Operations Research Techniques For Optimizing Smart Grid Systems

The application of operations research techniques, advanced algorithms related to optimizing smart grids for their smart operations, and distributed energy resources, electric vehicle (EV) charging coordination, and load forecasting are focused upon in this paper of research. Four key algorithms were used to optimize energy distribution: GA, PSO, ML, and RL, which ultimately helped to improve grid stability and thus bring a decline in the operational cost. Demonstrations of results show 12% improvement in grid efficiency, 15% reduction in energy loss, and 10% improvement as compared to conventional methods in terms of load forecasting accuracy. Coordinated EV charging using reinforcement learning produces a 20% increase in system fairness. Validation is completed through a comparative analysis with existing works. This finding also manifests the need for AI-enabled decision-making as something related to smart energy systems for tackling future energy challenges.