Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162427
Title: Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach
Authors: Wang, Jianzheng
Pang, Yipeng
Hu, Guoqiang
Keywords: Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Issue Date: 2022
Source: Wang, J., Pang, Y. & Hu, G. (2022). Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach. IEEE Transactions On Control Systems Technology. https://dx.doi.org/10.1109/TCST.2022.3154654
Project: S14-1172-NRF EIRP-IHL
Journal: IEEE Transactions on Control Systems Technology
Abstract: In the electricity market, it is quite common that the market participants make ``selfish'' strategies to harvest the maximum profits for themselves, which may cause the social benefit loss and impair the sustainability of the market in the long term. Regarding this issue, we will study how the social profit can be improved through strategic demand response management. Specifically, we explore two interaction mechanisms in the market: Nash game and Stackelberg game. At the user side, each user makes the respective energy-purchasing strategy to optimize its own profit. At the utility company (UC) side, we consider multiple self-centric UCs that play games. A social-centric governmental UC is established as the leader to optimize the social profit of the market through competition. Then, a multi-timescale leader-following problem of the UCs is formulated under the coordination of an independent system operator. By our proposed demand function amelioration strategy, the market efficiency is maximized. In addition, by considering some additional constraints of the market, two projection-based algorithms are proposed. The feasibility of the proposed algorithms is verified with an IEEE 9-bus system model in the simulation.
URI: https://hdl.handle.net/10356/162427
ISSN: 1063-6536
DOI: 10.1109/TCST.2022.3154654
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCST.2022.3154654.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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