基于小脑模型神经网络的温控负荷优化调度方法

Optimal Dispatch Method of Thermostatically Controlled Load Based on Cerebellar Model Articulation Controller

温控负荷能够通过需求响应为电网提供频率调节辅助服务。针对传统控制方式调节能力有限和温度变化较大的问题,提出了一种基于小脑模型神经网络的混合控制策略。首先,利用小脑模型神经网络实时优化功率调节量,从而将调节任务分配给不同控制方式的负荷集群;然后,负荷集群完成所分配的调节任务,并根据用户的不同需要分别定义储能指标和不舒适度指标;最后,采用模糊综合评判法对用户满意度进行评估,再将用户满意度与均方根误差相互权衡以建立综合评价指标,并将其反馈回小脑模型神经网络,从而为功率调节量的优化提供依据。仿真结果表明,所提策略不仅可以提高系统的跟踪精度,而且能够改善用户的满意度。

Thermostatically controlled loads can provide frequency regulation auxiliary services for power grids through demand response.Aiming at the problems of limited adjustment ability and large temperature variation on traditional control methods,a hybrid control strategy based on the cerebellar model articulation controller(CMAC)is proposed.First,the CMAC is used to optimize the power regulation quantity in real time,so as to assign the regulation task to the load clusters with different control methods.Then,the load cluster completes the assigned adjustment task and defines the energy storage index and discomfort index according to the different needs of users,respectively.Finally,the fuzzy comprehensive evaluation method is used to evaluate user satisfaction,and then the user satisfaction and root mean square error are weighed to establish a comprehensive evaluation index which is fed back to the CMAC,thereby providing a basis for the optimization of power regulation quantity.Simulation results show that the proposed strategy can not only increase the system tracking accuracy,but also improve the user satisfaction degree.