Multi-energy Complementary Microgrid Energy Management Optimization Considering Demand Response

As a new type of energy network that contains and connects various energy sources,smart microgrid can play a series role between the user side and distributed energy by using networked supply and intelligent management technology,and can be used between the user side and distributed energy.Play a series role.In the multi-energy complementary family microgrid group,the intermittent shortcomings of distributed power output can be compensated by reasonable regulation,so as to ensure the quality and reliability of power supply and use,and reduce investment and operating costs.This paper proposes a multi-energy complementary smart microgrid group control optimization method based on deep reinforcement learning,establishes a microgrid system model based on demand response,and constructs system optimization goals and constraints,and uses reinforcement learning algorithms to control and optimize the microgrid group.The specific research work is as follows:(1)Construct the structural framework of the energy management system in the household microgrid,establish the equipment models of the photovoltaic power generation system,wind power generation system,and energy storage system in the household microgrid;classify them through the analysis of user load characteristics,and establish user controllability Load equipment model.(2)Propose an optimal control strategy for the microgrid home energy management system based on demand response strategy.In the upper-level optimization strategy,the user’s home-type load is the research object,and the microgrid user load curve is adjusted under the premise of ensuring user satisfaction;the lower-level optimization strategy is based on the upper-level optimization results,and the demand response strategy is established The comprehensive operating cost objective function of the family microgrid is solved by reinforcement learning algorithm to determine the optimal operation mode of distributed energy,energy storage system and distribution network.Finally,the effectiveness and practicability of the proposed control strategy are verified by a comparative analysis of the economics of the household microgrid in the context of whether to participate in the demand response.(3)In order to further improve the user-side economic benefits of the home microgrid,taking into account the mobile energy storage characteristics of electric vehicles,an optimized control strategy for the home energy management system in the multi-energy complementary smart microgrid group is proposed.The Monte Carlo algorithm is used to analyze the travel mode of electric vehicles,and an optimal control model for each unit under the smart microgrid group is established.Use reinforcement learning algorithms to optimize the microgrid group,rationally regulate the coordinated output of multiple energy sources,adjust the load status,analyze the impact of different electric vehicles participating in demand response ratios on charging/discharging loads and charging costs,and compare the economics of microgrids in different scenarios Benefits and environmental benefits.Based on the Matlab platform,a microgrid group model is built,and simulation experiments are performed to verify the effectiveness of the proposed method.