NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

NREL Variability Analysis 

for the Western 

Interconnect

WECC WebEx

Brendan Kirby, 

Jack King, 

Michael Milligan

National Renewable Energy 

Laboratory

July 29, 2011

NREL/PR-5500-52430

Composite photo created by NREL
National Renewable Energy Laboratory 2 Innovation for Our Energy Future

• Study Data

• Methodology

• Analysis

• Allocation of reserve requirements

Contents
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• Investigate the effects of several Energy Imbalance 

Markets implementations in the Western 

Interconnect (WI):

– Operating reserves;

– Ramping demand;

– Alternative Schedu.ing

• Analysis based on aggregation of variability and 

uncertainty:

– Uses available load, wind and solar data.

• Calculate the reserve requirements:

– Estimating within the hour and hourly requirements 

based on historical data.

• Compared to NREL draft report, this analysis has 

approximately 1/3 VG.

Overview
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Study Data
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Model Area

WALC

BPA

PACW

EPE

CHPD

GCPD

DOPD

TPWR

SCL

TEP

SRP

AZPS

TID NEVP

SMUD

PNM

PSCO

SPP

PACE

WACM

NWE

IPC

PGE

AVA

BCTC

PSE

IID

• Based on TEPPC PC 0.

• Areas of the WI not 

already in a market 

structure (CAISO, 

Alberta).

• LADWP not included.

• Analysis at BAA 

granularity.

• Generation only BAAs 

not considered.

• 28 load and generation 

BAAs retained – The 

study ‘Footprint.’

• Regions are:

– Columbia Grid – Orange;

– NTTG – Blue;

– West Connect – White.
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• 2020 loads provided by WECC;

• Load shape is based on 2006 load;

• 193,700 MW in full Western Interconnect (noncoincident).

• 116,700 MW in analysis footprint (noncoincident);

• One hour load provided, 10-minute synthesized.

Load Data
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• NREL WWSIS wind dataset for 2006;

• 10-minute resolution;

• Sites identified by TEPPC 2020 PC 0;

• 29,085 MW in Western Interconnect;

• Approximately 8% of WI 2020 load;

• 18,272-MW nameplate modeled in analysis –

footprint.

Wind Data
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• NREL WECC Solar dataset for 2006;

• 10-minute resolution;

• Sites identified by TEPPC 2020 PC 0;

• 14,300 MW in WI;

• Approximately 3% of WI 2020 load;

• 4,568 MW nameplate in analysis footprint.

Solar Data
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Reserve Calculations
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• Developed for the EWITS study;*

• Statistical approach based on 10-minute time 

series wind, solar and load;

• Method that can estimate adequate reserves to 

cover the short term and hour-ahead forecast error 

based on historical data;

• Predicts requirements based on current hour load 

and wind, solar production;

• Statistically combine with load regulation 

requirements;

• Provide 8760 vector of requirements for the 

production simulations.

*For in-depth discussion see section 5 of the EWITS final report: 

http://www.nrel.gov/wind/systemsintegration/pdfs/2010/ewits_final_report.pdf

Reserve Calculations
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• Regulation – Fast changes:

– Due to variability and short-term forecast errors;

– Faster than re-dispatch period;

– AGC resources required.

• Spinning – larger, slower, less frequent variations:

– Due to longer term forecast errors;

– AGC not required;

– 10-minute response.

• Non-spinning/supplemental:

– Large, infrequent, slow moving events such as 

unforecasted ramps;

– 30-minute response.

Reserve Definitions
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Short-Term Forecast Error - Regulation

• Based on persistence 

forecas;t

• Wind data is 10 

minute, 10-minute 

delay for forecast;

• Forecast error is 

calculated as 

difference from actual 

to forecast.
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Short-term Calculation

• Measure as standard deviation of the forecast 

error; 

• Forecast error varies with production level;

• Empirical expected error as a function of production 

is quadratic:

– Low variability at low and high wind (cut-in and rated);

– High variability in mid range at steep part of power curve;

– Solar follows same pattern, more or less.

