prototypical distribution 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 PVs with respect 

to peak load, annual energy consumption, system losses, power factor, annual output, and CO2

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 will 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 peak load 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, certain 

feeders have larger peak load 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 times

occur. The change in peak load is shown in Figure 2.3 and the percent change values are shown 

in Figure 2.4.
13

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

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 changing their state behavior when voltage is varied, causing the 

consumption of more power. For example, variable voltages can cause HVAC (Heating, 

Ventilation and Air Conditioning) units to shift their behavior and can cause 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 supplied 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 the feeder. The reduction 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 consumption by feeder

As can be seen in Figure 2.5, the annual energy consumption provided by the feeder, was 

reduced for all the feeders with residential PVs added. With the PV units operating in accordance 

with IEEE 1547, the PV units injected real power at the point of interconnection which supplied 

some of the local energy demand. This 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 still 

decreased because the PV units injected real power into the feeder.

The change in annual energy consumption, when compared to the base case, is given by

Figure 2.6. Figure 2.7 demonstrates that region 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

Figure 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, where 

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 adding 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 flow 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 by 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 residential 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 reduction 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 units were randomly placed, it is possible that the largest load, or an entire branch of the 

feeder, did not have a PV unit associated with it. Therefore, 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 feeder 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, maximum 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 

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 2.11 shows the variation of the 

average power factor over the course of the year for the prototypical feeders with residential enduse customers.
20

Figure 2.11 : Comparison of average annual power factor by feeder

Figure 2.12 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 lags. Overall, this lagging power factor was 

not improved. This emphasizes 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 2.13 shows the annual power output by residential PV arrays added to the distribution 

feeder. Recall that region 5 had the highest penetration of PV units, as 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 CO2 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 using a simple dispatch 

algorithm. Generation sources were sized by the regional generation 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 region, are explained in Appendix 

B.

Figure 2.14 shows the annual CO2 emissions for both the base case and the residential PV case.
23

Figure 2.14: Comparison of total annual CO2 emission by feeder

Figure 2.14 shows that the CO2 emissions were reduced on 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 CO2. Region 2 received the lowest amount of PV penetration 

and showed the smallest improvement in energy consumption reduction, as was seen from Figure 

2.7. Hence, region 2 also demonstrated the smallest reduction in CO2 emissions. Region 5 

received higher penetration of PV as fraction of the feeder peak load and showed the highest 

reduction in CO2 emissions. Figure 2.15 and Figure 2.16 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 CO2 emissions by feeder (tons)

Figure 2.16: Change in total annual CO2 emissions by feeder (%)
25

2.1.2 Commercial PVs

The addition 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 installations 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 tools, models, and materials used to model the commercial PV 

placements are openly available 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 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
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 regional

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 section, the aggregated annual simulation results will be examined. Simulation results 

for each of the prototypical distribution feeders will be examined, including five cases of GC12.47-1; 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 CO2 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 Figure 2.17. 
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 2.19.

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 (MW)

Figure 2.19: Change in peak load by feeder (%)
29

2.1.2.5 Annual Energy Consumption

The addition of commercial scale PV reduces 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 consumption by feeder (MWh)

From Figure 2.21 and Figure 2.22, 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 (GWh)

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 annual 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 2.24 and Figure 2.25, losses were reduced for nearly all the feeders. 

However, feeder R4-2500-1 showed no noticeable change. The largest loss reduction was seen 

on feeder R5-12.47-3. The reduction in losses varies for the different feeders since losses are 

affected by a number of factors, such as end-use load composition, equipment sizing, feeder 

design, and most importantly the distribution of the PV units on the feeder. These various 

factors cause the imbalance 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 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 factor as seen at the substation to degrade and move farther away from unity. 

Figure 2.26 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 controls, either integrated 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 2.28 shows the annual energy produced by the addition of commercial PV. The 

commercial PV output shown is the sum of the commercial PV installations on office buildings, 

strip malls and big box retailers. The largest PV output is observed for feeder R5-35.00-1 in 

region 5, which contained 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. This indicates that PV output in a region is not only

dependent on the penetration level. In this study, fixed PV arrays of 100kW were distributed on 

commercial loads, one array per load, which limited the number of PV units on the feeder based 

on the number of commercial end-use customers.
35

Figure 2.28: Annual commercial PV output by feeder

2.1.2.9 Annual CO2 emissions

Figure 2.29, Figure 2.30, and Figure 2.31 show the annual reduction of carbon dioxide emitted 

by generating 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 the 

emissions calculations are again outlined in Appendix B.
36

Figure 2.29 : Change in total annual CO2 emissions by feeder (kilotons)

