基于深度策略梯度的综合能源系统优化调度研究

王臣宇; 袁树福; 王志霄; 丁吉; 孙立

doi:10.19805/j.cnki.jcspe.2026.240740

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基于深度策略梯度的综合能源系统优化调度研究

综合能源系统 | 更新时间：2026-10-10

- 基于深度策略梯度的综合能源系统优化调度研究
- Research on Optimal Scheduling of Integrated Energy SystemsBased on Deep Deterministic Policy Gradient
- 动力工程学报 2026年46卷第2期页码：193-201
- 作者机构：
  
  1. 东南大学能源与环境学院, 大型发电装备安全运行与智能测控国家工程研究中心,江苏,南京,210018
  2. 苏州同元软控信息技术有限公司,江苏,苏州,215000
- 作者简介：
  
  王臣宇(2001—),男,江苏南京人,硕士研究生,研究方向为综合能源调度
  孙立(通信作者),男,教授,博士,E-mail:sunli12@seu.edu.cn
- 基金信息：
  
  江苏省科技厅资助科技项目(BK20240073,BA2023005)
- DOI：10.19805/j.cnki.jcspe.2026.240740
  中图分类号：
- 网络首发：2026-02-10，
  
  纸质出版：2026-02-10
- 稿件说明：
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王臣宇,袁树福,王志霄,丁吉,孙立. 基于深度策略梯度的综合能源系统优化调度研究动力工程学报, 2026, 46(2): 193-201 https://doi.

org/10.19805/j.cnki.jcspe.2026.240740
王臣宇,袁树福,王志霄,丁吉,孙立. 基于深度策略梯度的综合能源系统优化调度研究动力工程学报, 2026, 46(2): 193-201 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240740.

org/10.19805/j.cnki.jcspe.2026.240740 DOI：

摘要

随着可再生能源的广泛应用

综合能源系统的运行复杂性和不确定性增大

传统的调度方法难以高效应对多能流、强耦合等问题。因此

构建了一个涵盖光伏、风力、氢气、蓄电池、热泵和燃料电池等设备的综合能源系统模型

结合深度强化学习深度策略梯度(DDPG)算法

以实现在不确定性工况下的能源调度优化。结果表明:DDPG算法能够有效解决多能流协同调度问题

在不同负荷需求和能源供应条件下

实现了电、热、冷、氢的供需平衡和能源高效利用;通过训练

系统在面对不同的不确定性工况下展现出良好的调度能力和适应性

能够灵活应对能源的波动。

Abstract

With the widespread application of renewable energy

the operational complexity and uncertainty of integrated energy systems have increased significantly. Traditional scheduling methods are insufficient to efficiently address issues such as multi-energy flows and strong coupling. Therefore

an integrated energy system model encompassing photovoltaic panels

wind turbines

hydrogen storage tanks

batteries

heat pumps

and fuel cells was developed. By combining the deep deterministic policy gradient (DDPG) algorithm

the energy scheduling optimization under uncertain conditions was achieved. Results show that the DDPG algorithm can effectively solve the problem of multi-energy flow coordinated scheduling

achieving supply-demand balance and efficient utilization of electricity

heat

cooling

and hydrogen under different load demands and energy supply conditions. Moreover

after training

the system exhibits excellent scheduling performance and adaptability when confronted with diverse uncertain scenarios

enabling flexible responses to energy fluctuations.

关键词

Keywords

references

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