• Predicts the expected variability for the hour based 

on the intra-hour statistics, current production;

• Assumes fast dispatch, 10 minutes in this case:

– Implication is that economic movement happen at 10- minute updates.
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Calculation of Short-term Forecast Error

• Calculate and sort error 

by production level;

• Divide production into 

deciles;

• Calculate error sigma in 

each decile;

• Blue line is calculated 

from data;

• Red line is curve fit;

• Equation of the curve 

shown below.
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Calculating the Regulation Requirement
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Hour-ahead Forecast Errors

• Repeat the short term forecast procedure with 

hour-ahead forecasting;

• Again, same procedure for wind and solar;

• Load following not included.
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Spin and Non-spin Calculation
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Verification of Reserve Coverage

• Shows coverage of 

VG intra-hour 

movements;

• Actual ramp data 

from footprint EIM;

• Red line – Actual 

average reserve 

calcs from footprint 

EIM;

• Probability lines are 

z% of all ramps at x 

minutes are less 

than y MW.
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Reserves Provided to E3 for Phase 2 Study

• NREL provided reserve calculations to E3 for phase 2 

study;

• Slight variation in calculation of regulation component –

load not included in E3 Flex;

• Does not include contingency reserves;

• Areas are slightly different from each other.

NREL E3 Response Notes

Regulation Flex AGC

E3 flex reserve does not 

contain load

Spin Spin <10 minutes

Non-spin Supplemental <30 minutes Terminology change
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Analysis
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Cases

• Complete analysis includes:

– Footprint EIM

• All 28 BAAs participating. 

– ‘Regional’ EIM implementations

• Columbia Grid;

• Northern Tier Transmission Group;

• WestConnect.

– EIM implementations without BPA and/or WAPA 

participation.

– Comparison to the Business-As-Usual (BAU) case –

No EIM in place;

– Selected results.
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Footprint EIM Regulation

• Detail of the individual 

regulation components;

• Bars are average values, 

whiskers are min and 

max;

• 3 regional results in 

smaller reductions 

compared to footprint.

Reduction over BAU

Footprint Regional

MW

% 

Reduct. MW

% 

Reduct.

Load Reg 211 18% 178 16%

Wind Reg 372 53% 231 33%

Solar Reg 49 21% 46 19%

Total Reg 570 36% 419 26%
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Reserve Details for Footprint and Regional EIM

• Up to 42% (2000 MW) 

reduction in total 

reserve requirement 

for footprint EIM;

• 26% for regional 

EIMs.

Reduction over BAU

Footprint Regional

MW % Reduct. MW % Reduct.

Total Reg 570 36% 419 26%

Spin 481 45% 271 25%

Non-spin 963 45% 542 25%

Total 2014 42% 1233 26%
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Ramp Demand Reduction – Footprint EIM

• Based on hourly ramps;

• Shows ramp reduction 

from EIM over BAU;

• Duration plot shows 

hours per year for 

reduction level;

• Reduction in net ramp is 

greater than 1000 MW 

for 251 hours per year 

and averages about 260 

MW.
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Reserve Reduction Duration – Footprint EIM

• Average reductions:

– 590-MW Regulation;

– 2092-MW Total Reserves.
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Regional EIM – Columbia Grid (CG)

• Average reserves 

for a CG EIM;

• BPA dominates the 

wind so saving less 

than other regions;

• 8070 MW Wind.

Reserve Reduction

MW % Reduc.

Load Reg 37 14%

Wind Reg 39 15%

Solar Reg 0 0%

Total Reg 85 21%

Spin 38 12%

Non-spin 76 12%

Total 199 15%
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Regional EIM – NTTG

• Average reserves;

• Very small solar;

• Wind dominates 

savings;

• ~4200 MW wind 

and 20 MW solar.

Reserve Reduction

MW % Reduc.

Load Reg 28 12%

Wind Reg 87 45%

Solar Reg 0 0%

Total Reg 82 25%

Spin 79 42%

Non-spin 158 42%

Total 319 36%
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Regional EIM – WestConnect (WC)

• Average reserves;

• Substantial saving 

from wind and solar;

• ~5700 MW Wind and 

4550 MW solar.

Reserve Reduction

MW % Reduc.

Load Reg 113 22%

Wind Reg 105 41%

Solar Reg 46 20%

Total Reg 253 34%

Spin 154 27%

Non-spin 307 27%

Total 714 29%
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Conclusions

• Method for calculating additional reserve 

requirements due to wind and solar production;

• EIM results in substantial reduction in reserves 

requirements and ramping demand;

• Reduced participation reduces benefits for all 

but reduces the benefits to non-participants the 

most;

• Full participation leads to maximum benefit 

across the Western Interconnection, up to 42% 

of total reserve requirement; 

• Regional EIM implementations have smaller but 

substantial benefits.