An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a balancing composite motion optimization (BCMO) approach

Abstract

The efficient management and optimal scheduling of generation resources in multi-supply microgrid (MG) are essential for ensuring cost-effective and reliable power supply. Renewable energy resources such as wind turbines (WTs), photovoltaic (PV) systems, micro turbine (MT) batteries, and fuel cells (FCs) are utilized. This paper presents Balancing Composite Motion Optimization (BCMO) for day-ahead cost-based generation scheduling in multi-supply MG. The BCMO approach combines the advantages of composite motion optimization techniques to minimize the overall generation cost. The proposed method considers demand forecasts, renewable energy availability, and real-time pricing to optimize generation schedule. The microgrid is modeled as mathematical optimization problem, considering multiple generation sources, including renewable energy, conventional generators, and energy storage systems, that are used to optimize the above factors. The objective function is formulated to minimize the total generation cost while satisfying demand requirements and operational constraints. By considering the cost of generation from different sources and availability of renewable energy, the proposed approach enables the microgrid to operate economically efficiently. The objective function is formulated to minimize the total generation cost while satisfying the demand requirements and operational constraints. By considering the cost of generation from different sources and the availability of renewable energy, the proposed approach enables the microgrid to operate in an economically efficient manner. The performance of the proposed system is executed on the MATLAB working platform and compared with various approaches. The best BCMO solution in cold sunny, warm sunny, cold cloudy days and warm cloudy are 290.8821, 358.7908, 261.324, and 2265.055., respectively.