Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach
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 ho...
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sg-ntu-dr.10356-1624272022-10-19T04:32:56Z Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach Wang, Jianzheng Pang, Yipeng Hu, Guoqiang School of Electrical and Electronic Engineering Centre for system intelligence and efficiency (EXQUISITUS) Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Electricity Market Leader-Following Approach 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. Economic Development Board (EDB) National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by the Singapore Economic Development Board under EIRP Grant S14-1172-NRF EIRP-IHL and in part by the Republic of Singapore National Research Foundation under its Campus for Research Excellence and Technological Enterprise (CREATE) Program through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. 2022-10-19T04:32:56Z 2022-10-19T04:32:56Z 2022 Journal Article 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 1063-6536 https://hdl.handle.net/10356/162427 10.1109/TCST.2022.3154654 2-s2.0-85126299657 en S14-1172-NRF EIRP-IHL IEEE Transactions on Control Systems Technology © 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. application/pdf |
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Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Electricity Market Leader-Following Approach Wang, Jianzheng Pang, Yipeng Hu, Guoqiang Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Wang, Jianzheng Pang, Yipeng Hu, Guoqiang |
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Article |
author |
Wang, Jianzheng Pang, Yipeng Hu, Guoqiang |
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Wang, Jianzheng |
title |
Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
title_short |
Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
title_full |
Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
title_fullStr |
Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
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Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
title_sort |
social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach |
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2022 |
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https://hdl.handle.net/10356/162427 |
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