PNNL Methodology to Evaluate Balancing Reserve Requirements

and Integration Plans to ABB GridView

Nader SamaanYuri V. Makarov

Pacific Northwest National Laboratory

Presentation to PDWG - PCM (Production Cost Model) Data Work Group November 5, 2019

Jinxiang Zhu ABB GridView

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Summary of PNNL’s Methodology

The methodology mimics the real balancing process including scheduling and real-time dispatchIt incorporates both the variability and uncertainty factorsAll sources of uncertainty (load, wind, solar) are included

It reflects forecast errors and their impact on balancing requirementsThe methodology evaluates not only the capacity requirements, but also the ramping requirementsThe approach is flexible to reflect differences between balancing processes in different systemsThe methodology has been benchmarked against the actually observed balancing requirementsIt has been used in multiple studies

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Net Load and Generation Requirement

BA Hourly Net Load = Load – Wind – Solar + Interchange Net Load = Generation RequirementScheduled Generation Requirement = Forecasted Net LoadActual Generation Actual Net Load ± ΔGeneration Requirement:

Energy schedulesReal time dispatch (Load following)Regulation

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Variability and Uncertainty (HA-Forecast)

Load,MW

tOperating Hour

Hour Ahead Load Schedule

20 Minute Ramps Actual Load

Average Actual Load

Forecast Error

UNCERTAINTY

VARIABILITY

Load,

MW

t

Operating Hour

Hour Ahead

Load Schedule

20 Minute Ramps

Actual Load

Average

Actual Load

Forecast Error

5

Block Hour-Ahead Net Load Schedules

Load,MW

t

Hour Ahead Load Schedule

20 Minute Ramps

Forecast Error

Hour Ahead Load Schedule

t+1

Actual hourly load average

Load,

MW

t

Hour Ahead

Load Schedule

20 Minute Ramps

Forecast Error

Hour Ahead

Load Schedule

t+1

6

Real-Time Scheduling

Load,MW

t5 Minute Dispatch Interval

Real Time Load Schedule

5 Minute Ramps Actual Load

Average Actual Load

Forecast Error

Load,

MW

t

5 Minute Dispatch Interval

Real Time

Load Schedule

5 Minute Ramps

Actual Load

Average

Actual Load

Forecast Error

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Balancing Reserves Components

5 min 5 min

Hour-Ahead Schedule

RT ForecastError (Persistent)

1 min

RT Dispatch

Regulation(Includes RT Error)

RT Schedule(Includes RT Error)

Actual GenRequirement (Load – Wind – Solar)

Load Following(Includes RT Error)

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Load Following and Regulation

t

MW“Regulation”

“Load Following”

ActualLoad5-Minute

Schedule

Hourly Schedule

Load Following is the difference between the hourly energy schedule including 20-minute ramps (red line) and the short-term 5-minute forecast/schedule and applied “limited ramping capability” function (blue line). Regulation is the difference between the actual generation requirement and the short-term 5-minute dispatch (the red area between the blue and green lines).

t

MW

“Regulation”

“Load Following”

Actual

Load

5-Minute

Schedule

Hourly

Schedule

Example of Load Following and Regulation Requirements

Generate hour-ahead and 5-minute ahead forecast errors for load, wind and solarUse actual and forecast data to derive minute by minute load following and regulation requirements for each BA

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Load Following requirements Regulation requirements

Models for Balancing Requirements Uncertainty: Multidimensional Uncertainty Analysis

In existing approaches, the analysis is frequently limited to just one dimension of the uncertainty problem –capacity. But the capacity is not a single sufficient descriptor of the problem. Operational performance of a power system can be demonstrated through four basic metrics, forming the “first performance envelope”.

Capacity (π) indicates the required minute-to-minute amount of generation or change in generation output to meet variations in balancing requirements. Ramp rate (ρ) is the slope of the ramp. Ramp duration (δ) is the duration of a curve’s ramp along the time axis. Energy (Є) is the integration of capacity over time and can be calculated as the area between the analyzed curve and the time axis.

Ramp Duration, min

Energy, MWh

Capacity,MW

Time

MW

Ramp Rate, MW/min

Net Load ORLoad Following ORRegulation Curve

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Assessment of Ramping Requirements

A swinging door algorithm is used to derive the required capability (π), ramp rate (ρ) and ramp duration (δ).

Populate the triads (πi, ρi, δi) into a three-dimensional space.

