Wind power bidding coordinated with energy storage system operation in real-time electricity market: A maximum entropy deep reinforcement learning approach

Xiangyu Wei, Yue Xiang, Junlong Li, Junyong Liu

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

Original languageEnglish
Pages (from-to)770-775
Number of pages6
JournalEnergy Reports
Volume8
Issue number1
Early online date3 Dec 2021
DOIs
Publication statusPublished - 30 Apr 2022

Keywords

  • Deep RL
  • Electricity market
  • Energy storage system
  • Maximum entropy
  • Soft actor-critic
  • Wind farm

ASJC Scopus subject areas

  • Energy(all)

Fingerprint

Dive into the research topics of 'Wind power bidding coordinated with energy storage system operation in real-time electricity market: A maximum entropy deep reinforcement learning approach'. Together they form a unique fingerprint.

Cite this