Comparative Evaluation of Different Fuzzy Tuning Rules on Energy Management Systems Cost Savings

• A fuzzy logic-based EMS with a different rule set is proposed to achieve cost savings in a grid-connected hybrid solar PV and battery storage system. • The proposed fuzzy logic controller (FLC)-EMS incorporates a reduced rule set, which is compared to a more extensive rule set. • The integration of demand-side management in the FLC-EMS enhances the system's operational efficiency by effectively balancing the energy demand between the grid and the solar PV, particularly for powering heavy-duty appliances. • Furthermore, this study employs load categorization to reduce the typically required solar PV/battery installation costs in hybrid energy systems by prioritizing the use of the renewable source for serving the heavy loads. This paper presents a comparative evaluation of two fuzzy logic tuning approaches applied to the energy management system (EMS) of a grid-connected hybrid solar photovoltaic (PV) and battery storage system. Unlike optimization-driven methods that require extensive training and computational resources, the proposed rule-based fuzzy logic approach ensures fast execution, making it highly suitable for real-time applications. The EMS optimizes cost savings by dynamically balancing energy generation, storage, and consumption in response to fluctuating resource availability. The study employs simplified fuzzy tuning rules to enhance cost efficiency by minimizing grid energy imports, optimizing battery charge/discharge cycles, and maximizing solar energy utilization. Simulation results reveal that the optimized 12-rule fuzzy EMS achieved a 17.5% reduction in daily grid costs compared to a less optimized 16-rule EMS and conventional fixed-rule EMS approaches. Additionally, long-term techno-economic analysis over 20 years demonstrated energy cost savings of up to 23.5%, emphasizing the importance of rule optimization in hybrid energy management. The findings highlight the comparative advantages of both approaches and their suitability for various energy consumption patterns, emphasizing their potential for substantial cost savings and improved system efficiency in hybrid energy systems.