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۱A binary particle swarm optimization based (BPSO–based) scheduling method for plug–in electric vehicles (PEVs) using a fuzzificated multi–objective function
نویسنده(ها): ، ،
اطلاعات انتشار: کنفرانس بین المللی پژوهش در مهندسی، علوم و تکنولوژی، سال
تعداد صفحات: ۱۳
Nowadays, with the widespread adoption of plug–in electric vehicles (PEVs), an efficient coordination among load generation, transmission, distribution, and customers is expected. This paper presents a novel approach to coordinate the PEVs’ scheduling plan to reduce the total amount of active power losses in addition to improve the voltage profile. To close a gap between reality and simulation, the traveling time of the PEV is considered throughout the problem modeling. This work has two innovative characteristics. One is that the proposed scheme considers the prominent factor of the PEV called state of charge (SOC) as a constraint of PEV. Another is to achieve the best result in the scheduling of PEVs, a binary particle swarm optimization is presented. Further, a fuzzificated multi–objective function is introduced to mitigate the problem of weighted multi–objective function. In this way, each part of a multi–objective function is fuzzificated to prevent scaling problem and to have the same range to be combined into a single objective function. To obtain the fuzzificated objective function, in this paper a payoff table method is employed. In the payoff table method, each objective function is optimized individually and the maximum and minimum value of that part are calculated and used throughout the fuzzification approach<\div>
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