Efficient energy management of domestic loads with electric vehicles by optimal scheduling of solar-powered battery energy storage system

• Published in: Electric power systems research 2024-09, Vol.234, p.110570, Article 110570
• Main Authors: Ullah, Zia, Qazi, Hasan Saeed, Rehman, Anis Ur, Hasanien, Hany M., Wang, Shaorong, Elkadeem, Mohamed R., Badshah, Fazal
• Format: Article
• Language: English
• Subjects: Battery energy storage system; Electric vehicle scheduling; Electric vehicles; Energy economics; Energy management; Optimal charging/discharging strategy; Solar power; State of charge
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2024.110570

•This paper presents efficient energy management for loads with and without EVs.•This is based on optimal scheduling of solar-powered battery energy storage system.•A case study of a practical system is investigated.•The model considers seasonal variation effects of power generation and EV charging.•The results of proposed model are compared with other models. The increasing adoption of electric vehicles (EVs) and variable energy usage patterns substantially strain the electrical grid; indeed, optimal energy management, monitoring, and utilization are required for the reliable operation of the grid. This paper introduces a novel model design of a solar-powered battery energy storage system (SPBESS) as a viable substitute for conventional demand-side management (DSM) and time of use (ToU) pricing schemes, intending to optimize energy management and utilization with IoT monitoring. In addition, the IoT-based prototype has been developed to monitor and control the proposed system. To validate the proposed SPBESS model, the study examines two distinct cases: one encompassing domestic loads and the other integrating EV loads alongside household demand. Using ToU pricing, it determines the optimal charging and discharging strategies for the SPBESS, evaluates their implications for the system configuration and grid ToU pricing, and quantifies the annual reductions in power purchases, electricity bills, energy costs, and carbon emissions. Moreover, the study comprehensively analyzes the optimal power exchanges between the photovoltaic system, battery energy storage system, and the grid, precisely considering the specific load requirements and grid ToU pricing. The conducted research findings manifest considerable decreases in energy costs, with the domestic load condition witnessing a reduction from $0.312 per kilowatt-hour to $0.245 per kilowatt-hour and the scenario involving EV and residential loads experiencing a decline from $0.27 per kilowatt-hour to $0.197 per kilowatt-hour. Furthermore, the annualized cost savings for cases 1 and 2 amount to $1,336 and $4,544, respectively, yielding substantial reductions in polluting gas emissions. Besides, the IoT-based constructed prototype model design for real-time power parameters monitoring and control shows the importance of IoT-based monitoring for remote load control, allowing users to maximize energy efficiency, resource utilization, and system visualization.