Scheduling of renewable energy and plug-in hybrid electric vehicles based microgrid using hybrid crow-Pattern search method

Electric vehicles are fast becoming a key device in the transportation system to reduce the challenges caused by environmental issues and the energy crisis. The increasing penetration of PHEVs in the distribution networks with appropriate management provides more flexibility and controllability for the power system. In this study, the day-ahead operation problem of a microgrid (MG) containing different renewable energy-based technologies like solar photovoltaic (PV) system, wind turbine (WT), and PHEVs is investigated. In addition, the uncertainties in the problem formulation are accurately modelled in the optimal management of the microgrid using the Monte Carlo simulation (MCS). The proposed day-ahead scheduling is modelled for 24 h considering the uncertainties in the charging demand of electric vehicles, loads, price, and output power of stochastic RERs. To investigate the behavior of storage devices on the optimal operation of the MG, NiMH-Battery is integrated into the system. The problem is implemented while aimed at optimizing the total operating cost of the MG. To deal with the high complexity of the problem, the hybrid crow search and pattern search (HCS-PS) method is used to consider the entire search space globally. Results in scenario 1, the MG operating cost by employing the HCS-PS method is 262.784 euro ct/day which is lower than those reported by the genetic algorithm, crow search, and also PSO. Similarly, the cost values reported for scenario 2 and scenario 3 are 299.8513 euro ct/day and 337.2845 euro ct/day, respectively, using hybrid CS-PS algorithm. The results confirm the effectiveness of the suggested HCS-PS method with high convergence appropriately. Additionally, the generation costs of the offered HCS-PS are lower than those of the classical crow search algorithm and other conventional optimization methods.