电测与仪表2025，Vol.62Issue(5)：208-217,10.DOI:10.19753/j.issn1001-1390.2025.05.025
知识与强化学习融合的气电两用热水器需求响应优化
Demand response optimization of gas-electric water heater based on fusing knowledge and reinforcement learning
摘要
Abstract
Gas-electric water heater(GEWH)is an important load type for integrated demand response(IDR),in which the IDR optimization strategy is required of fast self-adaptiveness to conquer uncertainties in the load itself and in operation environment of GEWH.This paper investigates a solution that integrates knowledge in deep rein-forcement learning(DRL)method.We establish an optimization structure that couples the physical device,device model and optimization strategy automatically.We set up rule-based knowledge for IDR optimization.Furthermore,we design a DQN(deep Q-learning)-based optimization model with knowledge integration including common fea-tures of DQN,the method that optimization knowledge works in reward function and the control mechanism that co-ordinates the depthand probability of knowledge participation.Our case studies show that the proposed method is a-ble to automatically adapt to the uncertainties in GEWH load and its working environment and converge to the opti-mal solution.Compared with the demand response of electric water heater,IDR for GEWH reduces the energy cost by 18.7％.Moreover,the proposed method outperforms standard DQN by five times the convergence rate,which provides references for large-scale IDR optimization implementation for GEWH.关键词
气电两用热水器/综合需求响应/深度强化学习/知识规则/不确定性Key words
gas-electric water heater/integrated demand response/deep reinforcement learning/knowledge rule/un-certainty分类
信息技术与安全科学引用本文复制引用
杨晓坤,燕凯,张烁,刘岩,袁瑞铭,郑小平..知识与强化学习融合的气电两用热水器需求响应优化[J].电测与仪表,2025,62(5):208-217,10.基金项目
国家电网总部科技项目(5400-202211163A-1-1-ZN （）
国网冀北电力有限公司科技项目(52018520002U) （52018520002U）
