Energy-Efficient Optimal Scheduling of Renewable Energy Sources in Power Systems Using Genetic Algorithms and Support Vector Machines

Abstract As global energy demand is on the rise and so is the need for sustainable solutions, it has become imperative for RES scheduling to ensure grid reliability, minimize operation costs, and de-fossilize sources. This paper proposes a combined hybrid approach in using genetic algorithm (GA) to bestow renewable energy forecasting and scheduling into power systems alongside Support Vector Machine (SVM). The GA seeks the optimum scheduling decisions based on total operational cost minimization, while SVM provides precise short-term predictions of RES generation using historical and real-time meteorological data. The integrated model thus facilitates dynamic adaptation to environmental variability and system changes, which enhance energy system responsiveness. Performance evaluation indicated that the model achieves 92% mean absolute percentage error (MAPE) forecasting accuracy, allows up to 25% reduction in total operational costs, and increases by 20% renewable energy integration. It also achieves economic dispatch performance at less than 5% from optimal benchmark models with an overall average system efficiency of 95%. All these findings prove the model's ability to better predictive accuracy, efficiency in decision-making, and energy-saving improvements in the management system. Solar irradiance and wind velocity data of the National Renewable Energy Laboratory (NREL) of Tamil Nadu, India, 2015–2022 (42,560 hourly samples) along with regional load demand and electricity prices were used to validate the model, as well as load demand in the region and electricity prices. The GA–SVM achieved 92% MAPE forecasting accuracy for combined solar and wind generation, 25% cost reduction, and 20% increase in renewable energy integration. In addition to technical benefits, the model has economic benefits of cutting down on fossil fuel costs, decreasing operating costs to utilities, and enhancing tariff competitiveness to end-users.