Determine the boundary values for (π, ρ, δ) to make the probability of being outside the box:

Swinging door" algorithm – obtaining capacity, ramp rate and duration.

Concurrent consideration of the capacity, ramp rate and duration requirements.

inout

outout NN

Np+

=

Simulate Forecast Errors –Load and Wind

Load and wind forecast errors are simulated using a random number generator based on the statistical characteristics of the actual forecast errorsThe distribution of forecast errors is an unbiased Truncated Normal Distribution (TND)

PDF(Ɛ)

0

1

εmaxεmin Ɛ

PDF(Ɛ)

0

1

εmax

εmin

Ɛ

2010 Day Ahead Load Forecast Error Statistics

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Simulating Wind Forecast Error in the WECC VGS Study (as an example)

3Tier day-ahead wind forecast error available through NREL 2006 Western Wind Data Sets were usedHour-ahead wind forecast is based on (T-2 hours) persistence model in production cost model

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Hour-Ahead Wind Forecast Error for Balancing Reserve Calculations

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Simulated Solar Forecast Errors

Behavior of solar is different than wind: In the absence of clouds and fog,

solar output is very predictable. Cloud and fog impacts are less

predictable and act quickly. Many days have little appreciable

variation caused by cloud cover. For days with cloud cover, some

hours are cloudless. The clearness index (CI) is obtained by

dividing the observed horizontal global solar radiation Rg by the horizontal extraterrestrial solar radiation R:

k = Rg/R Solar forecast errors vs. clearness

index.

Daily pattern of the solar radiation of clearness index.

Solar forecast errors vs. clearness index.

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The time and weather conditions during a day can result in the following different solar forecast errors patterns: Night Time — the forecast error is zero, ε = 0; Sunny Day — the forecast error is small or close to

0, ε→0, when the CI→1; Very Cloudy Day — the forecast error is limited or

close to 0, ε→0, when the CI→0; Partly Cloudy Day — the forecast error varies in a

wide range for the intermediate values of CI. The standard distribution of solar forecast errors can be

described as a function of CI. Solar generation profiles including actual solar generation

and ideal solar generation are used to calculate the clearness index:

Hypothetical distribution of the standard deviation of solar forecast errors depending on the

clearness index.

CI σHA0 < CI ≤ 0.2 5%

0.2 < CI ≤ 0.5 10%0.5 < CI ≤ 0.8 7.5%0.8 < CI ≤ 1.0 5%

Table 1. Standard Deviation of HA Solar Forecast Errors Based on Clearness Index

Level.

std(ε)

CI

stdmax

10.5

)21=()(

)(=)( n,..,,t,

tGtG

tCI smax

sa

Simulated Solar Forecast Errors (cont.)

Generated Day-Ahead Solar Forecast Error by BA (TEPPC 2020)

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Benchmarking of the Methodology

Load following and regulation requirements calculated by this methodology was benchmarked against actual load following and regulation applied in CAISO, BPA, PSE, NV Energy and Duke EnergyOverall, there was a good match (about 80%) between calculated values and actual applied values.

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Balancing Reserve Calculations NWPP EIM Study

Used a simplified one dimensional approachSignificant amount of effort for benchmarking of the methodology

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Grid Reserve And Flexibility Planning Tool (GRAF-Plan)

Stand alone GUI of PNNL toolOutput: Find the reserve capacity and ramping requirements for with certain confidence levelINPUT FILES/PARAMETERS:

Input forecast statisticsInput CSV filesAdvanced parameters

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PNNL-ABB Technology Commercialization Project (TCF)

Title: Deploying Intra-hour Uncertainty Analysis Tools to ABB's GridView, Duration: Two years from 1/2020 to 12/2021

Tasks: This project will incorporate the following incremental capabilities to GridView,

PNNL forecast error generator functionality to model uncertainty associated with day-ahead, hour-ahead and real-time forecasts of solar, wind and load at the balancing authority level (BA)Balancing reserve requirement calculation using PNNL “Swing Door” algorithm to add load following and regulation constraints at the BA levelSub-hourly database and optimization capability using selected functions from PNNL System Intra-hour Operation Simulator (ESIOCS) to model 5-min market operations

https://www.energy.gov/articles/department-energy-announces-2019-technology-commercialization-fund-projects

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Path Forward

As part of the industry outreach effort, the project team could support flex reserve calculations for the WECC 2030 PCM caseAssumptions about load, wind, solar forecast error statistics will need to be agreed on.

