How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Hobbs, Benjamin F.; Krishnan, Venkat; Zhang, Jie; Hamann, Hendrik F.; Siebenschuh, Carlo; Zhang, Rui; Li, Binghui; He, Li; Edwards, Paul; Sky, Haiku; Krad, Ibrahim; Spyrou, Evangelia; Fang, Xin; Wang, Yijiao; Xu, Q.; Zhang, Shu

doi:10.1109/oajpe.2022.3217909

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Journal Article · Fri Oct 28 00:00:00 EDT 2022 · IEEE Open Access Journal of Power and Energy

DOI:https://doi.org/10.1109/oajpe.2022.3217909· OSTI ID:1903776

^[1]; Krishnan, Venkat ^[2]; ^[3]; Hamann, Hendrik F. ^[4]; Siebenschuh, Carlo ^[4]; Zhang, Rui ^[4]; ^[3]; ^[3]; Edwards, Paul ^[2]; Sky, Haiku ^[2]; Krad, Ibrahim ^[2]; ^[2]; ^[2]; Wang, Yijiao ^[1]; Xu, Q. ^[1]; Zhang, Shu ^[1]

Johns Hopkins Univ., Baltimore, MD (United States). Robert O'Connor Sustainable Energy Inst.
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of Texas at Dallas, Richardson, TX (United States)
IBM Thomas J. Watson Center, Yorktown Heights, NY (United States)

Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

View Accepted Manuscript (DOE)

Research Organization:: Johns Hopkins University, Baltimore, MD (United States); National Renewable Energy Laboratory (NREL), Golden, CO (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:: AC36-08GO28308; EE0008215

OSTI ID:: 1903776

Alternate ID(s):: OSTI ID: 1991271

Report Number(s):: NREL/JA-6A20-84782; MainId:85555; UUID:089357ae-fc52-4dce-9b37-c84db1f872d3; MainAdminID:68247

Journal Information:: IEEE Open Access Journal of Power and Energy, Journal Name: IEEE Open Access Journal of Power and Energy Vol. 9; ISSN 2687-7910

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (49)

