A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

Xiang Gao, Haomin Ma, Ka Wing Chan, Shiwei Xia, Ziqing Zhu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

Original languageEnglish
Article number750796
JournalFrontiers in Energy Research
Volume9
DOIs
Publication statusPublished - 11 Oct 2021

Keywords

  • BESS
  • bidding strategy
  • incomplete information game
  • multiagent reinforcement learning
  • PV
  • WoLF-PHC

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Economics and Econometrics

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