An Improved Sine Cosine Algorithm for the Day-ahead Microgrid Management in the Presence of Electric Vehicles

(MG) is capable of accommodating renewable energy sources (RESs) with high flexibility. With the rapid development of MGs, plug-in hybrid electric vehicles (PHEVs) are gaining increasing attention since they can alleviate pollution and reduce energy consumption. The appearance of PHEVs would exacerbate the power supply shortages and bring new challenges to the power system. This paper develops an effective day -ahead optimal scheduling of a MG, taking into account RESs, storage devices and PHEVs. The Monte Carlo simulation is utilized to model the uncertainties of PHEVs. A smart charging/discharging strategy incorporating the V2G technique is proposed to smooth the demand curve and reduce the operational costs. To handle the MG scheduling problem in the presence of PHEVs, an improved sine cosine algorithm with simulated annealing based local search operator and chaotic opposition learning strategy (CSCASA) is proposed to minimize the total costs. The proposed algorithm can keep a better balance between global and local search abilities. CSCASA is first validated on some benchmark problems. Then, CSCASA is employed to generate optimal schedule of a grid -connected MG with PHEVs. The experimental results demonstrate the superior performance of CSCASA in the optimal MG scheduling problem with and without PHEVs.