Hybrid whale optimization and pattern search algorithm for day-ahead operation of a microgrid in the presence of electric vehicles and renewable energies

Abstract

A significant fraction of the environmental emissions is due to the power generation sector and burning fossil fuels to produce electricity. Moreover, the transportation system with conventional fossil-fuel vehicles plays a key role in climate change. Accordingly, the generation sector has already changed its planning strategies to employ more renewable energies to supply the load demand, particularly at the distribution level. Besides, other alternatives have been being used in the transportation system to alleviate the pollution, caused by this sector, and plug-in hybrid electric vehicles (PHEVs) have grabbed attention. However, it should be noted that connecting a large number of PHEVs would impose a considerably high load demand on the distribution system, and may cause different problems. In this regard, this research study develops an effective day-ahead resource scheduling framework for a microgrid (MG), taking into account the PHEVs and renewable energy sources (RESs). The model has been defined for an MG, which is equipped with renewable and non-renewable energy-based distributed generation (DG) technologies, storage devices, and PHEVs. The proposed model addresses the uncertain parameters, relating to the hourly value of the load, the price of energy, procured by the upstream network, and renewable power generation, by deploying Monte-Carlo simulation (MCS). Furthermore, the nickel–metal hydride (Ni-MH) battery as a widely-used and reliable technology is employed in this study. The resource scheduling problem is introduced in the framework of an optimization problem with one objective function, intended to minimize the total cost of operation over a 24-h horizon. Then, an efficient optimization method, named the hybrid whale optimization algorithm and pattern search (HWOA-PS), is utilized to cope with the mentioned optimization problem. The results, found by this approach would then be compared to the ones, obtained from other approaches to validate the results.