Research On Residential Demand Response And Strategy Of Load Optimization Under Random Interaction

Under the background of carbon peaking and carbon neutrality goals,with the improvement of living standards and the popularization of intelligent electric appliance,the electricity load of residential households presents a flexible and increasingly complex development trend.with a large difference between peak and valley loads.At the same time,the high proportion of renewable energy access exacerbates the power output fluctuations at the source end,and the high randomness on both sides of the source and load highlights the insufficient flexibility of the traditional power system operation mode.Demand response can fully tap the scheduling potential of demand-side resources,guide users to reduce peak and fill valleys through electricity prices or incentive measures,improve the flexibility and stability of power system operation and the utilization rate of power supply equipment.and effectively enhance safety and economic benefits.In this paper,the key supporting issues for the implementation of demand-side response are taken as the research object to carry out modeling research,in order to provide targeted analysis theory and optimization support for the accurate implementation of demand response.Firstly.considering the randomness and volatility of residential electricity load,this paper deeply analyzes the influence mechanism of residential electricity consumption behavior from the perspective of residential travel chain.The differences in electricity consumption behavior characteristics and load levels between households are comprehensively considered,and a simulation model of the distribution of summer residential household electricity load in Beijing is constructed.The Monte Carlo simulation method is introduced to explore the impact of resident travel chain,temperature,and household electricity consumption level differences on household electricity load.The research results show that the resident travel chain,temperature,and differences in household electricity consumption level have significant effects on the total and distribution of electricity load.while the differences in transportation mode have no significant effect on peak load.Secondly,considering the accumulation of data related to the implementation of resident demand response is poor,a demand response control potential evaluation model is constructed.consisting of a household classification module,a similar day classification module,and a response potential evaluation module.The household classification module and similar day classification module use electricity load characteristic indicators and weather data as inputs to realize the classification of residential households and similar days.The response potential evaluation module proposes a evaluation method for demand response potential based on Gaussian mixture model,which quantifies the hourly potential through historical electricity consumption data and explores the distribution characteristics of electricity load and demand response potential.The empirical analysis shows that the proposed method is feasible and effective.Thirdly,price-type and incentive-type demand response scenarios are set up,the residential electricity load is classified and modeled,and the improved particle swarm optimization algorithm is introduced to solve the problem.The quantitative analysis and comparison of the optimization effect under multiple scenarios are conducted from the aspects of total electricity cost,comfort level,and peak-load shifting effect.The research results show that total electricity cost is significantly reduced in both demand response scenarios while losing little comfort level,and the load characteristics of households is effectively improved.Under the high randomness on both sides of the source and load,the necessity and urgency of the implementation of residential demand response is important.The paper carries out modeling research on three key support issues for residential demand response implementation on the residential side,namely,the analysis of residential electricity consumption behavior,the potential evaluation of demand response,and the optimization strategy of load regulation,which can play a reference and promotion role for the theoretical development and implementation of demand response in our country.