Biblio
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Decentralized Coordination for Large-scale Plug-in Electric Vehicles in Smart Grid: An Efficient Real-time Price Approach. IEEE 54th Annual Conference on Decision and Control (CDC). :5877-5882.
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2015. It has been a hot research topic to research the incorporation of large-scale PEVs into smart grid, such as the valley-fill strategy. However high charging rates under the valley-fill behavior may result in high battery degradation cost. Consequently in this paper, we novelly setup a framework to study a class of charging coordination problems which deals with the tradeoff between total generation cost and the accumulated battery degradation costs for all PEVs during a multi-time interval. Due to the autonomy of individual PEVs and the computational complexity for the system with large-scale PEV populations, it is impractical to implement the solution in a centralized way. Alternatively we propose a novel decentralized method such that each individual submits a charging profile, with respect to a given fixed price curve, which minimizes its own cost dealing with the tradeoff between the electricity cost and battery degradation cost over the charging interval; the price curve is updated based upon the aggregated PEV charging profiles. We show that, following the proposed decentralized price update procedure, the system converges to the unique efficient (in the sense of social optimality) solution under certain mild conditions.
Efficient decentralized coordination of large-scale plug-in electric vehicle charging. Automatica. 69:35-47.
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2016. Minimizing the grid impacts of large-scale plug-in electric vehicle (PEV) charging tends to be associated with coordination strategies that seek to fill the overnight valley in electricity demand. However such strategies can result in high charging power, raising the possibility of local overloads within the distribution grid and of accelerated battery degradation. The paper establishes a framework for PEV charging coordination that facilitates the tradeoff between total generation cost and the local costs associated with overloading and battery degradation. A decentralized approach to solving the resulting large-scale optimization problem involves each PEV minimizing their charging cost with respect to a forecast price profile while taking into account local grid and battery effects. The charging strategies proposed by participating PEVs are used to update the price profile which is subsequently rebroadcast to the PEVs. The process then repeats. It is shown that under mild conditions this iterative process converges to the unique, efficient (socially optimal) coordination strategy.