高压电器2025，Vol.61Issue(5)：197-207,11.DOI:10.13296/j.1001-1609.hva.2025.05.021
基于深度强化学习的机组组合智能求解算法
Intelligent Solution Algorithm for Unit Commitment Based on Deep Reinforcement Learning
母欢欢 1余凌 1夏凡 1袁业1
作者信息
- 1. 华电(林周)新能源有限公司,拉萨 851600
- 折叠
摘要
Abstract
With the continuous and deep transformation of energy structure in China,the access of the high propor-tion of volatile new energy makes the existing unit commitment theory unable to suit the development demands of the present market decision-making in the new power system.Therefore,a kind of UC intelligent solution algorithm in combination with the deep reinforcement learning(DRL)technology is proposed.Firstly,the DRL algorithm is intro-duced to model the Markov decision process of UC problem and the corresponding state space,transfer function,ac-tion space and reward function are given.Then,the strategy policy gradient(PG)algorithm is adopted for solution and,on this basis,the Lambda iteration are adopted to solve the output scheme of the unit under the startup and shutdown state respectively.Finally,a DRL-based UC intelligent solution algorithm is proposed.The applicability and effectiveness of this method are verified based on simulation examples.关键词
安全约束机组组合/马尔科夫决策过程/深度强化学习Key words
security constrained unit commitment/Markov decision process/deep reinforcement learning引用本文复制引用
母欢欢,余凌,夏凡,袁业..基于深度强化学习的机组组合智能求解算法[J].高压电器,2025,61(5):197-207,11.