Figure 2.30: Change in total annual CO2 emissions by feeder (tons)
37

Figure 2.31: Change in total annual CO2 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 commercial) have different levels 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 Combined 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 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

2.1.3.2 Specific Implementation of Combined PV

Combined PV used penetration levels, as shown in Table 2.6, for different regions, 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 this 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

Table 2.6: Combined 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 annual values are

examined. 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, annual output, and CO2 emissions.

2.1.3.4 Annual Peak Load

As seen in previous sections for residential PV integration and commercial PV integration, 

peak 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 feeders, this helped mitigate the voltage issues caused 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 loads. Figure 2.33 shows the comparison 
40

of annual peak load reduction for all the feeders under study. Figure 2.34 and Figure 2.35 show 

the change in annual peak load due to the addition of combined PVs.

Figure 2.33 : Comparison of peak load by feeder
41

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

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 2.36, energy

consumption is reduced for all the feeders when a combination of PV units are installed on the 

distribution feeders. Figure 2.37 and Figure 2.38 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 (GWh)
43

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

Figure 2.38: Change in annual energy consumption by feeder (%)
44

2.1.3.6 Annual System Losses

As stated in previous sections, the reduction of distribution system losses is also one of the 

primary benefits of PV installation. It can be observed from Figure 2.39, Figure 2.40, and Figure 

2.41 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 components and different feeders,

it can be concluded that the addition of PV generally increases system efficiency by reducing 

real power losses.

Figure 2.39: Comparison of total annual losses by feeder (MWh)
45

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

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 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 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 2.42 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 2.44 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 CO2 emissions

Similar to the other PV integration technologies discussed in previous sections, carbon dioxide 

emissions for the feeder were reduced with the addition of PV. Figure 2.45, Figure 2.46 and 

Figure 2.47 demonstrate that annual carbon dioxide emissions are reduced when the energy 

consumption is reduced. The amount of CO2 reduced is directly proportional to the amount of 

energy consumption reduced and will also vary depending on the generation mix for the region.
49

Figure 2.45: Comparison of annual CO2 emission by feeder

Figure 2.46: Change in annual CO2 emission by feeder (kilotons)
50

Figure 2.47: Change in annual CO2 emission by feeder (%)

2.2 Addition of Commercial WTG

In this section, the addition of commercial wind turbine generators at the distribution level will 

be examined. As the delivered cost of wind power from large wind turbines continues to fall, the 

cost of wind power becomes ever more competitive with utility supplied power. The improving 

economics of wind generation has prompted schools, businesses, and ranchers to consider the 

possibility of 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 

capabilities 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 have still not gained popularity. For the 

purposes 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 simulations, the analysis can be replicated and adapted by 

interested parties using the GridLAB-D materials available at [18]. All of the tools, models, and 

materials used by the researchers for this portion of the study are 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 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

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. Several locations, such as the beginning of the feeder, mid-feeder, and at the 

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 realistic scenario where the 

commercial wind turbine is not located directly near the substation. 
52

The theoretical power available in the wind ( Pwind ) 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 P Av R v wind = ρ = ρπ

(2.3)

The fraction of the power extracted from the wind by a practical wind turbine is usually 

represented byCp , which is the coefficient of performance, or power coefficient. The actual 

mechanical power output can be written as equation 2.4, where Cp is a function of tip speed 

ratio,λ , and blade pitch angle, β (deg). The tip ratio is defined in equation 2.5, where, ωR is the 

mechanical angular speed. 

ρπ (λ,β ) 2

1 2 3

mech Cp P = R v

(2.4)

v

ωR R λ = (2.5)

In the model used for the SGIG analysis, Cp 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 electrical 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 connected to the 

distribution feeder will be examined. At this level of examination, the data will not be divided 

into monthly values; only annual values will be examined. Simulation results for 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 WTGs with respect to peak load, annual energy 

consumption, system losses, power factor, annual output, and CO2 emissions.

