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

Guanglei Huang (), Tian Mao, Bin Zhang, Renli Cheng and Mingyu Ou
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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

Sustainability, 2023, vol. 15, issue 14, 1-19

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.

Keywords: safety restraint unit combination; Markov decision process; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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