Thermostatically Controlled Load Aggregation Method And Its Optimal Control Strategy Considering User Response

With the increasing output of renewable energy and its gradual raising penetration rate,the demand-side management resources have been widely concerned in terms of participating in solving the power fluctuations and uncertainties brought by new energy integration.Among them,air conditioning load,serving as the main representative of the thermostatically controlled load,accounts for a relatively large proportion in summer electricity consumption.Considering its fast response speed,large response capacity and low input cost,air conditioning load is gradually becoming a type of reliable and effective reserve form besides the generation-side.Reasonable allocation and optimal control of the air conditioning load can reduce the load power,achieving the purpose of mitigating the power supply pressure during the peak period and stabilizing the system power shortage caused by the fluctuation of renewable energy output.Based on the above considerations,this thesis,taking the air conditioning load participating in demand response as the background,studies its load aggregation method as well as optimal control effect.The main work of this thesis is as follows:(1)A hierarchical aggregation model of air conditioning load is proposed.This model takes into account the users’ responding willingness as well as the differences in air conditioning equipment parameters,making up for the lack of differences in users’ response willingness in most existing studies.Hierarchical aggregation of small airconditioning clusters formed by adaptive clustering can better grasp the electricity consumption tendency of air-conditioning load.The validity of the adaptive clustering method is verified.Plus,it is proved that the hierarchical aggregation model has the good effect of simulating the electric power of the load group and mastering its change law.(2)The aggregated weighting coefficient queuing(AWCQ)control algorithm is proposed to control the small air-conditioning clusters formed by adaptive clustering.The algorithm gives priority to user’s responding willingness.The average room temperature and the average cumulative controlled times are included in the weighting sequence while screening the target load group in order to better ensure the fairness of controlling different air conditioning groups.It is verified that the aggregated control algorithm can simplify the control logic and shorten the calculation duration,so as to achieve the effect of smoothing power fluctuation and peak clipping.(3)An air conditioning load group multi-objective optimal control algorithm is raised which aims to minimize the cost caused by regulating and controlling load power and maximize the customers’ comfort level,comprehensively considering target power constraints,equipment locking constraints.To some extent,the algorithm avoids the unfavorable economic benefits of the regulated equipment group practices based on weighting sequence in AWCQ algorithm.The simulation case demonstrates and analyzes the optimization effect of the optimal control algorithm.