电动汽车充电调度策略

ELECTRICAL VEHICLE CHARGING SCHEDULING STRATEGY

凡红林

11749159

硕士

机械工程

杨再跃

2019-05-30

2019-06-28

哈尔滨工业大学

深圳

作为新型交通工具，电动汽车相比燃油汽车不仅能够减少石油能源消耗，还能实现零污染排放。在环境保护与石油资源日益受到重视的今天，各国相继出台了促进电动汽车发展的政策，推动电动汽车的发展，使得电动汽车替代燃油汽车逐渐成为一种趋势。但随着电动汽车的发展，其充电需求也日益增大。充电站作为电动汽车充电的主要场所，不仅用电负荷高且波动大，如果没有合理的调度策略不仅会影响电网运行的稳定性，还会增加充电站的运营成本、降低运营效率，甚至影响充电站的安全性。电动汽车的无序充电行为，在影响电网和充电站安全与效率的同时，还会将充电成本转移到电动汽车车主，进而阻碍电动汽车的发展。因此，为充电站设计一种合理有效的调度策略具有重要意义。本文首先介绍如何运用 Flask 框架搭建数据接收服务器，从华南充电科技有限公司获取实时的充电数据。根据获取到的充电数据，分析了目前电动汽车车主的用电习惯、可调度空间及目前充电模式存在的问题，为后续算法设计及仿真奠定基础。然后，以最小化充电站的购电成本为目标，在保证电动汽车车主的充电需求能够被满足的情况下，提出了离线的充电调度算法。由于该算法需要全局的电动汽车充电信息，难以应用于实际，因此进一步提出了简单但有效且易于实施的在线调度策略，并通过 Matlab 仿真验证了在线调度策略与离线调度算法具有相似的优化性能。最后，为了进一步提升在线调度策略的性能，进一步提出了差异化充电定价策略与二阶段调度策略。其中差异化充电定价策略有以下三方面的好处：给予充电灵活性高的用户一定的折扣；保证定价对电动汽车车主的公平性；所有电动汽车车主的最优充电选择也能够使得充电站达到最优购电成本。仿真验证表明定价策略能够根据充电站的运营情况引导电动汽车选择合理的充电需求。同时，仿真结果还表明二阶段调度能够减少电动汽车总的充电时间，提升充电桩的利用率与用户满意度。

As a new-type of transportation, electric vehicles (EVs) have enormous advantages over fuel vehicles in emission reduction and energy saving. Therefore, as environmental protection and oil resources are increasingly valued, many countries have introduced various policies to promote the development of EVs, which makes it a trend for EVs to replace fuel vehicles gradually. As the number of EVs is surging, their charging demands are also growing enormously. Charging stations (CSs) are the most common places to charge EVs. However, without proper management, the heavy charging load not only poses a serious threat to power grids due to sudden and uncertain spikes, but also causes tremendous charging cost and unsafety to CSs, which significantly degrades the efficiency of a CS. The disorderly charging behavior of EVs will not only affect the stability and efficiency of the power grid and CSs, but also transfer the charging cost to the owners of EVs, thus affecting the development of EVs. Therefore, it is imperative to implement effective coordinated scheduling strategies in CSs, considering that the charging demands of EVs are deferable, mobile and random. Firstly, the Flask framework is used to build a data acquisition server to obtain real-time charging data from South China charging technology co., ltd. According to the obtained charging data, the current charging habits of electric vehicle owners, the potential flexibility for charging scheduling and existing problems of the current charging mode are analyzed. This becomes a foundation for subsequent algorithm designs and simulations. Then, in order to minimize the purchase cost of a CS, an offline charging scheduling algorithm is proposed, such that energy demands of all EVs are fulfilled before departure. However, as the offline algorithm requires global EV charging information, it is difficult to be applied in practice.Therefore, we proceed to design a simple, yet effective and implementable online algorithm that requires zero future information. Matlab simulations verify that the performance of the online scheduling algorithm optimization is similar to that of the offline scheduling algorithm. Finally, in order to further improve the performance of the online algorithm, a pricing mechanism and a second-stage scheduling strategy are proposed based on the online algorithm. There are three-fold benefits of the proposed pricing mechanism: providing discounts to users with high scheduling flexibility, ensuring fairness among EV users and the individually optimal strategies of EV users jointly minimize the cost of the CS. Numerical results based on real data further verify the effectiveness of the proposed online algorithm and the pricing mechanism. At the same time, the simulation results also show that the second-stage scheduling strategy can reduce the total charging time of EVs and improve the utilization rate of charging piles and user satisfaction.

电动汽车 充电站 调度策略 定价策略

electric vehicle charging station scheduling scheme pricing mechanism

中文

联合培养

南方科技大学

凡红林. 电动汽车充电调度策略[D]. 深圳. 哈尔滨工业大学,2019.