Optimal planning and design of integrated energy systems in a microgrid incorporating electric vehicles and fuel cell system

• Published in: Journal of Power Sources
• Main Author: Hai T.; Zhou J.; khaki M.
• Format: Article
• Language: English
• Published: Elsevier B.V. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147260601&doi=10.1016%2fj.jpowsour.2023.232694&partnerID=40&md5=e1b10f4ab5a993a59d996aa659db154e

In recent decades, as a result of diminishing fossil fuel reserves and rising social concern, electric power systems as one of the largest sectors for environmental emissions, have had no choice but to search for alternatives to alleviate the environmental problems due to conventional generation system. Plug-in electric vehicles (PEVs) can cause difficulties in the electrical grid and system operation. To circumvent this issue, an efficacious stochastic optimization model is developed in this paper to enable the control entity to manage generation and storage assets by controlling the charging behavior of PEVs. To achieve the lowest total cost, a new strategy for reducing reliability costs is proposed. In this regard, vehicle-to-grid (V2G) tool is used to reduce the overall system cost. The presented energy management model for the MG in the grid-connected mode takes into consideration the uncertainty of output power of the wind turbine (WT) and the photovoltaic (PV), as well as the PEVs’ charging and discharging. In this study, an innovative and effective optimization algorithm known as the modified fluid search optimization algorithm (MFSO) is utilized to optimize the MG operation problem. The results show that the suggested model incorporating electric vehicles (EVs) can enable MG operation at the minimum cost and reliability. As can be observed, the amount of time required by the presented method to solve the first case problem is 6.64s, which is significantly less than the amount of time required by the other methods. In a similar vein, the MFSO method is noticeably quicker than both GA and PSO when applied to case studies 2 and 3. © 2023 Elsevier B.V.