2.2.3.1 Annual Peak Load

Figure 2.48 shows a comparison 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 2.49 and 

Figure 2.50. As the differences show, peak demand is reduced for all the feeders except for GC12.47-1_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 dependent 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
54

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

Figure 2.50: Change in peak load by feeder (%)
55

2.2.3.2 Annual Energy Consumption

Figure 2.51 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 2.52 and Figure 2.53 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 turbine to the GC feeders within the 

taxonomy. Since the GC feeders have a peak load of around 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 (GWh)
56

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

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 

2.54 shows the comparison of annual system losses by feeder. Figure 2.55 and Figure 2.56 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 performance 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 reductions 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 2.57 shows the 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 upon 

on the capacity of wind power that can be extracted in a particular region. From Figure 2.57, 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 useful 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.

Figure 2.57 : Comparison of annual wind output by feeders

2.2.3.5 Annual CO2 Emissions

Reduction of carbon dioxide emissions is another important benefit from the integration of 

WTGs 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 CO2 emission will be. Figure 2.58 compares the annual 

CO2 emission by feeder. Figure 2.59 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 CO2 emissions.
60

Figure 2.58 : Comparison of annual CO2 emission by feeders

Figure 2.59 : Change in annual CO2 emission by feeders (tons)
61

Figure 2.60 : Change in annual CO2 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 results 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 will be displayed for each feeder: peak monthly 

demand, monthly energy consumption, monthly losses, monthly PV output, and monthly CO2

emissions.

3.1.1 Example Feeder R1-12.47-1

 Figure 3.1 through Figure 3.5 show the monthly plots that are generated 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 has 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 shows 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 R1-12.47-1

 Figure 3.2 shows a comparison of reductions in energy consumption 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 results 

demonstrate that energy consumption was reduced for every month under simulation.

 It is interesting 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 months of December and January showed a very slight increase of 

peak load; however, the energy consumption is reduced for the same months, as seen in Figure 

3.2. Since the peak solar output typically does not coincide with the peak 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 GridLAB-D’s voltage-dependent models. As such, the 

devices would be operating in a slightly different 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 of energy consumption by month for R1-12.47-1

 The reduction of losses is also a benefit associated with residential PV installations, 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 decreased for all the components in the distribution system. The largest loss savings were

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 losses by month for R1-12.47-1

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: summer. 

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 decreases the CO2 emissions with a strong seasonal influence.
67

Figure 3.5: Comparison of CO2 emissions by month for R1-12.47-1

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 will be displayed for each feeder: peak monthly demand, monthly energy consumption, 

monthly losses, monthly PV output, and monthly CO2 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 sizes 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 all months except for the month of November. 
69

Figure 3.7: Comparison of energy consumption by month for GC-12.47-1_R1

As discussed in previous sections, 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 is 

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 losses

is a benefits of PV installation. Figure 3.8 shows a comparison of monthly 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 majority 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, the overhead line losses increase when commercial PV is installed on the feeder. 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 increased the potential for 

the excessive amounts of reverse current flow in the overhead lines, causing 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 CO2 emissions by month for GC-12.47-R1
72

Reduction in energy consumption leads to a reduction in carbon dioxide emissions, as 

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; any 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 feeders. This analysis is particularly interesting because some 

of the issues caused by the addition of PV can be solved by adding different sized PV units. For 

combined PV deployments, there are 5 plots that will be displayed for each feeder: peak monthly 

demand, monthly energy consumption, monthly losses, monthly PV output and monthly CO2

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 southeast part of the United States, 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 for the R5-35.00-1 feeder. It can be seen from the 

plot that the peak monthly demand decreases 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 sections, 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 consumption by month for R5-35.00-1

As seen in previous 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 observed 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 sections. 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 potential for reverse current flow in the overhead lines, 

causing the losses to increase. Once again, 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, it can be seen that reductions in energy consumption leads to reductions in carbon dioxide 

emissions for all the months.
77

Figure 3.15: Comparison of CO2 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 regions. 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 will be displayed for each feeder: peak monthly demand, monthly energy consumption, 

monthly losses, monthly PV output, and monthly CO2 emissions.

3.4.1 Example Feeder GC-12.47-1_R2

 Figure 3.16 through Figure 3.20 show the plots that are generated for feeder GC-12.47-1_R2

when it is operated with a single large WTG. Climate region 2 represents the north central and 

north eastern part of the United States, which generally has a cold climate, but does have 

considerable wind availability. A model of a 1.8 MW WTG was added to the feeder and the 

impact of its operation was compared to the base case.

