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An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning

Author

Listed:
  • Guanglei Huang

    (Shenzhen Power Supply Company, China Southern Power Grid, Shenzhen 518067, China)

  • Tian Mao

    (Electric Power Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Bin Zhang

    (Shenzhen Power Supply Company, China Southern Power Grid, Shenzhen 518067, China)

  • Renli Cheng

    (Shenzhen Power Supply Company, China Southern Power Grid, Shenzhen 518067, China)

  • Mingyu Ou

    (Shenzhen Power Supply Company, China Southern Power Grid, Shenzhen 518067, China)

Abstract

With the reform of energy structures, the high proportion of volatile new energy access makes the existing unit commitment (UC) theory unable to satisfy the development demands of day-ahead market decision-making in the new power system. Therefore, this paper proposes an intelligent algorithm for solving UC, based on deep reinforcement learning (DRL) technology. Firstly, the DRL algorithm is used to model the Markov decision process of the UC problem, and the corresponding state space, transfer function, action space and reward function are proposed. Then, the policy gradient (PG) algorithm is used to solve the problem. On this basis, Lambda iteration is used to solve the output scheme of the unit in the start–stop state, and finally a DRL-based UC intelligent solution algorithm is proposed. The applicability and effectiveness of this method are verified based on simulation examples.

Suggested Citation

  • Guanglei Huang & Tian Mao & Bin Zhang & Renli Cheng & Mingyu Ou, 2023. "An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11084-:d:1194972
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    References listed on IDEAS

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