The increasing global energy demand necessitates innovative solutions for energy management, especially in the realm of microgrids. Recent advancements have been made with a new Integrated Energy Management System (EMS) that employs fuzzy logic to optimize energy resources in microgrids. This system combines a hybrid utility grid, photovoltaic (PV), wind energy, and battery storage to efficiently meet the energy requirements of both residential and commercial loads.
The research focused on implementing this EMS within a microgrid located at the Sanasi Hostel Block of SRM Institute of Science and Technology in Chengalpattu, Tamil Nadu. Utilizing fuzzy logic allows for effective decision-making, adapting to varying conditions of renewable energy production and energy demands, ensuring a balance between consumption and generation.
By integrating renewable energy sources with the grid, the EMS is designed to enhance efficiency while reducing overall energy costs. Specifically, the fuzzy EMS enhanced management by achieving a 41.40% reduction in Levelized Cost of Energy (LCOE) when compared with the traditional Firefly Algorithm. Furthermore, it provided a 24.09% cost reduction compared to the Particle Swarm Optimization (PSO) Algorithm, and a 45.02% decrease in LCOE compared to the Genetic Algorithm.
The main goal of implementing this EMS is to maintain a reliable electrical power supply while minimizing operating expenses through demand-side management. The system intelligently schedules energy usage and can decide whether to draw from the grid, utilize battery reserves, or depend on renewable resources based on current conditions.
To optimize the performance of the EMS, the research employed various optimization techniques, including the Firefly Algorithm, PSO, and Genetic Algorithms, to evaluate the effectiveness of the management system. This hybrid approach ensures that the EMS is adaptive to real-time energy generation discrepancies, ultimately enhancing the microgrid's operational efficiency.
During the course of the study, data was collected regarding local solar radiation and wind performance to establish a realistic load profile. The calculated mean load was identified as 303.01 kW, with a maximum of 780.22 kW and a minimum of 61.47 kW, illustrating significant potential for optimization in energy management. By analyzing the LCOE, the fuzzy EMS consistently demonstrated the lowest costs, indicating its efficacy in various operational scenarios.
Importantly, the fuzzy EMS’s sophisticated control functions enable it to adapt based on the State of Charge (SoC) of the batteries and the availability of renewable resources. When renewable production is low, the fuzzy logic system dynamically engages grid power or battery reserves to satisfy energy needs without compromising performance.
This research emphasizes the crucial need for reliable energy management systems, particularly in areas where renewable resources are integral to electrical supply. The deployment of the fuzzy EMS not only addresses the challenges of intermittent renewable energy sources but also positions itself as a cost-effective solution for managing grid-tied microgrids, optimizing both sustainability and economic viability.
The results assert that innovative EMS strategies enhanced by fuzzy logic can significantly minimize energy expenditures while ensuring the maximization of renewable energy use in microgrids, paving the way for more sustainable energy systems in the future.
