考虑路网和用户满意度的集群电动汽车主从博弈优化调度策略

Stackelberg game optimization scheduling strategy for aggregated electric vehicles considering customer satisfaction and the road network

传统的电动汽车(electric vehicle,EV)集中优化方法在实际应用中面临调度困难、计算量大、缺乏真实数据支撑等问题,无法准确揭示各主体间的交互行为。为此,提出一种考虑路网和用户满意度的集群EV主从博弈优化调度策略。首先基于真实出行数据和路网数据模拟用户出行行为。其次,负荷聚合商(load aggregator,LA)整合EV负荷资源,对相似出行特性的EV进行聚类。在双层主从博弈模型中,LA作为上层领导者,聚类后的各EV子群作为下层跟随者。考虑EV用户不同消费偏好,通过优化LA定价策略、新能源及储能系统出力计划、EV集群充放电策略实现纳什均衡,达到各主体共赢,并使用改进遗传算法进行求解。最后,利用仿真验证了所提模型可有效提升LA收益及EV用户消费者剩余,增加新能源消纳,并可为不同消费偏好的用户提供差异化服务。

Traditional centralized optimization methods for electric vehicles(EVs)are faced with problems such as scheduling difficulty,a large amount of computation and lack of real data support in practical application,and they cannot accurately reveal the interaction behavior among various entities.Therefore,a Stackelberg game optimization scheduling strategy for aggregated EVs considering user satisfaction and the road network is proposed.First,it simulates user travel behavior based on real travel data and road network data.Second,the load aggregator(LA)integrates EV load resources to cluster EVs with similar travel characteristics.In the two-level Stackelberg game model,the LA is the leader of the upper level,and the clustered EV subgroups are the followers of the lower level.Considering the different consumption preferences of EV users,Nash equilibrium is achieved by optimizing the pricing strategy of the LA,the output plan of new energy and energy storage systems,and the charging and discharging strategies of EV clusters.The solution is achieved by an improved genetic algorithm.Finally,simulation is used to verify that the proposed model can effectively improve revenue of the LA and consumer surplus of EV users,increase consumption of new energy,and provide differentiated services for users with different consumption preferences.