Publications

Abstract : The non-convex and combinatorial nature of the UC-ELD problems requires the application of heuristic algorithms to generate optimal schedules. In studies reported so far, the Unit Commitment and the Economic Load Dispatch problems are solved as separate problems. In the addressed work, the commitment and de-commitment of generating units is obtained using a Genetic Algorithm (GA), and the optimal load distribution of the scheduled units is obtained using Improved Differential Evolution with Opposition Based Learning (IDE-OBL). The power demand is varied for 24 hours to determine the schedule in the IEEE 30 bus system including transmission losses, power balance and generator capacity constraints. Optimal distribution of load among generating units, fuel cost per hour, power loss, total power and computational time are computed for each of the test systems using the intelligent algorithms. From the comparative analysis, it can be concluded that GA-IDE-OBL is a better approach for solving UC-ELD problems in terms of optimal solution, robustness, and computational efficiency.