Application of probabilistic wind power forecasting in electricity markets Zhou, Z.; Botterud, A.; Wang, J. Wind Energy, Vol. 16, Issue 3 https://doi.org/10.1002/we.1496	journal	March 2012
Wind power forecasting uncertainty and unit commitment Wang, J.; Botterud, A.; Bessa, R. Applied Energy, Vol. 88, Issue 11 https://doi.org/10.1016/j.apenergy.2011.04.011	journal	November 2011
Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts Bhavsar, S.; Pitchumani, R.; Ortega-Vazquez, M. A. Applied Energy, Vol. 293 https://doi.org/10.1016/j.apenergy.2021.116964	journal	July 2021
Sizing ramping reserve using probabilistic solar forecasts: A data-driven method Li, Binghui; Feng, Cong; Siebenschuh, Carlo Applied Energy, Vol. 313 https://doi.org/10.1016/j.apenergy.2022.118812	journal	May 2022
Economic and environmental implications of different approaches to hedge against wind production uncertainty in two-settlement electricity markets: A PJM case study Daraeepour, Ali; Patino-Echeverri, Dalia; Conejo, Antonio J. Energy Economics, Vol. 80 https://doi.org/10.1016/j.eneco.2019.01.015	journal	May 2019
Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium De Vos, K.; Stevens, N.; Devolder, O. Energy Policy, Vol. 124 https://doi.org/10.1016/j.enpol.2018.09.031	journal	January 2019
Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool Voyant, Cyril; Notton, Gilles Renewable and Sustainable Energy Reviews, Vol. 92 https://doi.org/10.1016/j.rser.2018.04.116	journal	September 2018
State-of-the-art short-term electricity market operation with solar generation: A review Yin, S.; Wang, J.; Li, Z. Renewable and Sustainable Energy Reviews, Vol. 138 https://doi.org/10.1016/j.rser.2020.110647	journal	March 2021
The value of day-ahead solar power forecasting improvement Brancucci Martinez-Anido, Carlo; Botor, Benjamin; Florita, Anthony R. Solar Energy, Vol. 129 https://doi.org/10.1016/j.solener.2016.01.049	journal	May 2016
Review of photovoltaic power forecasting Antonanzas, J.; Osorio, N.; Escobar, R. Solar Energy, Vol. 136 https://doi.org/10.1016/j.solener.2016.06.069	journal	October 2016
A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique Kumler, Andrew; Xie, Yu; Zhang, Yingchen Solar Energy, Vol. 177 https://doi.org/10.1016/j.solener.2018.11.046	journal	January 2019
Making reference solar forecasts with climatology, persistence, and their optimal convex combination Yang, Dazhi Solar Energy, Vol. 193 https://doi.org/10.1016/j.solener.2019.10.006	journal	November 2019
A review on the integration of probabilistic solar forecasting in power systems Li, Binghui; Zhang, Jie Solar Energy, Vol. 210 https://doi.org/10.1016/j.solener.2020.07.066	journal	November 2020
A clustering-based scenario generation framework for power market simulation with wind integration Li, Binghui; Sedzro, Kwami; Fang, Xin Journal of Renewable and Sustainable Energy, Vol. 12, Issue 3 https://doi.org/10.1063/5.0006480	journal	May 2020
Machine learning derived dynamic operating reserve requirements in high-renewable power systems Sun, Yuchi; Nelson, James H.; Stevens, John C. Journal of Renewable and Sustainable Energy, Vol. 14, Issue 3 https://doi.org/10.1063/5.0087144	journal	May 2022
PAIRS: A scalable geo-spatial data analytics platform Klein, Levente J.; Marianno, Fernando J.; Albrecht, Conrad M. 2015 IEEE International Conference on Big Data (Big Data) https://doi.org/10.1109/BigData.2015.7363884	conference	October 2015
IBM PAIRS curated big data service for accelerated geospatial data analytics and discovery Lu, Siyuan; Shao, Xiaoyan; Freitag, Marcus 2016 IEEE International Conference on Big Data (Big Data) https://doi.org/10.1109/BigData.2016.7840910	conference	December 2016
Dynamic sizing of automatic and manual frequency restoration reserves for different product lengths Jost, Dominik; Braun, Axel; Fritz, Rafael 2016 13th International Conference on the European Energy Market (EEM) https://doi.org/10.1109/EEM.2016.7521276	conference	June 2016
Dynamic dimensioning of balancing reserve Sprey, J. D.; Schultheis, P.; Moser, A. 2017 14th International Conference on the European Energy Market (EEM) https://doi.org/10.1109/EEM.2017.7981896	conference	June 2017
A Copula Enhanced Convolution for Uncertainty Aggregation Li, Binghui; Zhang, Jie; Hobbs, Benjamin F. 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) https://doi.org/10.1109/ISGT45199.2020.9087644	conference	February 2020
Reinventing 2D Convolutions for 3D Images Yang, Jiancheng; Huang, Xiaoyang; He, Yi IEEE Journal of Biomedical and Health Informatics, Vol. 25, Issue 8 https://doi.org/10.1109/JBHI.2021.3049452	journal	August 2021
Solar Forecasting: Methods, Challenges, and Performance Tuohy, Aidan; Zack, John; Haupt, Sue Ellen IEEE Power and Energy Magazine, Vol. 13, Issue 6 https://doi.org/10.1109/MPE.2015.2461351	journal	November 2015
The Use of Probabilistic Forecasts: Applying Them in Theory and Practice Haupt, Sue Ellen; Pestana, Rui; Zack, John IEEE Power and Energy Magazine, Vol. 17, Issue 6 https://doi.org/10.1109/MPE.2019.2932639	journal	November 2019
Forecasting and Market Design Advances: Supporting an Increasing Share of Renewable Energy Fox, Jack; Ela, Erik; Hobbs, Ben IEEE Power and Energy Magazine, Vol. 19, Issue 6 https://doi.org/10.1109/MPE.2021.3104132	journal	November 2021
A Data-driven Method for Adaptive Reserve Requirement Estimation via Probabilistic Net Load Forecasting Feng, Cong; Sun, Mucun; Zhang, Jie 2020 IEEE Power & Energy Society General Meeting (PESGM) https://doi.org/10.1109/PESGM41954.2020.9282155	conference	August 2020
Enhancing Power System Operational Flexibility With Flexible Ramping Products: A Review Wang, Qin; Hodge, Bri-Mathias IEEE Transactions on Industrial Informatics, Vol. 13, Issue 4 https://doi.org/10.1109/TII.2016.2637879	journal	August 2017
