电动汽车充/换点设施布局及政策驱动下的规模扩张优化研究

Due to the energy crisis and serious environmental pollution problems, the electric vehicle project has received wide attention from the governments and enterprises. However, backward infrastructure in our country has become the bottleneck for the development of electric vehicles. This project studies the charging or battery swapping facilities optimization. According to the reality, analyze on the key factors of facility location design, establish mathematical model of facilities optimization from multi-level, multi-angle and multi-subject, with decisions of facility location, charging infrastructure capacity, battery backup inventory, demand allocation, routing and so on. Mainly researches are as following: demand behavior analysis and the influence of facility design, facilities robust optimization with uncertain demand, and facilities scale expansion optimization according to development planning and policy for the future electric vehicles. On one hand, these theoretical results will extend the research field of facility location theory; on the other hand, they will provide theoretical principles for charging or battery swapping facility construction in reality.

由于能源危机和严重的环境污染问题，电动汽车项目得到了政府、企业的广泛关注。然而，我国落后的基础设施成为电动汽车发展的瓶颈。本项目以电动汽车充/换电设施为研究对象，根据实际情况，分析设施布局的关键影响因素，多层次多角度多主体建立设施优化模型，决策变量包括充换电设施及配送站选址、充电设施容量、备用电池库存、服务范围划分、路径等，并根据模型提出有效算法。拟研究内容包括：需求行为分析及对于设施设计影响，需求不确定下的设施鲁棒优化设计问题，以及根据电动汽车发展规划及政策要求对未来设施规模扩张优化进行研究。这些研究一方面丰富了选址优化的理论体系；另一方面，相应的结果将为充/换点设施的设计与规划提供有力的理论指导。

本项目从电动汽车充电需求出发，研究了充电站的布局与容量优化。根据电动汽车行驶里程较短、充电时间较长等特点，结合排队理论、随机理论和选址理论，提出了相应的非线性混合整数优化模型，研究了针对路途中的快充设备选址问题，综合性充电站选址问题，考虑服务均衡的充电站布局和容量设置，以及在国家政策推动下的多阶段充电站扩张优化问题。由于电动汽车数目快速增长，以现有电动汽车数目作为模型需求参数显然缺乏合理性，提出了电动汽车数目预测框架，采用神经网络方法，以未来预测电动汽车数目为模型输入参数。由于模型复杂性且为NP困难问题，提出了相应的有效的启发式算法，包括粒子群算法、遗传算法、贪婪算法等，并通过大规模算例验证算法的有效性。将模型和算法应用于实际案例中，为实际的充电站布局提供建议。这些研究一方面拓展了随机情形下的选址理论；另一方面，相应结果为充电站布局和发展提供有力的理论指导。本项目标注发表论文8篇，其中sci检索期刊论文4篇。