A day-ahead economic scheduling of microgrids equipped with plug-in hybrid electric vehicles using modified shuffled frog leaping algorithm

With the ever-increasing energy demand for energy all across the world and also, the increasing concern on environmental issues due to using fossil fuels mostly in power systems, some appropriate alternatives should be used to solve the issue. In this respect, renewable energy sources (RESs) with almost zero pollution have been turned into the first choice for supplying the required energy. In this study, to achieve the minimum total cost of the grid, a novel problem formulation is proposed to reduce the reliability costs. Simultaneously, the transportation sector has been replacing the current conventional fossil-fuel vehicles with electrified ones, where plug-in electric vehicles (PEVs) as well as plug-in hybrid electric vehicles (PHEV) have captured the attention and there is an increasing rate in using electric vehicles (EVs). These vehicles are capable of connecting to the electrical grid to absorb/deliver energy from/to the grid through the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. On the other hand, a new concept in power systems, namely microgrid (MG) has been introduced to facilitate the integration of RESs and make the most of EVs’ capabilities using the smart infrastructure. In fact, the vehicle to grid (V2G) capability is used to decrease the operating cost to achieve a proper opportunity to accommodate PEVs in the network. The output power produced by RESs is volatile, which is more obvious in wind energy and solar power. Thus, the resource management issue of MGs would be of very high significance. In this regard, this paper proposes an efficient optimization framework for the optimal day-ahead energy management of MGs, including PEVs and RESs, utilizing an effective stochastic programming method, i.e. unscented transformation (UT). It is significant to note that the problem has tackled as a single-objective stochastic optimization problem, while the objective has been defined as minimizing the total cost of operation, The presented stochastic optimization problem is then solved by employing a nature-inspired efficient method, named “modified shuffled frog leaping algorithm (MSFLA)” and the obtained results are compared to those reported by other methods to verify its performance.