Recursive learning based smart energy management with two-Level dynamic pricing demand response

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dc.contributor.author Zhang, Huifeng
dc.contributor.author Huang, Jiapeng
dc.contributor.author Yue, Dong
dc.contributor.author Xie, Xiangpeng
dc.contributor.author Zhang, Zhijun
dc.contributor.author Hancke, Gerhard P.
dc.date.accessioned 2024-10-23T13:05:56Z
dc.date.available 2024-10-23T13:05:56Z
dc.date.issued 2024
dc.description.abstract Due to dynamic characteristic of demand response and stochastic nature of power generation, it brings great challenge to smart energy management. In this paper, a demand response model is created with two-level dynamic pricing transaction among grid operator, service provider and customers, which also involves customers’ active participation with load shifting issue. To effectively control system load on the demand side, an improved deep reinforcement learning approach is proposed with a recursive least square (RLS) technique to deal with the dynamic pricing demand response problem, which accelerates the on-line training and optimization efficiency. On the power generation side, a probabilistic penalty-based boundary intersection (PBI) based multi-objective optimization algorithm is improved to optimize the economic cost, emission rate and statistic voltage stability index (SVSI) simultaneously with generated stochastic scenarios, which can ensure energy conservation and environmental protection, as well as system security. The case results reveal that the proposed two-level optimization strategy successfully deals with energy management with dynamic pricing demand response Note to Practitioners —This paper is motivated by solving stochastic energy management issue of isolated power system with dynamic pricing demand response. Those existing methods merely focus on the load demand or power generation side, and the methods for demand response issue lacks efficient on-line learning ability, while this work proposes a recursive least square based deep reinforcement learning approach to tackle with the two-level dynamic pricing demand response issue, scenario based PBI multi-objective optimization is proposed to solve the power dispatch issue on power generation side, and the numerical analysis results suggest that the proposed optimization strategy can deal with the whole energy management issue well. The future work will focus on the dynamic power-load coordination in the energy management issue. en_US
dc.description.department Electrical, Electronic and Computer Engineering en_US
dc.description.librarian hj2024 en_US
dc.description.sdg SDG-07:Affordable and clean energy en_US
dc.description.sdg SDG-09: Industry, innovation and infrastructure en_US
dc.description.sponsorship In part by the National Natural Science Fund, the Basic Research Project of Leading Technology of Jiangsu Province and the National Natural Science Fund of Jiangsu Province. en_US
dc.description.uri http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=8856 en_US
dc.identifier.citation H. Zhang, J. Huang, D. Yue, X. Xie, Z. Zhang and G.P. Hancke, "Recursive Learning Based Smart Energy Management With Two-Level Dynamic Pricing Demand Response," in IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2024.3446849. en_US
dc.identifier.issn 1545-5955 (print)
dc.identifier.issn 1558-3783 (online)
dc.identifier.other 10.1109/TASE.2024.3446849
dc.identifier.uri http://hdl.handle.net/2263/98732
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.rights © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. en_US
dc.subject Recursive least square (RLS) en_US
dc.subject Demand response en_US
dc.subject Pricing en_US
dc.subject Penalty-based boundary intersection (PBI) en_US
dc.subject Power generation en_US
dc.subject Load modeling en_US
dc.subject Energy management en_US
dc.subject Optimization en_US
dc.subject Power system dynamics en_US
dc.subject Stochastic en_US
dc.subject Energy management en_US
dc.subject Reinforcement learning (RL) en_US
dc.subject Multi-objective optimization en_US
dc.subject SDG-09: Industry, innovation and infrastructure en_US
dc.subject SDG-07: Affordable and clean energy en_US
dc.title Recursive learning based smart energy management with two-Level dynamic pricing demand response en_US
dc.type Postprint Article en_US


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