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Thanks!

Contact Information

Nader Samaan, Ph.D., P.E. Team Lead (Grid Analytics)Electricity Infrastructure GroupPacific Northwest National LaboratoryP.O. Box 999, MSIN J4-90 Richland, WA 99352

Phone: (509) 375-2954 (W)

Email: nader.samaan@pnnl.gov

mailto:nader.samaan@pnnl.gov

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Backup Slides

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Including Balancing Reserve Requirements in Production Cost Simulation (NWPP EIM analysis)

o 24-hour optimization windowo Day-ahead forecast hourly load profiles at BA levelo Day-ahead forecast hourly wind and solar profiles at bus levelo BA level contingency reserve.o BA level balancing reserves (load following and regulation reserves)

DayAhead

DA UC Pattern

HA UC Pattern

o One 10-min plus five 10-min look-ahead optimization window

o Actual 10min average loads profileso Actual 10min average wind and solar

profiles.o BA level contingency reserve.o BA level regulation reserveso DA/HA Unit Commitmento 10-min ED at NWPP EIM footprint

10-min Real Time (EIM)

o One 10-min plus five 10-min look-ahead optimization window

o Actual 10min average loads profileso Actual 10min average wind and solar

profiles.o BA level contingency reserve.o BA level regulation reserve.o DA/HA Unit Commitmento Freeze the hourly exchanges

between BA’s (within L10 of BA)

10-min Real Time, Hourly BA interchange (BAU)

o 1-hour optimization window with 5 hour look aheado Hour-ahead forecast hourly load profiles at BA levelo Hour-ahead forecast hourly wind and solar profiles at bus

levelo BA level contingency reserve.o BA level flexibility (load following and regulation reserves)o Allow the commitment and decomitment of units that can

start/shutdown in less than 6 hours

HourAhead

HA UC Pattern

Projects (1)

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No. Project Titles Funding Sources Project Duration

1 Integration of Wind Plant Output Forecasting into Utility Energy Management Systems (EMS) DOE’s Office of EERE 2008-2009

2 Developing Tools for Online Analysis and Visualization of Operational Impacts of Wind and Solar Generation CEC 2009-2011

3 Predict Day-ahead Regulation Requirements for CAISO CEC 2010-2011

4 Assessing the Value of Regulation Resources Based on Their Time Response Characteristics CEC 2007-2008

5 Operational Impacts of Wind Energy Resources in the Bonneville Power Administration (BPA) Control Area BPA 2008

6 CAISO 20% and 33% Renewables Penetration Study CAISO 2007, 2009

7 Large-Scale PV Integration Study for NV Energy NV Energy and DOE 2010-2012

8 WECC BA Consolidation Study DOE 2009-2012

9 Energy Storage for Power Systems Applications: A Regional Assessment for the Northwest Power Pool (NWPP) DOE 2009-2010

Projects (2)

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No. Project Titles Funding Sources Project Duration

10 NWPP EIM Study NWPP 2012-2013

11 Duke Energy PV Integration Study Duke Energy 2013-2014

12 Honduras PV integration Study DOS 2015-2016

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Publications (1)

Project Reports1. Lu S, NA Samaan, D Meng, FS Chassin, Y Zhang, B Vyakaranam, WM Warwick, JC Fuller, R Diao, TB Nguyen, and C

Jin, “Duke Energy Photovoltaic Integration Study: Carolinas Service Areas”, PNNL-23226, Pacific Northwest National Laboratory, Richland, WA, 2014. [Online.] Available: http://www.pnnl.gov/main/publications/external/technical_reports/PNNL-23226.pdf

2. NA Samaan, R Bayless, M Symonds, TB Nguyen, C Jin, D Wu, R Diao, YV Makarov, LD Kannberg, T Guo, S Dennison-Leonard , M Goodenough, R Schellberg, S Conger, K Harris, M Rarity, S Wallace, J Austin, R Noteboom, T Van Blaricom , K McRunnel, J Apperson, M Empey, PV Etingov, D Warady, R Brush, J Newkirk, P Williams, M Landauer, H Owen, W Morter, K Haraguchi, J Portouw, Downey, S Sorey, S Williams, T Gossa, C Kalich, P Damiano, C Macarthur, T Martin, J Hoerner, S Knudsen, A Johnson, R Link, and D Holcomb, “Analysis of Benefits of an Energy Imbalance Market in the NWPP.” PNNL-22877, Pacific Northwest National Laboratory, Richland, WA, 2013. [Online.] Available: http://www.pnnl.gov/main/publications/external/technical_reports/PNNL-22877.pdf

