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Optimal operation for coupled transportation and power networks considering information propagation
Electric Power Systems Research ( IF 3.3 ) Pub Date : 2021-02-10 , DOI: 10.1016/j.epsr.2021.107082
Hanning Mi , Sijie Chen , Jinhui Zhang

The growing number of electric vehicles (EVs) and the rapid development of wireless charging technology couple the transportation network (TN) and power distribution network (PDN). When local traffic events happen, transportation parameters are changed and drivers will change their driving behaviors accordingly. However, information spread by vehicle-to-vehicle communication takes time. Information asymmetry among EVs will break the original traffic equilibrium. Therefore, a transient state is caused between two steady states in TN. Meanwhile, the variation of traffic flow caused by information propagation will also influence the power flow in PDN. This paper investigates the interactive mechanism of coupled transportation network and power network during the transient and steady state. Considering road parameters, electricity price and information flow among vehicles, a model that combines traffic assignment model and clustered epidemiological differential equations is proposed to formulate spatio-temporal distribution of EVs. Distflow equations are employed and relaxed to a second-order cone problem. Then the locational marginal price (LMP) is formulated. To avoid massive parameters transfer between two networks, we use a best-response decomposition algorithm to iteratively calculate the electricity price and real-time traffic flow. The results of two coupled networks justify the necessity to take information propagation among vehicles into account to coordinate the operation of coupled TN and PDN.



中文翻译:

考虑信息传播的交通运输和电力网络优化运行

电动汽车(EV)的数量不断增长,无线充电技术的迅猛发展将交通网络(TN)和配电网络(PDN)结合在一起。当发生本地交通事件时,交通参数将发生变化,驾驶员将相应地改变其驾驶行为。但是,通过车辆间通信传播的信息需要时间。电动汽车之间的信息不对称将打破原来的交通平衡。因此,在TN中的两个稳态之间会引起一个过渡状态。同时,由信息传播引起的业务流的变化也将影响PDN中的功率流。本文研究了交通运输网络与电力网络在瞬态和稳态下的相互作用机理。考虑到道路参数 提出了一种基于电价和车辆间信息流的模型,该模型结合了交通分配模型和聚类的流行病学微分方程,以建立电动汽车的时空分布。使用Distflow方程并将其放宽到二阶锥问题。然后制定区域边际价格(LMP)。为了避免在两个网络之间传递大量参数,我们使用最佳响应分解算法来迭代计算电价和实时流量。两个耦合网络的结果证明有必要考虑车辆之间的信息传播以协调耦合TN和PDN的操作。提出了一种将交通分配模型和流行病学微分方程相结合的模型,以建立电动汽车的时空分布。使用Distflow方程并将其放宽到二阶锥问题。然后制定区域边际价格(LMP)。为了避免在两个网络之间传递大量参数,我们使用最佳响应分解算法来迭代计算电价和实时流量。两个耦合网络的结果证明有必要考虑车辆之间的信息传播以协调耦合TN和PDN的操作。提出了一种将交通分配模型和流行病学微分方程相结合的模型,以建立电动汽车的时空分布。使用Distflow方程并将其放宽到二阶锥问题。然后制定区域边际价格(LMP)。为了避免在两个网络之间传递大量参数,我们使用最佳响应分解算法来迭代计算电价和实时流量。两个耦合网络的结果证明有必要考虑车辆之间的信息传播以协调耦合TN和PDN的操作。我们使用最佳响应分解算法来迭代计算电价和实时交通流量。两个耦合网络的结果证明有必要考虑车辆之间的信息传播以协调耦合TN和PDN的操作。我们使用最佳响应分解算法来迭代计算电价和实时交通流量。两个耦合网络的结果证明有必要考虑车辆之间的信息传播以协调耦合TN和PDN的操作。

更新日期:2021-02-10
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