Research On The Control Strategy Of Home Energy Management System Based On Intelligent Power Utilization

Intelligent power utilization is a bridge connection between the power supply department and the users. It is one of the important components of the Smart grid, and the flexible interaction of intelligent power utilization has a huge role in solving the problems of energy saving and emission reduction, environmental pollution and so on. The home energy management system(HEMS) is an extension of intelligent power utilization in residential electricity side, with the support of the Advanced Metering Infrastructure system and demand response project, we study the home energy management system, through optimal control strategy we adjust household electricity equipment and storage facilities accommodate the charge and discharge electricity grid load changes and automation systems to achieve energy savings, reduce electricity costs for users, cuts and load transfer, improve grid stability and security purposes at the same time respond to the needs of users to participate in the project, you can also direct users to develop good habits of using electricity.This paper first analyzes the domestic and fore ign research status of intelligent power utilization, then introduce the Advanced Metering Infrastructure system, focuses on the role of smart meters in HEMS, also introduce the Demand Response in domestic and implementation of environmental policies and programs, after that put forward a overall design of home energy management system framework, and introduce some of its detailed composition. HEMS have basic functions for information collection, electrical equipment control switch, control strategy optimization, in addition should also have data analysis, forecasting, real-time price forecast and other advanced features, so this paper based on the study of HEMS, proposed a real-time electricity price forecasting model based on dragonfly algorithm optimized SVM. The input feature will affect the accuracy of the model, so we first analysis the original data, then use the Elastic Net algorithm for feature dimension reduction, both to ensure the accuracy of the prediction model, and improve processing speed model, at last by comparing the real case simulation to validate the prediction model. This paper proposes an optimal control strategy for home appliance based on Critical Peak Pricing(CPP) and Real Time Pricing(RTP), this method using the analytic hierarchy process(AHP) to evaluate the comfort level of the adjustable home appliance. Establishing mathematical model with highest comfort level and both highest comfort level with minimum cost, at the same time consider the influence of family distributed power and the energy storage equipment, make the household getting the satisfied and comfortable control strategy. Finally an example proved the practicability and effectiveness of the proposed control strategy.