3. M. Hunsaker, N. Samaan, M. Milligan, T. Guo, G. Liu & J. Toolson, "Balancing Authority Cooperation Concepts to Reduce Variable Generation Integration Costs in the Western Interconnection: Intra-Hour Scheduling", Final Project Report, PNNL-22406, WECC. April 2013. [Online.] Available: http://www.wecc.biz/Reliability/VGS_BalancingAuthorityCooperationConcepts_Intra-HourScheduling.pdf

http://www.pnnl.gov/main/publications/external/technical_reports/PNNL-23226.pdfhttp://www.pnnl.gov/main/publications/external/technical_reports/PNNL-22877.pdfhttp://www.wecc.biz/Reliability/VGS_BalancingAuthorityCooperationConcepts_Intra-HourScheduling.pdf

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Publications (2)

Project Reports (cont.)4. Y. V. Makarov, P. V. Etingov, K. Subbarao, J. Ma, and C. Loutan, "Online Analysis of Wind and Solar Part I: Ramping Tool

-Final Report," PNNL Project Report, PNNL-21112, Prepared for California Energy Commission (CEC) by PNNL, Jan. 2012.

5. Y.V. Makarov, P.V. Etingov, N. Samaan, J. Ma, and C. Loutan, “Predict Day-ahead Regulation Requirements for CAISO Balancing Area - Final Report,” PNNL Project Report, PNNL-20676, Prepared for CEC, by PNNL, Aug. 2011. [Online.] Available: http://uc-ciee.org/downloads/Day-ahead_Regulation_Final_Report.pdf

6. S. Lu, P.V. Etingov, R. Diao, J. Ma, N.A. Samaan, Y.V. Makarov, X. Guo, R.P. Hafen, C. Jin, H. Kirkham, et al., “Large-Scale PV Integration Study,” PNNL Project Report, PNNL-20677, Prepared for the U.S. Department of Energy, PNNL, Richland, WA, July 2011.

7. M. Kintner-Meyer, P. Balducci, C. Jin, T. Nguyen, M. Elizondo, V. Viswanathan, X. Guo, F. Tuffner, "Energy Storage for Power Systems Applications: A Regional Assessment for the Northwest Power Pool (NWPP),”PNNL-19300, Prepared for DOE, Apr. 2010.

8. Y.V. Makarov, P.V. Etingov, N. Zhou, J. Ma, N.A. Samaan, D. Ruisheng, S. Malhara, R.T. Guttromson, P. Du, and C. Sastry, “Analysis Methodology for Balancing Authority Cooperation in High Penetration of Variable Generation-Final Report,” PNNL Project Report, PNNL-19229, Prepared for the U.S. DOE, Feb. 2010.

9. Y.V. Makarov, Z. Huang, P.V. Etingov, J. Ma, R.T. Guttromson, K. Subbarao, and B.B. Chakrabarti, “Wind Energy Management System (EMS) Integration Project – Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations,” PNNL Project Report, PNNL-19189, Prepared for the U.S. DOE, Jan. 2010.

10.Y.V. Makarov, J. Ma, S. Lu, and T. Nguyen, “Assessing the Value of Regulation Resources Based on Their Time Response Characteristics,” PNNL Project Report, PNNL-17632, Prepared for CEC, June 2008.

11. “Integration of Renewable Resources - Operational Requirements and Generation Fleet Capability At 20% RPS,” California CAISO Report, Aug. 31, 2010.

12. “Integration of Renewable Resources Report−Transmission and Operating Issues and Recommendations for Integrating Renewable Resources on the California ISO Controlled Grid,” California ISO Report, Nov. 2007.

http://uc-ciee.org/downloads/Day-ahead_Regulation_Final_Report.pdf

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Publications (3)

Journal Papers1. Y.V. Makarov, P.V. Etingov, J. Ma, Z. Huang, and K. Subbarao, “Incorporating wind generation forecast uncertainty into

power system operation, dispatch, and unit commitment procedures,” IEEE Transactions on Sustainable Energy, vol. 2, no. 4, pp. 433-442, Oct. 2011.

2. Y.V. Makarov, C. Loutan, J. Ma, and P.de Mello, “Operational impacts of wind generation in California power system,” IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 1039-1050, May 2009.

Conference Papers1. R. Diao, N. Samaan, Y. Makarov, R. Hafen, and J. Ma, “Planning practice considering renewable integration: Balancing

authorities consolidation,” 2012 IEEE PES General Meeting, San Diego, CA, July 22-27, 2012, (accepted).2. J. Ma, S. Lu, P.V. Etingov, and Y.V. Makarov, “Evaluating the impact of solar power on balancing reserves in the southern

Nevada system,” 2012 IEEE PES General Meeting, San Diego, CA, July 22-27, 2012, (accepted).3. Y.V. Makarov, P.V. Etingov, N.A. Samaan, N. Lu, J. Ma, K. Subbarao, P. Du, L.D. Kannberg, “Improving performance of

power systems with large-scale variable generation additions,” 2012 IEEE PES General Meeting, San Diego, CA, July 22-27, 2012, (accepted).

4. N. Samaan, M. Milligan, Y.V. Makarov, M. Hunsaker, T. Nguyen, C. Jin, R. Diao, J. Ma, R. Hafen, X. Guo, and N. Lu, “Evaluation of balancing authorities consolidation benefits in the western interconnection under high wind and solar penetration,” AWEA Wind Power 2012 Conference & Exhibition, Atlanta, GA, June 3-6, 2012, (accepted).

5. J. Ma, S. Lu, R.P. Hafen, P.V. Etingov, N.A. Samaan, Y.V. Makarov, and V. Chadliev, "The impact of solar photovoltaic generation on balancing requirements in the southern Nevada system,” 2012 IEEE PES Transmission and Distribution Conference and Exposition, Orlando, FL, May 7-10, 2012, (accepted).

6. P.V. Etingov, S. Lu, X. Guo, J. Ma. Y.V. Makarov, V. Chadliev, and R. Salgo, “Identifying challenging operating hours for solar integration in the NV Energy system,” 2012 IEEE PES Transmission and Distribution Conference and Exposition, Orlando, FL, May 7-10, 2012, (accepted).

7. R. Diao, S. Lu, and J. Ma, “On evaluating cycling and movement of conventional generators for balancing services with large solar penetration,” 2012 IEEE PES Transmission and Distribution Conference and Exposition, Orlando, FL, May 7-10, 2012, (accepted).

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Publications (4)

Conference Papers (cont.)8. J. Ma, Y.V. Makarov, C. Loutan, and Z. Xie, “Impact of wind and solar generation on the California ISO’s intra-hour

balancing needs,” 2011 IEEE PES General Meeting, July 24-29, 2011, Detroit, Michigan.9. Y.V. Makarov, S. Lu, N. Samaan, Z. Huang, K. Subbarao, P. Etingov, J. Ma, R. Hafen, R. Diao, and N. Lu, “Integration of

uncertainty information into power system operations,” 2011 IEEE PES General Meeting, July 24-29, 2011, Detroit, Michigan.

10.Y.V. Makarov, J. Ma, C. Loutan, and Z. Xie, “Methodology of evaluating the impact of wind and solar generation on intra hour requirements within the California ISO balancing authority used in the 33% renewables penetration level study,” AWEA Windpower 2011 Conference & Exhibition, Anaheim, CA, May 22-25, 2011.

11.C. Loutan, Z. Xie, J. Ma, and Y.V. Makarov, “Evaluating the impact of 33% renewables on the California ISO balancing authority operational requirements,” AWEA Windpower 2011 Conference & Exhibition, Anaheim, CA, May 22-25, 2011.

12.Y.V. Makarov, N. Samaan, R. Diao, J. Ma, and R. Hafen, “Wide area uncertainty model for variable generation and load forecast errors,” AWEA Windpower 2011 Conference & Exhibition, Anaheim, CA, May 22-25, 2011.

13.Y.V. Makarov, J. Ma, N. Samaan, P.V. Etingov, C. Loutan, and M. Rothleder, “Prediction of day-ahead regulation requirement in the California ISO balancing area,” AWEA Windpower 2011 Conference & Exhibition, Anaheim, CA, May 22-25, 2011.

14.Y.V. Makarov, P.V. Etingov, J. Ma, and Z. Huang, “Incorporating of the wind generation forecast uncertainty into power system operation, dispatch, and unit commitment procedures,” The 9th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Québec, Canada, Oct. 18-19, 2010.

