Research on Optimization of Demand Response Characteristics Based on MCMC Sampling and Considering User Production Characteristics

Mobilizing users to participate in Demand Side Response (DSR) is an inevitable requirement for the construction of the Energy Internet. The user's demand response characteristics shape the user's ability to reduce the total amount of electricity bills and control the risk of electricity bill fluctuations in an environment of uncertain electricity price fluctuations. Different users have different risk preferences, and because different users have different production characteristics, the difficulty of adjusting their electricity consumption behaviors in each time period is also different. Based on the aforementioned two reasons, users need personalized demand response characteristics to maximize their own utility. Based on the modeling of user response behavior and the simulation of electricity price risk environment based on MCMC sampling method, this paper designs a method that can optimize the demand response characteristics of different users according to their risk preferences and production characteristics with the help of Genetic Algorithm.