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首页> 外文期刊>Transportation Electrification, IEEE Transactions on >Expansion Planning of Urban Electrified Transportation Networks: A Mixed-Integer Convex Programming Approach
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Expansion Planning of Urban Electrified Transportation Networks: A Mixed-Integer Convex Programming Approach

机译：城市电气化交通网络扩展规划：混合整数凸规划方法

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摘要

Electric vehicles (EVs) have been widely acknowledged as one effective solution to alleviate the fossil fuel shortage and environmental pressure in modern metropolises. To foster the large-scale integration of EVs, transportation electrification is becoming an emerging trend. This paper proposes a comprehensive model for the expansion planning of urban electrified transportation networks (ETNs), which determines the best investment strategies for the TN and the power distribution network (PDN) simultaneously, including the sites and sizes of new lanes, charging facilities, distribution lines, and local generators. The steady-state distribution of traffic flow in the TN is characterized by the Nesterov user equilibrium (NUE). The operating condition of the PDN is described by linearized branch power flow equations. To consider the interdependency between the TN and PDN created by the charging behavior of EVs, the power demand of on-road charging facility is assumed to be proportional to the traffic flow it carries. The expansion planning model is formulated as a mixed-integer nonlinear program with NUE constraints. In order to retrieve a global optimal solution, it is further transformed into an equivalent mixed-integer convex program through duality theory and techniques of integer algebra; no approximation error is involved. Case studies on a test ETN corroborate the proposed model and method.
机译：电动汽车（EV）已被广泛认为是缓解现代大都市化石燃料短缺和环境压力的一种有效解决方案。为了促进电动汽车的大规模整合，交通运输电气化正在成为一种新兴趋势。本文为城市电气化交通网络（ETN）的扩展规划提出了一个综合模型，该模型同时确定了TN和配电网（PDN）的最佳投资策略，包括新车道的位置和大小，充电设施，配电线路和本地发电机。 TN中流量的稳态分布以Nesterov用户平衡（NUE）为特征。 PDN的工作条件由线性化分支潮流方程描述。为了考虑由电动汽车的充电行为所产生的TN和PDN之间的相互依赖性，假定道路充电设施的电力需求与其所承载的交通流量成正比。扩展计划模型被公式化为具有NUE约束的混合整数非线性程序。为了获取全局最优解，通过对偶理论和整数代数技术将其进一步转化为等效的混合整数凸程序。不涉及近似误差。关于测试ETN的案例研究证实了所提出的模型和方法。

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