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Day-ahead resource scheduling including demand response for electric vehicles

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Abstract

Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

demand side management distributed power generation electric vehicles energy management systems integer programming nonlinear programming particle swarm optimisation scheduling smart power grids Engineering Profession Power Energy and Industry Applications 33-bus distribution network computational intelligence techniques day-ahead energy resource scheduling day-ahead energy resources management demand response deterministic techniques distributed generation Distributed power generation distributed resources DR programs Educational institutions Electric vehicles Energy resources EV management approaches full AC power flow calculation Load management massive gridable vehicle mixed integer nonlinear programming modified particle swarm optimization network constraints smart charging Smart grids uncontrolled charging V2G approach