Research On Decision Model And Control Strategy Of Load Aggregator For Demand Response Resource

With the problem of energy shortage and environmental pollution becoming more and more serious,the installed capacity of traditional generating sets will be less and less,and the installed capacity of new energy will be more and more.Faced with the reduction of adjustable capacity of traditional generator sets and the uncertainty of new energy output,the development and utilization of small and medium-sized demand response resources become an important approach to solve the problem of active power balance in power system.The demand response behavior of small and medium-sized users is very decentralized and uncertain,as a new transaction subject,load aggregators can aggregate and schedule the demand response resources in the controlled area,which can not only promote the active power balance of the power system,but also improve their own interests and reduce the electricity cost of users.Therefore,this thesis is of great significance to research on the decision-making model and regulation strategy of load aggregator.The main research contents and achievements of this thesis are as follows:First,demand response resources are identified and classified.A load identification method based on genetic optimization algorithm is proposed to realize the identification and monitoring of user load,which is conducive to better control of controllable load at the peak of load.On the basis of load identification,the demand response resources are divided into three categories:shiftable load,curtailable load and transferable load according to the user’s power consumption characteristics and habits,and the curves before and after their participation in dispatching are analyzed.By economizing the comfort of user participation in scheduling sacrifice,mathematical modeling is made for the comfort cost of three types of loads respectively,so as to realize the schedulable potential analysis of demand response resources.Then,facing the diversity of demand response resources,a two-layer optimization model of day-ahead real-time electricity price strategy is established.The upper layer aims at maximizing the net income of load aggregator,while the lower layer aims at minimizing the electricity cost and comfort cost of users.The artificial bee colony algorithm and Cplex solver were used to solve the two-layer optimization model.The effect of pricing strategy on system load curve,user cost and revenue of load aggregator is analyzed through simulation.Finally,a demand response resource aggregation scheduling system based on cloud-edge-side collaboration technology is designed,and the data interaction process of cloud-edge-side collaboration and the aggregation scheduling process of cloud-edge-side collaboration are analyzed to realize the hierarchical aggregation scheduling of user-side demand response resources.The regulation strategy of demand response resource aggregation is studied.Load aggregators develop the regulation strategy of demand response resource aggregation in order to maximize their own economy on the basis of completing the target curve of power grid regulation,and make the regulation target curve of various users through simulation verification combined with examples.The research of this thesis is of great significance to realize the revenue optimization of load aggregator,reduce the peak-valley difference of power system load curve,and promote the safe and stable operation of power grid.