Demand Response Evaluation And Coordinated Optimization Of Thermostatically Controlled Loads

Thermostatically controlled loads(TCLs)have been considered as one of the most important demand side resources in many studies to assist in balancing the power supply and demand for the future smart grid.However,due to the uncertainty and diversity of TCLs,it is still a considerable challenge to utilize these great potential resources in a reliable and efficient way.This paper focuses on the modelling,demand side response potential evaluation,user behavior simulation,parameter estimation and coordinated management of TCLs.The main content is as follows.(1)Based on the electric-thermal parameter model of TCL,the operating mechanism of TCL in demand response is analyzed.According to the demand response(DR)performance evaluation index,the influence of different external environment on DR capability of TCL is analyzed.(2)A quantitative DR capacity evaluation method is proposed for large-scale aggregated heterogeneous TCLs without sufficient measurement data.Firstly,an individual TCL model on account of consumer behaviors is developed to characterize the impact of fluctuated electricity prices and different thermal comfort requirements.Secondly,a novel optimization model of heterogeneous TCLs,wh ich can guarantee consumer satisfaction,is proposed to provide operating reserve for power systems.Thirdly,the probability density estimation(PDE)method is developed to evaluate the DR capacity provided by large-scale heterogeneous TCLs with insufficient data.(3)A distributed demand side management(DSM)is proposed for power consumption scheduling of heterogeneous TCLs based on the game theory.Firstly,a novel TCL agent model and its simplified solving method are proposed considering self-constraints as well as others'decisions,in which each individual user can make the best schedule of power consumption to save energy cost in a quick response.Then,the game-theoretic DSM framework of TCL aggregation is developed based on the asynchronous decision making process,enabling participating TCLs to achieve optimal power allocation in a distributed algorithm.Finally,considering the uncertainty of renewable energies,the DSM is further developed to suppress renewable generation fluctuations and provide fast power balancing for system.