Figure 3.16 shows the effect of adding a commercial WTG on feeder GC-12.47-1_R2. It can 

be observed 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-12.47-1_R2

Figure 3.17: Comparison of energy consumption by month for GC-12.47-1_R2
79

Figure 3.17 shows the reduction in monthly energy consumption for feeder GC-12.47-1_R2. 

Similar to the PV technologies described in previous sections, the addition of a large WTG 

considerably reduces energy consumption. 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 integrating 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 base 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 output 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, the larger the reduction in energy 

consumption, the larger the reduction in CO2 emitted. The reduction in emissions will be heavily 

dependent on the generation mix within the region.
81

Figure 3.20: Comparison of CO2 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 largest value.

4.1 Residential PV Impact Metrics
83

Table 4.1: Residential PV impact metrics for region 1

Index ∆ Metric Units

R1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

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

21

40

13
84

Table 4.2: Residential PV impact metrics for region 2

Index ∆ Metric Units

R2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

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

21

40

4

13
85

Table 4.3: Residential PV impact metrics for region 3

Index ∆ Metric Units

R3-12.47-1R3-12.47-3

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

21

40

4

13
86

Table 4.4: Residential PV impact metrics for region 4

Index ∆ Metric Units

R4-12.47-1R4-12.47-2R4-25.00-1

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

21

40

4

13
87

Table 4.5: Residential PV impact metrics for region 5

Index ∆ Metric Units

R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

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

21

40

4

13
88

4.2 Commercial PV Impact Metrics

Table 4.6: Commercial PV impact metrics for region 1

Index ∆ Metric Units

GC-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

40

13

21
89

Table 4.7: Commercial PV impact metrics for region 2

Index ∆ Metric Units

GC-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

21

40

4

13
90

Table 4.8: Commercial PV impact metrics for region 3

Index ∆ Metric Units

GC-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

21

40

4

13
91

Table 4.9: Commercial PV impact metrics for region 4

Index ∆ Metric Units

GC-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

21

40

4

13
92

Table 4.10: Commercial PV impact metrics for region 5

Index ∆ Metric Units

GC-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

21

40

4

13
93

4.3 Combined PV Impact Metrics

Table 4.11: Combined PV impact metrics for region 1

Index ∆ Metric Units

R1-12.47-1R1-12.47-2

R1-12.47-3R1-12.47-4R1-25.00-1

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

40

13

21
94

Table 4.12: Combined PV impact metrics for region 2

Index ∆ Metric Units

R2-12.47-1R2-12.47-2R2-25.00-1R2-35.00-1

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

21

40

4

13
95

Table 4.13: Combined PV impact metrics for region 3

Index ∆ Metric Units

R3-12.47-1R3-12.47-3

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

21

40

4

13
96

Table 4.14: Combined PV impact metrics for region 4

Index ∆ Metric Units

R4-12.47-1R4-12.47-2R4-25.00-1

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

21

40

4

13
97

Table 4.15: Combined PV impact metrics for region 5

Index ∆ Metric Units

R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

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

21

40

4

13
98

4.4 Commercial WTG Impact Metrics

Table 4.16: Commercial WTG impact metrics for region 1

Index ∆ Metric Units

GC-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

40

21

13
99

Table 4.17: Commercial WTG impact metrics for region 2

Index ∆ Metric Units

GC-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

13

4

40

21
100

Table 4.18: Commercial WTG impact metrics for region 3

Index ∆ Metric Units

GC-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

13

40

21

4
101

Table 4.19: Commercial WTG impact metrics for region 4

Index ∆ Metric Units

GC-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

13

40

21

4
102

Table 4.20: Commercial WTG metrics for region 5

Index ∆ Metric Units

GC-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

13

40

21

4
103

5 Conclusion 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 interface 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 installations 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 reasons 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 benefits from net metering. The utilities 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 additional load growth with no 

transmission investment. For the purposes of these simulations, a low, 1% to 6% of feeder peak 

load was used as the PV population 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 distributed 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 energy 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 PV 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. However, 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 

primarily 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-use 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 

compositions and various end-use load types. Increasing the 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 placement of PVs. Throughout the nation, there are older 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 decreases the 

pollutant emissions. The extent to which the emissions are reduced will be heavily dependent on 

the generation mix within the region, with each generation type within the mix producing 

varying amounts of CO2, 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 profile 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 utility, or commercial, industrial, or residential

customers.

