A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization.