A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

Bhavsar, S.; Pitchumani, R.; Ortega-Vazquez, M. A.

doi:10.1016/j.epsr.2022.108157

A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

Journal Article · Sat Jun 18 00:00:00 EDT 2022 · Electric Power Systems Research

DOI:https://doi.org/10.1016/j.epsr.2022.108157· OSTI ID:1986484

Bhavsar, S. ^[1]; Pitchumani, R. ^[2]; Ortega-Vazquez, M. A. ^[3]

Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Electric Power Research Institute (EPRI)
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Electric Power Research Institute (EPRI), Palo Alto, CA (United States)

The installed capacity of renewables-based energy sources has been increasing in traditional power systems. In order to accommodate the increased variability and uncertainty associated with the deeper penetration of renewable sources like solar and wind, adjusted amounts of dynamic reserve are needed. Although probabilistic dynamic reserve estimation methods have been previously developed, most of them consider the uncertainty to be represented by parametric density functions that tend to perform poorly under extreme events and, moreover, neglect uncertainty introduced by the forecasting model itself. Toward addressing these limitations, this work presents, for the first time, a dynamic reserve estimation method for flexibility that incorporates nonparametric density estimation and a machine learning based reforecasting to provide a day-ahead prediction of the mean and spread of uncertainty around the base forecast. The prediction is, in turn, used to estimate the up and down reserve relative to the base forecast. Here, the present method takes various endogenous and exogenous features, including the calendar variables, as input to estimate the day-ahead reserve. Using a combination of reforecasting and dynamic reserve estimation techniques, the method is shown to adjust better to the dynamic nature of reserve requirements providing only what is needed to accommodate the expected deviations. Considering California Independent System Operator (CAISO) solar, wind and load data over an 18 month period, up to 67% reduction in the amount of reserve capacity needed for a one day reserve and reserve penalty for solar uncertainty is demonstrated. Additionally, the risk of reserve insufficiency in meeting the net demand is reduced by 20% with the proposed method.

View Accepted Manuscript (DOE)

Research Organization:: Electric Power Research Institute (EPRI), Palo Alto, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0008601

OSTI ID:: 1986484

Alternate ID(s):: OSTI ID: 1874722

Journal Information:: Electric Power Systems Research, Journal Name: Electric Power Systems Research Vol. 211; ISSN 0378-7796

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (18)

Optimum design of proportional-integral controllers in grid-integrated PMSG-based wind energy conversion system: Optimum Design of PI Controllers Tripathi, Saurabh M.; Tiwari, Amar Nath; Singh, Deependra International Transactions on Electrical Energy Systems, Vol. 26, Issue 5 https://doi.org/10.1002/etep.2120	journal	August 2015
Time series analysis univariate and multivariate methods Lai, T. H. International Journal of Forecasting, Vol. 7, Issue 3 https://doi.org/10.1016/0169-2070(91)90015-N	journal	November 1991
Ensemble re-forecasting methods for enhanced power load prediction Kaur, Amanpreet; Pedro, Hugo T. C.; Coimbra, Carlos F. M. Energy Conversion and Management, Vol. 80 https://doi.org/10.1016/j.enconman.2014.02.004	journal	April 2014
A new method for day-ahead sizing of control reserve in Germany under a 100% renewable energy sources scenario Jost, D.; Speckmann, M.; Sandau, F. Electric Power Systems Research, Vol. 119 https://doi.org/10.1016/j.epsr.2014.10.026	journal	February 2015
Estimating the additional operating reserve in power systems with installed renewable energy sources Rejc, Živa Bricman; Čepin, Marko International Journal of Electrical Power & Energy Systems, Vol. 62 https://doi.org/10.1016/j.ijepes.2014.05.019	journal	November 2014
Time-adaptive quantile-copula for wind power probabilistic forecasting Bessa, Ricardo J.; Miranda, V.; Botterud, A. Renewable Energy, Vol. 40, Issue 1 https://doi.org/10.1016/j.renene.2011.08.015	journal	April 2012
Review on probabilistic forecasting of wind power generation Zhang, Yao; Wang, Jianxue; Wang, Xifan Renewable and Sustainable Energy Reviews, Vol. 32 https://doi.org/10.1016/j.rser.2014.01.033	journal	April 2014
Short-term reforecasting of power output from a 48 MWe solar PV plant Chu, Yinghao; Urquhart, Bryan; Gohari, Seyyed M. I. Solar Energy, Vol. 112 https://doi.org/10.1016/j.solener.2014.11.017	journal	February 2015
Wind generation as a reserve provider Dvorkin, Yury; Ortega‐Vazuqez, Miguel A.; Kirschen, Daniel S. IET Generation, Transmission & Distribution, Vol. 9, Issue 8 https://doi.org/10.1049/iet-gtd.2014.0614	journal	May 2015
Nonparametric Random Variate Generation Using a Piecewise-Linear Cumulative Distribution Function Kaczynski, W.; Leemis, L.; Loehr, N. Communications in Statistics - Simulation and Computation, Vol. 41, Issue 4 https://doi.org/10.1080/03610918.2011.606947	journal	December 2011
Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration Ortega-Vazquez, M. A.; Kirschen, D. S. IEEE Transactions on Power Systems, Vol. 24, Issue 1, p. 114-124 https://doi.org/10.1109/TPWRS.2008.2004745	journal	February 2009
Dynamic Scheduling of Operating Reserves in Co-Optimized Electricity Markets With Wind Power Zhou, Zhi; Botterud, Audun IEEE Transactions on Power Systems, Vol. 29, Issue 1 https://doi.org/10.1109/TPWRS.2013.2281504	journal	January 2014
Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks Fahiman, Fateme; Disano, Steven; Erfani, Sarah Monazam IEEE Transactions on Power Systems, Vol. 34, Issue 3 https://doi.org/10.1109/TPWRS.2018.2884711	journal	May 2019
Methodologies to Determine Operating Reserves Due to Increased Wind Power Holttinen, Hannele; Milligan, Michael; Ela, Erik IEEE Transactions on Sustainable Energy, Vol. 3, Issue 4 https://doi.org/10.1109/TSTE.2012.2208207	journal	October 2012
A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing Bruninx, Kenneth; Delarue, Erik IEEE Transactions on Sustainable Energy, Vol. 5, Issue 3 https://doi.org/10.1109/TSTE.2014.2320193	journal	July 2014
Impacts of Dynamic Probabilistic Reserve Sizing Techniques on Reserve Requirements and System Costs Bucksteeg, Michael; Niesen, Lenja; Weber, Christoph IEEE Transactions on Sustainable Energy, Vol. 7, Issue 4 https://doi.org/10.1109/TSTE.2016.2555483	journal	October 2016
Managing uncertainty in electricity generation and demand forecasting Warren, K.; Ambrosio, R.; Chen, B. IBM Journal of Research and Development, Vol. 60, Issue 1 https://doi.org/10.1147/JRD.2015.2496822	journal	January 2016
Using Standard Deviation as a Measure of Increased Operational Reserve Requirement for Wind Power Holttinen, Hannele; Milligan, Michael; Kirby, Brendan Wind Engineering, Vol. 32, Issue 4 https://doi.org/10.1260/0309-524X.32.4.355	journal	June 2008

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