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 have 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, there are metrics 

that are either not directly tracked though 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 an 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 an SGIG smart grid metric,

but a full analysis would require 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 future 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 

combined 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 annual energy 

consumption on most feeders.

2) Peak load is generally reduced on all the feeders and 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 generation can increase losses. 

4) Annual CO2 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) CO2 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 feeder peak load, 

approximately 5 MW on the commercial feeders. Additionally, WTGs tend to 

produce more power at night when the generation sources tend 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 

individuals, 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 available 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 contained 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 listed 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 (SOx, 

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* $/kWmonth

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 (SOx, 

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 CO2 Emissions tons tons Could be modeled or estimated

64 Pollutant Emissions (SOx, 

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 distribution 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 difference 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 

percentages 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 distribution 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 cost: 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 substation

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 specific 

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 measures the CO2 emissions required to supply the 

electricity to the end use load.

13) Pollutant emissions: This metric measures 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 

specific 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 results. 

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 overload 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: Because 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 information 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 utilities, 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) Distribution 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: 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. 

28) Distribution restoration cost: Because this is dependent on the business structure of 

specific 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, transformers, 

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 various 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 indicated 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 indicates 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 defined in IEEE standard 1366 

CAIDI is the customer average interruption duration index. CAIDI 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 fault. The outage response time is the time between the occurrence of 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 substations and the interconnecting transmission or subtransmission 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 impedance 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 substations.

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 Distribution 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 seen 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 to 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 Taxonomy 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 office 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 the 

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 the end-use load 

models in the appropriate level of detail as was done for the 2010 report on Conservation 

Voltage Reduction [22].

B.1 End-use 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 consume 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 loads are classified into various 

categories. In 2010, an analysis of conservation 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 classifications 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 classifications

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 multistate model 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 S P Pθ S I I

V

V

S Z Z

V

V

P n n

n

a

n

n

a

i cos cos cos

2 % % %

2

 (B1)

( ) ( ) ( )

















= ⋅ ⋅ ⋅ + ⋅ ⋅ ⋅ + ⋅ ⋅ θ θ S P Pθ S I I

V

V

S Z Z

V

V

Q n n

n

a

n

n

a

i % sin % sin % sin

2

2

 (B2)

100 % % P% = Z + I + (B3)
119

where:

Pi : real power consumption of the ith load

Qi: reactive power consumption of the ith load

Va: actual terminal voltage 

Vn: nominal terminal voltage 

Sn: apparent Power consumption at nominal voltage

Z%: percent of load that is constant impedance

I%: percent of load that is constant current

P%: percent of load that is constant power

Zθ: phase angle of constant impedance component

Iθ: phase angle of constant current component

Pθ: phase angle of constant power component

In a time-variant load representation, the coefficients of the ZIP model, Vn, Sn, Z%, I%, P%, Zθ, 

Iθ, and Pθ, remain constant, but the power consumption, Pi and Qi, of the i

th load varies with the 

actual terminal voltage, Va. 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 

quadratic coefficient is equivalent to Z%. The ZIP model only varies the power consumption as a 

function of actual terminal voltage, Va. 

In (B1) and (B2), there are six constants that define the voltage dependent behavior of the ZIP 

load: Z%, I%, P%, Zθ, Iθ, and Pθ. 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 accurately reflect various 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 2010 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 developed 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, Sn. The value of Sn 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 the 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 only 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

100 105 110 115 120 125

Watts

Volts

Real Power Comparison

Pe

Pm

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

100 105 110 115 120 125

VAR

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 shining 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 HVAC Solar

Total

Heat

Tout Tair Tmass

Cair Cmass

Tset

UAenv UAmass

Q

Qair mass

Qsolar

Qgains

Qhvac

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

where, 

Cair: air heat capacity (Btu/°F)

Cmass: mass heat capacity (Btu/°F)

UAenv:external gain/heat loss coefficient (Btu/°F-h)

UAmass: internal gain/heat loss coefficient (Btu/°F-h)

Tout: air temperature outside the house (°F)
122

Tair: air temperature inside the house (°F)

Tmass: mass temperature inside the house (°F)

Tset: temperature set points of HVAC system (°F)

Qair: heat rate to house air (Btu/h)

Qgains: heat rate from appliance waste heat (Btu/h),

Qhvac: heat rate from HVAC system (Btu/h),

Qmass: heat rate to house mass (Btu/h), and

Qsolar: 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, Tair, known, the thermal 

behavior of the heat pump system in response to the defined thermostatic set point, Tset, can be 

determined. 

