Research On Optimization Management And Control Technology Of Smart Power Consumption

As an important component of construction of smart grid,smart power consumption played the role linking supply side to the demand side in people’s life and made great significance in social development.In this paper,research is mainly about the optimization and control technology of smart power consumption.In this paper,household load model is described,optimization scheduling algorithm is proposed,residential load demand response programs has been designed and concentrated load demand response method is analyzed.The work in this dissertation is summarized as follows:1.In accordance with the existing household load modeling research,an household load modeling method based load characterization is proposed.Smart appliances will load characteristics are summarized as follows: transfer characteristics,time constraints characteristic,interruptible characteristics,mode-switched characteristics,indicator-continuous characteristics.Construction of load models of different electrical devices by combining various characteristics.This model is not only more comprehensive,accurate,and scalability,also able to adapt to the future smart home with appliances of large number and variety2.This paper presents an optimization algorithm based on improved genetic algorithm for household electricity load scheduling.Making an improvement of traditional genetic algorithm on the aspects of optimal individual protection,sub-lethal genes control mechanisms and dynamic mutation rate are added.Multi-scene cases are analyzed under Matlab circumstance.Results show that the proposed algorithm can not only well suited to the electricity consumption optimization problem,but also operates in good performance.3.Designing a three-tier residential load demand response control program.The basic idea of the program is combining advanced forecasting and real-time control.Entire demand response program is divided into three levels,which are namely forecasting layer,optimal scheduling layer and real-time control layer.Distributed generation output forecasting and user’s home base-load forecasting are two main functions of prediction layer.Optimal Scheduling layer provides electricity consumption plan the next day with intelligent decision algorithm given.Real-time control layer is aimed to deal with users’ unexpected electricity consumption and emergency demand response events.An emergency demand response control strategy based on dynamic priority is described and proved working well.4.A user demand response analysis method based on the demand elasticity is proposed.The users’ load demand response matrix can be got by analyzing self-elasticity and cross-elasticity in different time series,and the users’ demand response behavior is analyzed under TOU electric price.The proposed method is proved effective in case study.