Economic Load Dispatch of Renewable Energy using Optimization Techniques

The modern power system has grown increasingly complex due to expanding interconnections and rising energy demand worldwide. The emphasis has transitioned toward improving performance, enhancing customer satisfaction, lowering costs, and ensuring reliable, clean energy. In this new context, the scarcity of energy resources, rising generation costs, and environmental concerns make optimal economic dispatch essential. Power stations vary in their distances from load centers and fuel cost functions, making it necessary to allocate load across different plants in a way that minimizes generation costs. Real-world economic dispatch scenarios are characterized by objective functions with strong equality and inequality restrictions that are substantially non-linear. The ANN-SVM (Artificial Neural Network-Support Vector Machine) technique is employed to distribute active electricity among generating stations while minimizing costs and adhering to system constraints. The effectiveness of this method is assessed based on its accuracy and convergence speed. Both ANN-SVM and conventional techniques are used to address the economic load dispatch problem for a six-unit system, taking into account both the cases of excluding and incorporating transmission losses. The ANN-SVM technique performs better than traditional methods, which often struggle with convergence to local optima. Since its introduction fifteen years ago, ANN-SVM has emerged as a viable solution for con strained economic dispatch problems, and optimization techniques continue to evolve to deliver more efficient and accurate results.