Study Of Automated Demand Response Technology On The Consumer Side

Demand response(DR)is one of the most important technical means in demand side management.Through guiding users to respond the price or incentive signal,DR improves the efficiency and the reliability of the power grid by load shaping and optimization of energy utilization.Automated demand response(Auto-DR)is the development trend of demand side management.It is urgent to achieve automatic demand response in the user-side with the development of smart grid.This dissertation studies the user-side automatic demand response technology.The main contributions are as follows.1.The Index system of load response capability for Auto-DR is built,and assessment method based on load decomposition is proposed.First,load response models for power users,including thermostatically uncontrolled loads and thermostatically controlled loads,are built as the theoretical support for Auto-DR methods.Second,the Index system of load Auto-DR capability is built.A triple credibility matching method for the online load decomposition is presented and the DR loads are decomposed from the whole loads to be used in DR.Based on the algorithm,the online assessment method is proposed for calculating the load DR capability of users.2.A thermostatically controlled loads shedding method based on emergency DR is proposed in the implementation of incentive-based DR.First,the control method for thermostatically controlled loads in emergency DR is determined by simulation of load aggregate response characteristic.Second,the temperature-controlled loads can participate in load shedding,which reduces the amount of loads being cut off with traditional methods and improves the economy and the stability of power systems.3.A scheduling method of home energy management under TOU price is proposed in the implementation of price-based DR.The basic strategies of automated demand response are discussed.Then,the scheduling model and method of home energy management are presented to improving local consumption of DGs.First,the optimization model of the co-schedule of PVs,energy storage systems,active loads is built.The model aims at the maximization of home energy profit with the constraints of user comfort and system operation condition.Then,an improved binary particle swarm algorithm is proposed to get the optimal daily household consumption plan,which improves the user energy efficiency and accommodation of DGs on the user side.4.A user energy management system for automated demand response is designed and implemented.This system is aimed at residential or commercial customers and both of the price based and incentive based automated demand response can be applied.Firstly,the framework and functions of the system are presented,and the database requirement for this system is determined.Then,an embedded integrated development environment is built and the system management progress and the GUI progress are developed.Finally,the system is deployed in smart power utilization laboratory.The result shows that the developed system can be used effectively in the implementation of automated demand response program and shows its practical values.