基于安全强化学习的热电联产机组经济调度策略研究  

Research on Economic Dispatching Strategy of CHP Units Based on SRL

作　　者：王欣 崔承刚 王想想 朱平 Wang Xin;Cui Chenggang;Wang Xiangxiang;Zhu Ping(College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China)

机构地区：[1]上海电力大学自动化工程学院,上海200090

出　　处：《系统仿真学报》2025年第4期968-981,共14页Journal of System Simulation

基　　金：国家自然科学基金青年科学基金(51607111);上海市2021年度“科技创新行动计划”科技支撑碳达峰碳中和专项(21DZ1207302)。

摘　　要：针对DRL算法在热电联产(combined heat and power,CHP)机组优化中缺乏安全性和稳定性保证的问题,提出了一种基于安全强化学习(SRL)的调度优化方法。在Dymola平台以CHP机组为热源建立了区域供热系统模型。设计了CHP机组经济调度的MDP模型,并通过控制障碍函数(control barrier functions,CBF)指导DRL安全探索。仿真结果表明:CBF-DRL方法在复杂且非线性的区域供热系统中,不仅能够提升DRL算法的收敛速度,还能够有效利用供热管道的热惯性提高CHP机组的经济效益,并在安全性方面表现出优势。In addressing the challenge of the DRL algorithm in the optimization of combined heat and power(CHP)units,lacking safety and stability guarantees,a scheduling optimization method based on SRL is proposed.Utilizing Dymola platform,a district heating system model is constructed with the CHP unit as the heat source.A MDP model for the economic dispatching of CHP units is designed,incorporating control barrier functions(CBF)to guide safe exploration in DRL.Simulation results show that the CBF-DRL method,in complex and nonlinear district heating systems,not only accelerates the convergence of DRL algorithms but also efficiently utilizes the thermal inertia of heating pipelines to enhance the economic performance of CHP units.In particular,it has significant safety benefits.

关 键 词：热电联产 区域供热系统 安全强化学习 控制障碍函数 经济调度 协同仿真 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]