Optimization of a multi-energy microgrid in the presence of energy storage and conversion devices by using an improved gray wolf algorithm

The growth of electricity consumption and demand for higher quality of electricity has directed the electricity industry towards using new technologies. In this paper, a novel model is proposed for the optimal scheduling of multi-energy microgrid (MEM) systems that rely on renewable energy sources. The model incorporates various emerging high-efficient technologies, including electric vehicle (EV) parking lots, power-to-gas (P2G) facilities, and demand response programs. The MEM system described in this paper comprises several components, such as wind energy generation, multi-carrier energy storage technologies, a boiler, a combined heat and power unit, power-to-gas (P2G) capability, electric vehicles (EVs), and demand response programs. The main objective of the system is to minimize the total operational cost. Moreover, the system operator has the opportunity to actively participate in the electricity, heat, and gas markets, enabling them to fulfill local energy demands and maximize profits through energy exchanges. The proposed system is exposed to uncertainties caused by wind power, demand, electric vehicles and power price. The optimization process in this study utilizes the Gray Wolf Algorithm, which has been enhanced by incorporating the Local Escaping Operator method. This improved algorithm is employed to optimize the scheduling and operation of the multi-energy microgrid (MEM) system. The goal of the Gray Wolf Algorithm, in combination with the Local Escaping Operator method, is to find optimal solutions that minimize the total operational costs of the MEM system while considering factors such as renewable energy generation, energy storage, demand response programs, and the integration of emerging technologies. The results obtained from the optimization process demonstrate a reduction in the total cost of operations when various technologies are integrated into the grid. By applying the proposed optimization model, which incorporates the Gray Wolf Algorithm improved via the Local Escaping Operator method, the overall operational costs of the MEM system are decreased. This reduction in costs is achieved by efficiently managing the integration of renewable energy sources, energy storage technologies, demand response programs, and other emerging technologies within the grid. Simulations are conducted for different scenarios, and the results show that integrated energy scheduling, along with the incorporation of emerging technologies and vehicle-to-grid capability, can reduce the total operating cost by 15%. Furthermore, the integration of multi-carrier energy storage systems with electricity-to-gas technology can reduce daily performance costs by up to 10%.