Research On DSM Time-of-use Power Price Based On PSO Algorithm

Intelligent power is an important part of the smart grid.It can greatly improve the utilization of electricity.At present,the most commonly used effective means is demand-side management,which guides users to rationally adjust and improve the structure of electricity consumption and electricity consumption by using price signals to form a smoother power load running state.Demand response is one of the most important ways to realize demand side management.Demand response refers to the electricity users because of price or other incentives to market information and take the initiative to change the daily habits of electricity in order to achieve the effect of energy saving.The most important way for the realization is time-sharing price,by changing the price of peak and valley period of the way to guide users to change the electricity load,optimizing power consumption.Then for the power supply companies and electricity users,the implementation of time-sharing price of the process should be how to set a better price signal to control the purpose of the user electricity load,in order to achieve load peak load shifting,energy saving effect,Which is one of the key problems that the future electricity market can use smart electricity successfully.First of all,the thesis based on the background of the development of intelligent power,the development and implementation of time-of-use pricing policy and the behavior of residential electricity users are studied and analyzed in this thesis.The thesis introduces the concept of demand response,summed up at home and abroad to carry out time-of-use pricing research and practice.In addition,the model of time-of-use pricing model is established for the response behavior of Chinese consumers.According to the actual needs of power companies and residents design optimization strategies and objective function.Based on the objective function,an optimization algorithm is proposed,and thegenetic algorithm and particle swarm optimization algorithm are selected to analyze the feasibility of this control strategy.Finally,under the MATLAB platform,when the user comfort is not considered,the algorithm is used to simulate the model with single target,and the two algorithms can achieve the goal of peak load shifting,and the optimization effect of particle swarm algorithm is better in this model.When multi-objective particle swarm optimization is applied to multi-objective optimization of the model,the optimization results are obtained and the goal of improving user comfort and peak load shifting is achieved.