The increasing penetration of electric vehicles (EVs) gradually evolves the urban transportation network (UTN) and power distribution network (PDN) from being independent to being coupled. To analyze the impact of dri...
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The increasing penetration of electric vehicles (EVs) gradually evolves the urban transportation network (UTN) and power distribution network (PDN) from being independent to being coupled. To analyze the impact of drivers' behavior on the coupled network, and reveal the relationship between the two networks, a coupled traffic-distribution (CTD) model is proposed, in which the total traveling cost of UTN and the power service cost of PDN is minimized. In the CTD model, A stochastic user equilibrium traffic assignment problem (SUE-TAP) with elastic traffic demand and discrete path selection of drivers is formulated to capture the traffic flow distribution comprised of EVs and gasoline vehicles (GVs). An alternative current optimal power flow (ACOPF) model of PDN is utilized to provide the optimal charging price and scheduling plan. Besides, the SUE-TAP with nonlinear functions is difficult to solve, a novel piecewise linear approximation method is presented to deal with the nonlinear items of the SUE-TAP caused by drivers' traveling cost and traveling tendency incorporating perception. Incorporating the behavioral theory, a distributed coordination method derived from optimal condition decomposition is proposed for the coupled traffic-power network equilibrium. The proposed distributed scheme is examined using a coupled network, which consists of a 20-road UTN and a 33-bus PDN. Numerical results demonstrate the effectiveness and practicability of the proposed model and the distributed coordination method.
In this paper, a fully distributedcoordination control based on electricity trading system (ETS) is proposed. It aims at simulating price trading mechanism and the interaction process of the electricity price bargain...
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ISBN:
(纸本)9781538611272
In this paper, a fully distributedcoordination control based on electricity trading system (ETS) is proposed. It aims at simulating price trading mechanism and the interaction process of the electricity price bargain among multiple users in a microgrid. Each user with an intelligent module is considered as an agent and each agent carries out its own price response and decision. The proposed approach adopts the ZigBee technology for communication. Each agent contains a ZigBee module based on the chip CC2530 and embedded C as the development language. The distributedcoordination algorithm is proposed for the agents to obtain the optimal power generation or consumption and maintain the supply demand-balance within a microgrid. In the proposed ETS, only local data and information exchange with other adjacent nodes are required, so the computation and communication burdens are evenly allocated to multiple local agents. Hardware simulation results verify the effectiveness of the proposed distributed control strategy.
Much research has focused on the issue of how to effectively coordinate the charging behaviors of large-scale plug-in electric vehicles (PEVs), like the valley-fill strategy, to minimize their impacts on the power gri...
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Much research has focused on the issue of how to effectively coordinate the charging behaviors of large-scale plug-in electric vehicles (PEVs), like the valley-fill strategy, to minimize their impacts on the power grid. However, high charging rates under the valley-fill strategy may result in a higher battery degradation cost. Consequently, in this brief, we formulate a class of PEV charging coordination problems that deal with the tradeoff between the total generation cost and the accumulated battery degradation cost of PEV populations. Due to the autonomy of individual PEVs and the computational complexity of the system with large-scale PEVs, it is impractical to implement the solution in a centralized way. Alternatively, in this brief, we propose a distributedmethod such that all of the individual PEVs simultaneously update their own best charging behaviors with respect to a common electricity price curve, which is updated as the generation marginal cost with respect to the aggregated charging behaviors of the PEV populations implemented at the last step. The iteration procedure terminates in case the price curve does not update any longer. We show that by applying the proposed distributedmethod and under certain mild conditions, the system can converge to a unique charging strategy, which is nearly socially optimal. Simulation examples are studied to illustrate the results developed in this brief.
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