cT d

dt

dT b

dt

d T

a air

air air + + = 2

2

 (B4)

Where,

mass

mass air

UA

C C

a ⋅ =

( )

air

mass

mass env mass C

UA

C UA UA b + ⋅ + =

UAenv c =

( ) d Qmass Qair UAenv Tout = + + ⋅

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 maintain the inside air 

temperature, Tair, within the desired range. The electrical input energy to the motor, Scomp-motor, 

necessary to provide the thermal heat energy, is a function of two elements: the heat flow 

through the cooling unit, Qhvac, and the electrical losses of the compressor motor, Slosses; as shown 

in (B5) [23] - [24]. 
123

[ ( ) ( )] Scomp−motor = Qhvac Tout VT COP + Slosses VT , , 

(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. VT is the terminal voltage of the system compressor motor. 

Additionally, it should be noted that Qhvac is expressed in terms of British thermal units (Btu) 

consistent with the conventions of the heating/cooling industry in the United States and the 

derivation of the ETP model of [24], while Slosses 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 dependent, changes in line voltage will cause 

a change in power consumption. The cooling system's heat removal rate, Qhvac, can be solved 

using heat transfer equations based on the available mechanical torque of the compressor [24]. 

The motor losses, Slosses, 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 function 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 

thermostatic 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 Tset must be split 

into a heating set point, Tlow, and a cooling set point, Thigh. These set points determine the mode 

of operation of the heat pump system at any given time: off, cooling, heating-normal, or heatingemergency, as shown in Figure B.4. 

For a simple single state simulation, the heat pump system would be operating to either 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 is 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 are Thigh and Tlow. If the 

internal air temperature Tair rises above Thigh plus a dead band, DBhigh, then the heat pump will 

start cooling. If Tair decreases below Tlow minus a dead band, DBlow then the heat pump will start 

heating normally. If Tout decreases to a temperature, Taux, 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 Taux, the 

DBlow and DBhigh 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 Tair > (Thigh + DBhigh)

1 3 Tair < (Tlow – DBlow)

1 4

Tair < (Tlow – DBlow) & Tout

< Taux

2 1 Tair < (Thigh – DBhigh)

3 1 Tair > (Tlow + DBlow)

3 4 Tout < Taux

4 1 Tair > (Tlow + DBlow)

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 

instantaneous power draw of the four states shown in Figure B.4 is given by (B6)-(B9).

State 1: Off

SHVAC = 0 (B6)

State 2: Cooling

SHVAC = S fan−motor + Scomp−motor (B7)

State 3: Heating-Normal

SHVAC = S fan−motor + Scomp−motor (B8)

State 4: Heating-Emergency
126

elements

T

HVAC fan motor R

V S S

2

= − + (B9)

where, 

Sfan-motor: apparent power of ventilation fan motor (VA)

Scomp-motor: apparent power of compressor motor (VA)

VT: terminal voltage of the heat pump unit (V)

Relements: 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 Qhvac, particularly with respect to 

the COPs. With the instantaneous power draw determined by (B6)-(B9), the time necessary to 

heat or cool the house 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 these 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 and 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 feeder 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 example, 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 at 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 load 

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 

Consumption 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 buildings [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 for 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 Southwest 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 homes. 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 approximately 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 breakdown 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 documentation, 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 

Non-Electric Heat Pump Resistance Home*

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 each 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. 

Figure 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 particular 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 that 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. “Appliances” 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 indicate 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 pricing. 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 effects, 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):

324.9* *1000/8760 .442 k = floor area (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 responsive 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 created 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 number 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 average 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 

area, 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 there 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 third 

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 adjusted by the randomly chosen square footage so that 3*1.5*x2

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 

similar 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 used 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 always 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 efficiencies 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 Figure 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 representation 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 scheduled 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 typically 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 energy 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, and 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 for 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 shows 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 approximately 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 

hydroelectric 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 Table 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)

CO2 SO2 NOx 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 describes 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 5,400 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 feeders, 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 lightly 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 adjacent 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 long 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 populated 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 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. 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 mainly 

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 located 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 commercial, 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 low 

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 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 6,700 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% 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 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 single 

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 transfer 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 geographic 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 6,900 

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 overhead 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 to 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 kVA. 

