4.5 Article

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

Journal

FRONTIERS IN ENERGY RESEARCH
Volume 9, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fenrg.2021.750796

Keywords

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

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Funding

  1. Shenzhen Polytechnic
  2. Hong Kong Polytechnic University
  3. Natural Science Foundation of Guangdong Province [2020A1515010461]
  4. National Natural Science Foundation of China [52077075]
  5. Jiangsu Basic Research Project [BK20180284]

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The study introduces a bidding model for PV-integrated BESS power plants in a day-ahead market, utilizing multiagent reinforcement learning to explore optimal bid prices, validating computational performance and analyzing bidding strategies of each participant.
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.

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