Matching EV Charging Load With Uncertain Wind: A Simulation-Based Policy Improvement Approach

Abstract

This paper studies the electric vehicle (EV) charging scheduling problem to match the stochastic wind power. Besides considering the optimality of the expected charging cost, the proposed model innovatively incorporates the matching degree between wind power and EV charging load into the objective function. Fully taking into account the uncertainty and dynamics in wind energy supply and EV charging demand, this stochastic and multistage matching is formulated as a Markov decision process. In order to enhance the computational efficiency, the effort is made in two aspects. Firstly, the problem size is reduced by aggregating EVs according to their remaining parking time. The charging scheduling is carried out on the level of aggregators and the optimality of the original problem is proved to be preserved. Secondly, the simulation-based policy improvement method is developed to obtain an improved charging policy from the base policy. The validation of the proposed model, scalability, and computational efficiency of the proposed methods are systematically investigated via numerical experiments.

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

“…Moreover, the convergence behavior of iteration (14) for customer 4 is depicted in Fig. 7(b) by regret criterion (15). The result denotes that at the first days the regret level is high, while as the learning capability of the customer increases, the regret level goes down and converges.…”

“…Supporting universal charging protocols: Charging the PEVs with faster rate and lower time by drawing the maximum charging power from the charging pole [12], prolonging the PEV battery's lifetime with constant power feeding [15], and requiring smaller communication overheads to contact (switch off or on) with a small subset of PEVs [16]. Further, with this method the infrastructures only need to work with one charging rate which needs very cheap infrastructure and services.…”

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

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“…Carpinelli et al [169] used exponential weighted method and a compromise programming method for multi-objectives problems including the minimisation of the power losses, the squared voltage deviation along the bus, and the security margin related to the line currents. Huang et al [170] proposed a Markov decision process to model a stochastic problem incorporating the matching degree between wind power and PEV charging load. Bai et al [171] utilised a nonlinear complementarity optimisation model for solving unit commitment in coordination with bidirectional large-scale PEVs.…”

Operational Optimization for Microgrid of Buildings with Distributed Solar Power and Battery

“…Considering the capability of autonomous operation, self-sufficiency (or self-adequacy) has been discussed for the energy management in different kinds of smart microgrids [27][28][29][30][31]. A high self-sufficiency can not only lower the electricity cost but also lower the emission for generating traditional power.…”