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 located 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 populated 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 substation 

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 18: 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% 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 loads from other feeders, and vice 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 suburban 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 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 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 residences 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% 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. 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 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 66% to ensure the 

ability to transfer most of the loads from other feeders, and vice versa. The residential 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 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. The residential 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 multi-disciplinary 

simulation environment, it is possible to bring together diverse teams of experts from multiple 

fields of study to holistically 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 th0is 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 is 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

When 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 intervals 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 were 

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 computing 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 CO2 emissions plot ‘Base Case’ and ‘ResPV’. 

Monthly losses plots “Base’ and ‘ResPV’ for 4 different loss types; losses in overhead lines ‘OHL’, 

underground lines ‘UGL’, transformers ‘TFR’, and triplex 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 CO2 emissions by month for R1-12.47-2
158

D.1.3 Detailed Residential PV Plots for 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 CO2 emissions 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 CO2 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 CO2 emissions 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 CO2 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 CO2 emissions 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 CO2 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 CO2 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 CO2 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 R3-12.47-1
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 CO2 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 CO2 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 consumption 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 CO2 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 CO2 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 R4-25.00-1

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 CO2 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 CO2 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 CO2 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
197

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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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.122: 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 CO2 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 CO2 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 CO2 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 CO2 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 R2-12.47-2

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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 energy 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 CO2 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 CO2 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 peak 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 CO2 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 for R1-12.47-3
283

Figure D.248: Comparison of CO2 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.250: 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 CO2 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 CO2 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 consumption 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 CO2 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 CO2 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 CO2 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 CO2 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 CO2 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 loadby 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 CO2 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 loadby 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 CO2 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 loadby 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 CO2 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 CO2 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 CO2 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 CO2 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 loadby 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 CO2 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 loadby 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 CO2 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
325

Figure D.332: PV output by month for R5-25.00-1

Figure D.333: Comparison of CO2 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 WTG 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: Comparison of WTG output by month for GC-12.47-1_R1

Figure D.338: Comparison of CO2 output by month for GC-12.47-1_R1
329

D.4.2 Detailed Commercial 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_R3

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 CO2 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: Comparison of CO2 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_R5

Figure D.352: Comparison of WTG output by month for GC-12.47-1_R5
336

Figure D.353: Comparison of CO2 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 Units

GC-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

4

13

40

21
337

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

Index Metric Units

GC-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

4

13

21

40
338

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

Index Metric Units

GC-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

4

13

40

21
339

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

Index Metric Units

GC-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

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340

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

Index Metric Units

GC-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

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341

E.2 Individual Residential PV Performance Metrics

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

Index Metric Units

R1-12.47-1R1-12.47-2R1-12.47-3R1-12.47-4R1-25.00-1

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

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342

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

Index Metric Units

R2-12.47-1R2-12.47-2R2-12.47-3R2-25.00-1R2-35.00-1

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

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343

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

Index Metric Units

R3-12.47-1R3-12.47-2R3-12.47-3

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

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344

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

Index Metric Units

R4-12.47-1R4-12.47-2R4-25.00-1

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

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345

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

Index Metric Units

R5-12.47-1R5-12.47-2R5-12.47-3R5-12.47-4R5-12.47-5R5-25.00-1R5-35.00-1

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

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E.3 Individual Commercial PV Performance Metrics
346

Table E.11: Commercial PV performance metrics for region 1

Index Metric Units

GC-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

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347

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

Index Metric Units

GC-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

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348

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

Index Metric Units

GC-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

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349

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

Index Metric Units

GC-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

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350

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

Index Metric Units

GC-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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4

13

E.4 Individual Combined PV Performance Metrics
351

Table E.16: Combined PV performance metrics for region 1

Index Metric Units

GC-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

40

13

21
352

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

Index Metric Units

GC-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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4

13
353

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

Index Metric Units

GC-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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4

13
354

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

Index Metric Units

GC-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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4

13
355

Table E.20: Combined PV performance metrics for region 5

Index Metric Units

GC-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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13

E.5 Individual Commercial WTG Performance Metrics
356

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

Index Metric Units

GC-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

40

21

13
357

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

Index Metric Units

GC-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

13

4

40

21
358

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

Index Metric Units

GC-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

13

40

21

4
359

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

Index Metric Units

GC-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

13

40

21

4
360

Table E.25: Commercial WTG performance metrics for region 5

Index Metric Units

GC-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

13

40

21

4
361

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