基于多代理系统的电动汽车协调充电策略

Multi-Agent System Based Coordinated Charging Strategy for Electric Vehicles

为最大化电力公司利益,设计了一种用于协调电动汽车充电的多代理系统,并在满足电动汽车车主充电需求及变压器容量限制的前提下,提出一种以负荷峰谷差最小为目标的分布式优化算法。利用分时电价算法初步优化后得到理想的充电时间区间,在充电区间内应用优化算法避免新的负荷尖峰,引入训练学习机制以使负荷曲线达到削峰填谷的效果。根据用户的驾驶习惯,采用蒙特卡洛方法模拟用户的充电需求,对电动汽车在无序充电、单次优化充电以及引入训练学习机制充电3种情况下的电网负荷进行了仿真分析。研究结果表明:单次优化可以避免负荷尖峰,但不能优化峰谷差;而引入训练学习机制后在减小峰谷差方面有显著作用,而且该分布式优化有更高的计算效率,适于实际应用。

To maximize the benefit of power company, a multi-agent system （MAS） to coordinate the charging of electric vehicles （EV） is designed. Under the premise of meeting the charging demand of EV owners and according to the restriction of transformer capacity a distributed optimization control algorithm, in which the minimized peak-valley load difference is taken as objective, is proposed. Firstly, the preliminarily optimized time-of-use （TOU） price algorithm is used to obtain ideal charging time interval; secondly, in the obtained time interval the optimization algorithm is utilized to avoid the occurrence of new peak load; finally, the training-learning mechanism is led into to achieve the effect of peak load shifting. According to the driving habits of EV owners the Monte Carlo method is used to simulate the charging demand of EV owners, and the power grid load conditions under three charging situations, namely the out-of-order charging, single-time optimization charging and the charging with leading in training-learning mechanism, are simulated. Results of this research show that adopting single-time optimization charging the new peak load can be avoided, however the peak-valley difference cannot be optimized; leading in training-learning mechanism plays obvious role in decreasing peak-valley difference and the distributed optimization control algorithm possesses higher computing efficiency, so it is suitable for actual application.