15.Y.V. Makarov, N. Zhou, P.V. Etingov, N. Samaan, J. Ma, R. Diao, and R.T. Guttromson, “Analyzing of balancing authorities cooperation methods with high variable generation penetration,” The 9th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Québec, Canada, Oct. 18-19, 2010.

16.P.V. Etingov, N. Zhou, Y.V. Makarov, J. Ma, R.T. Guttromson, B. A. McManus, and C. Loutan, “Possible improvements of the ACE diversity interchange methodology,” 2010 IEEE PES General Meeting, Minneapolis, Minnesota, July 25-29, 2010.

17.Y.V. Makarov, J. Ma, S. Lu, T.B. Nguyen, C. Loutan, G.R. Rosenblum, S.E. Chowdhury, J.H. Eto, M. Gravely, and M. Brown, “Value of fast regulation resources to balance against load and generation intermittency,” WINDPOWER 2010 Conference & Exhibition, Dallas, Texas, May 23-26, 2010.

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Publications (5)

Conference Papers (Cont.)18.Y.V. Makarov, B. Yang, J.G. DeSteese P. Nyeng, C.H. Miller, J. Ma, S. Lu, V.V. Viswanathan, D. Hammerstrom, B.

McManus, J. Pease, C. Loutan, and G.R. Rosenblum, "Sharing regulation resources among areas with high penetration of wind generation," WINDPOWER 2010 Conference & Exhibition, Dallas, Texas, May 23-26, 2010.

19.Y.V. Makarov, J. Ma, S. Lu, T.B. Nguyen, C. Loutan, G. Rosenblum, S. Chowdhury, J.H. Eto, M. Gravely, and M. Brown, “Assessing the value of regulation resources based on their time response characteristics,” The 8th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, Bremen, Germany, Oct. 14-15, 2009.

20.Y.V. Makarov, J. Ma, J.G. DeSteese, D.J. Hammerstrom, S. Lu, V.V. Viswanathan, C.H. Miller, B. Yang, P. Nyeng, J.H. Pease, C. Loutan, and G. Rosenblum, “Wide-area energy storage and management system to balance intermittent resources in the Bonneville Power Administration and California ISO control areas,” The 8th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, Bremen, Germany, Oct. 14-15, 2009.

21.Y.V. Makarov, J. Ma, S. Lu, T.B. Nguyen, C. Loutan, S. Chowdhury, J.H. Eto, and M. Gravely, “Assessing the value of regulation resources based on their time response characteristics,” IEEE PES/IAS Conference on Sustainable Alternative Energy, Valencia, Spain, Sep. 28-30, 2009.

22.Y. Makarov, J. DeSteese, C. Miller, S. Lu, P. Nyeng, J. Ma, D. Hammerstrom, J. Pease, B. McManus, C. Loutan, G. Rosenblum, M. Brown, and B. Yang, “Wide-area energy storage and management system to balance intermittent resources in the Bonneville Power Administration and California ISO control areas,” IEEE PES/IAS Conference on Sustainable Alternative Energy, Valencia, Spain, Sep. 28-30, 2009.

23.Y.V. Makarov, C. Loutan, J. Ma, P.de Mello, and S. Lu, “Impacts of wind generation on regulation and load following requirements in the California system,” in Proceedings of 2008 IEEE PES General Meeting, Pittsburgh, Pennsylvania, USA, July 20-24, 2008.

24.Y.V. Makarov, C. Loutan, J. Ma, P.de Mello, and S. Lu, “Evaluating impacts of wind generation on regulation and load following requirements for integrating intermittent resources,” in Proceedings of Windpower 2008 Conference and Exhibition, Houston, TX, June 1-4, 2008.

25.Y.V. Makarov, C. Loutan, J. Ma, P.de Mello, and S. Lu, “Impacts of integration of wind generation on regulation and load following requirements of California power systems,” in Proceedings of The 5th International Conference on the European Electricity Market (EEM08), Portugal, Lisbon, May 28-30, 2008.

Slide Number 1Slide Number 2Slide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Example of Load Following and Regulation Requirements Models for Balancing Requirements Uncertainty: Multidimensional Uncertainty AnalysisSlide Number 11Slide Number 122010 Day Ahead Load Forecast Error StatisticsSimulating Wind Forecast Error in the WECC VGS Study (as an example) Slide Number 15Slide Number 16Generated Day-Ahead Solar Forecast Error by BA (TEPPC 2020)Slide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Projects (1)Projects (2)Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32