Reduced Carbon Foot Print Based Localized Energy Demand Supply Using Micro Grids

Microgrid systems are the most used devices which can support the storage of a higher amount of energy with increased reliability and flexibility. In this study, a grid-connected microgrid (MG) integrated with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is introduced as a test system for optimizing costs and battery charging/discharging strategies. The framework developed represents a multi-objective function along with constraints, suitable for addressing this stochastic multi-objective problem. The performance of these physics-based optimization techniques is compared based on their effectiveness in achieving cost savings and optimizing battery charging. Upon analysis, it was found that Black Hole Optimization (BHO) exhibited significant cost savings overall, while Lightning Search Algorithm (LSA) proved more effective in optimizing battery charging strategies. This evaluation and comparison of different optimization techniques provide insights into their respective strengths and effectiveness when applied to the proposed MG test system. Specifically, BHO excelled in cost savings, whereas LSA showed superior performance in optimizing battery charging. These findings can guide researchers and practitioners in selecting appropriate optimization techniques based on the specific objectives and constraints of similar MG systems.