PROJECT TITLE :

An Electric Vehicle Load Management Application of the Mixed Strategist Dynamics and the Maximum Entropy Principle

ABSTRACT:

An application of an evolutionary game dynamics called mixed strategist dynamics (MSD), for the decentralized load scheduling of plug-in electrical vehicles (PEVs), is proposed during this paper. Following an analogy with the most entropy principle (MEP) for tuning parameters of discrete probability distributions, entropy of the entire load distribution and also the native load distributions are thought-about as objectives of the scheduling approach, and a tradeoff among them is defined by the electrical vehicle homeowners' convenience. Whereas entropy maximization for the local load distributions contributes to preserve the batteries' states of health, entropy maximization for the overall load distribution reduces the undesirable peak effects over the transformer loading. The matter is formulated such that final states of charge are assured relying on time constraints outlined by the house owners. Furthermore, mixed strategies within the MSD are defined such that they represent the vertices of the convex set of feasible load profiles which results from the constraints imposed by homeowners and chargers. The synergy of many PEVs is modeled as an application of the MSD in a very multipopulation scenario, where the interaction among populations follows another evolutionary game dynamics known as best reply (BR) dynamics. The performance of the proposed approach is tested on real information measured on a distribution transformer from the SOREA utility grid company in the region of Savoie, France.


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