Optimizing the Spinning Reserve Requirements Using a Cost/Benefit Analysis Ortega-Vazquez, Miguel A.; Kirschen, Daniel S. IEEE Transactions on Power Systems, Vol. 22, Issue 1 https://doi.org/10.1109/TPWRS.2006.888951	journal	February 2007
Bringing Wind Energy to Market Bitar, Eilyan Y.; Rajagopal, Ram; Khargonekar, Pramod P. IEEE Transactions on Power Systems, Vol. 27, Issue 3 https://doi.org/10.1109/TPWRS.2012.2183395	journal	August 2012
Studying the Variability and Uncertainty Impacts of Variable Generation at Multiple Timescales Ela, Erik; O'Malley, Mark IEEE Transactions on Power Systems, Vol. 27, Issue 3 https://doi.org/10.1109/TPWRS.2012.2185816	journal	August 2012
Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem Bertsimas, Dimitris; Litvinov, Eugene; Sun, Xu Andy IEEE Transactions on Power Systems, Vol. 28, Issue 1 https://doi.org/10.1109/TPWRS.2012.2205021	journal	February 2013
Applying High Performance Computing to Transmission-Constrained Stochastic Unit Commitment for Renewable Energy Integration Papavasiliou, Anthony; Oren, Shmuel S.; Rountree, Barry IEEE Transactions on Power Systems, Vol. 30, Issue 3 https://doi.org/10.1109/TPWRS.2014.2341354	journal	May 2015
Tutorial on Stochastic Optimization in Energy—Part I: Modeling and Policies Powell, Warren B.; Meisel, Stephan IEEE Transactions on Power Systems, Vol. 31, Issue 2 https://doi.org/10.1109/TPWRS.2015.2424974	journal	March 2016
An Improved Stochastic Unit Commitment Formulation to Accommodate Wind Uncertainty Uckun, Canan; Botterud, Audun; Birge, John R. IEEE Transactions on Power Systems, Vol. 31, Issue 4 https://doi.org/10.1109/TPWRS.2015.2461014	journal	July 2016
Value of Flexible Resources, Virtual Bidding, and Self-Scheduling in Two-Settlement Electricity Markets with Wind Generation—Part I: Principles and Competitive Model Kazempour, Jalal; Hobbs, Benjamin F. IEEE Transactions on Power Systems, Vol. 33, Issue 1 https://doi.org/10.1109/TPWRS.2017.2699687	journal	January 2018
Estimation of Regulation Reserve Requirement Based on Control Performance Standard Zhang, Guangyuan; McCalley, James D. IEEE Transactions on Power Systems, Vol. 33, Issue 2 https://doi.org/10.1109/TPWRS.2017.2734654	journal	March 2018
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks Chen, Yize; Wang, Yishen; Kirschen, Daniel IEEE Transactions on Power Systems, Vol. 33, Issue 3 https://doi.org/10.1109/TPWRS.2018.2794541	journal	May 2018
Setting Reserve Requirements to Approximate the Efficiency of the Stochastic Dispatch Dvorkin, Vladimir; Delikaraoglou, Stefanos; Morales, Juan M. IEEE Transactions on Power Systems, Vol. 34, Issue 2 https://doi.org/10.1109/TPWRS.2018.2878723	journal	March 2019
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
A Chance-Constrained Stochastic Electricity Market Dvorkin, Yury IEEE Transactions on Power Systems, Vol. 35, Issue 4 https://doi.org/10.1109/TPWRS.2019.2961231	journal	July 2020
Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology Zhao, Changfei; Wan, Can; Song, Yonghua IEEE Transactions on Power Systems, Vol. 36, Issue 4 https://doi.org/10.1109/TPWRS.2021.3053847	journal	July 2021
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
Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method Cui, Mingjian; Ke, Deping; Sun, Yuanzhang IEEE Transactions on Sustainable Energy, Vol. 6, Issue 2 https://doi.org/10.1109/TSTE.2014.2386870	journal	April 2015
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
Operating Reserve Dimensioning Methodologies for Renewable Energy Aligned Power Systems Bongers, Leigh; Mararakanye, Ndamulelo; Bekker, Bernard 2021 56th International Universities Power Engineering Conference (UPEC) https://doi.org/10.1109/UPEC50034.2021.9548228	conference	August 2021
Strictly Proper Scoring Rules, Prediction, and Estimation Gneiting, Tilmann; Raftery, Adrian E. Journal of the American Statistical Association, Vol. 102, Issue 477 https://doi.org/10.1198/016214506000001437	journal	March 2007
Quantile Regression Koenker, Roger; Hallock, Kevin F. Journal of Economic Perspectives, Vol. 15, Issue 4 https://doi.org/10.1257/jep.15.4.143	journal	November 2001
Solar-to-Grid: Trends in System Impacts, Reliability, and Market Value in the United States with Data Through 2019 Mills, Andrew; Seel, Joachim; Millstein, Dev https://doi.org/10.2172/1764566	report	February 2021
RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness - An Introduction to the Open-Source Tool and Use Cases Edwards, Paul; Sky, Haiku; Krishnan, Venkat https://doi.org/10.2172/1782444	report	May 2021
An Overview of Probabilistic Dimensioning of Frequency Restoration Reserves with a Focus on the Greek Electricity Market Papavasiliou, Anthony Energies, Vol. 14, Issue 18 https://doi.org/10.3390/en14185719	journal	September 2021

Similar Records

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

Technical Report · Mon Jun 13 00:00:00 EDT 2022 · OSTI ID:1873393

Pro2R: Procurement of Ramping Product and Regulation in CAISO Using Probabilistic Solar Power Forecasts

Conference · Fri May 28 00:00:00 EDT 2021 · OSTI ID:1785961

Related Subjects

14 SOLAR ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
29 ENERGY PLANNING, POLICY, AND ECONOMY
97 MATHEMATICS AND COMPUTING
California
Power markets
probabilistic solar forecasts
renewable energy
operating reserves
California
flexiramp
contingency management
costs
flexiramp
operating reserves
power markets
probabilistic logic
probabilistic solar forecasts
procurement
renewable energy
renewable energy sources
schedules
uncertainty

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Citation Formats

References (49)

Similar Records

Related Subjects