Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Liang, Zhirui; Li, Qi; Comden, Joshua; Bernstein, Andrey; Dvorkin, Yury

doi:10.1109/CDC56724.2024.10886163

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Conference · Wed Feb 26 04:00:00 EST 2025

DOI:https://doi.org/10.1109/CDC56724.2024.10886163· OSTI ID:2549274

Liang, Zhirui; Li, Qi; Comden, Joshua; Bernstein, Andrey; Dvorkin, Yury

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 2549274

Report Number(s):: NREL/CP-5D00-94095; MainId:95877; UUID:e2ca48fe-3493-4d44-b293-9bd0b08b6788; MainAdminId:76511

Country of Publication:: United States

Language:: English

References (20)

Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations Mohajerin Esfahani, Peyman; Kuhn, Daniel Mathematical Programming, Vol. 171, Issue 1-2 https://doi.org/10.1007/s10107-017-1172-1	journal	July 2017
Prescribed robustness in optimal power flow Mieth, Robert; Poor, H. Vincent Electric Power Systems Research, Vol. 235 https://doi.org/10.1016/j.epsr.2024.110704	journal	October 2024
Convex Optimization Boyd, Stephen; Vandenberghe, Lieven https://doi.org/10.1017/CBO9780511804441	book	January 2004
Electric demand response and bounded rationality: mean-field control for large populations of heterogeneous bounded-rational agents Marín Radoszynski, Andrea; Pinson, Pierre Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 379, Issue 2202 https://doi.org/10.1098/rsta.2019.0429	journal	June 2021
Linear power-flow models in multiphase distribution networks Bernstein, Andrey; Dall'Anese, Emiliano 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) https://doi.org/10.1109/ISGTEurope.2017.8260205	conference	September 2017
Revealing Decision Conservativeness Through Inverse Distributionally Robust Optimization Li, Qi; Liang, Zhirui; Bernstein, Andrey IEEE Control Systems Letters, Vol. 8 https://doi.org/10.1109/LCSYS.2024.3407629	journal	January 2024
Grid Edge Visibility: Gaps and a road map Zhang, Yingchen Y. C.; Feng, Cong; Shaffery, Peter IEEE Electrification Magazine, Vol. 10, Issue 4 https://doi.org/10.1109/MELE.2022.3211015	journal	December 2022
Unleash Values From Grid-Edge Flexibility: An overview, experience, and vision for leveraging grid-edge distributed energy resources to improve grid operations Ding, Fei; Liu, Weijia; Kumar, Utkarsh IEEE Electrification Magazine, Vol. 10, Issue 4 https://doi.org/10.1109/MELE.2022.3211017	journal	December 2022
An Incentive-Based Online Optimization Framework for Distribution Grids Zhou, Xinyang; Dall'Anese, Emiliano; Chen, Lijun IEEE Transactions on Automatic Control, Vol. 63, Issue 7 https://doi.org/10.1109/TAC.2017.2760284	journal	July 2018
Data Valuation From Data-Driven Optimization Mieth, Robert; Morales, Juan M.; Poor, H. Vincent IEEE Transactions on Control of Network Systems, Vol. 12, Issue 1 https://doi.org/10.1109/TCNS.2024.3431415	journal	March 2025
An Incentive-Based Demand Response (DR) Model Considering Composited DR Resources Yu, Mengmeng; Hong, Seung Ho; Ding, Yuemin IEEE Transactions on Industrial Electronics, Vol. 66, Issue 2 https://doi.org/10.1109/TIE.2018.2826454	journal	February 2019
Stochastic and Distributionally Robust Load Ensemble Control Hassan, Ali; Mieth, Robert; Deka, Deepjyoti IEEE Transactions on Power Systems, Vol. 35, Issue 6 https://doi.org/10.1109/TPWRS.2020.2992268	journal	November 2020
An Optimal and Learning-Based Demand Response and Home Energy Management System Zhang, Dong; Li, Shuhui; Sun, Min IEEE Transactions on Smart Grid, Vol. 7, Issue 4 https://doi.org/10.1109/TSG.2016.2552169	journal	July 2016
Online Learning for Network Constrained Demand Response Pricing in Distribution Systems Mieth, Robert; Dvorkin, Yury IEEE Transactions on Smart Grid, Vol. 11, Issue 3 https://doi.org/10.1109/TSG.2019.2957705	journal	May 2020
Constrained Thompson Sampling for Real-Time Electricity Pricing With Grid Reliability Constraints Tucker, Nathaniel; Moradipari, Ahmadreza; Alizadeh, Mahnoosh IEEE Transactions on Smart Grid, Vol. 11, Issue 6 https://doi.org/10.1109/TSG.2020.3004770	journal	November 2020
Incentivizing Prosumer Voltage Regulation for Unbalanced Radial Distribution Networks Yang, Jiakang; Alam, Mollah Rezaul; Tushar, Wayes IEEE Transactions on Sustainable Energy, Vol. 15, Issue 1 https://doi.org/10.1109/TSTE.2023.3274154	journal	January 2024
Demand response model identification and behavior forecast with OptNet Bian, Yuexin; Zheng, Ningkun; Zheng, Yang Proceedings of the Thirteenth ACM International Conference on Future Energy Systems https://doi.org/10.1145/3538637.3538871	conference	June 2022
Adaptive Distributionally Robust Optimization Bertsimas, Dimitris; Sim, Melvyn; Zhang, Meilin Management Science, Vol. 65, Issue 2 https://doi.org/10.1287/mnsc.2017.2952	journal	February 2019
Optimization of conditional value-at-risk Rockafellar, R. Tyrrell; Uryasev, Stanislav The Journal of Risk, Vol. 2, Issue 3 https://doi.org/10.21314/JOR.2000.038	journal	January 2000
Who knows what: information barriers to efficient DER roll-out Bialek, Sylwia; Dvorkin, Yury; Kim, Jip https://doi.org/10.2139/ssrn.3844269	preprint	May 2021

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