Energy management strategy of integrated energy system considering demand response by using deep reinforcement learning
TANG Hao
ZHANG Qing-hu
FANG Dao-hong
ZHU Hong
WU Yin-tao
Abstract：An integrated energy system(IES)that incorporates distributed energy resources such as photovoltaics,ener-gy storage,and gas turbines has the potential to provide a multi-energy coordinated and complementary energy utilization form,which can play an important role in participating in grid demand response.To effectively respond to grid peak regulation demands,this paper proposes an optimization method for IES intraday scheduling considering multi-energy complementarity and internal user response as the energy management means of IES.Firstly,based on the multi-energy coupling operation architecture of IES,the response characteristics of internal users are analyzed.The electric load demand of internal users is changed by subsidy price and load reduction respectively,and then the energy management strategy optimization model of IES participating in power grid demand response under photovoltaic output and load uncertainty is constructed.Secondly,the deep reinforcement learning algorithm based on TD3 is used to solve the IES energy man-agement strategy.Finally,the case study shows that the proposed energy management strategy optimization model and strategy optimization method can reasonably formulate the energy conversion control and demand response scheme within the system to fully tap the response potential of the system and effectively achieve the peak regulation demand response goal of the grid.
Keywords：integrated energy systemsmultiple complementationdemand responseoptimized schedulingdeep rein-forcement learning
Publication Date：2026-01-30
Online Publishing Date：2026-02-05(First online date of this platform, not the publication date of the document)
Pages：11( 205-215 )