Hybrid immune genetic algorithm approach for short-term unit commitment problem

This paper presents a Hybrid Immune Algorithm (IA)/Genetic Algorithm (GA) and Fuzzy System (FS) method (IGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. First, we combined the IA and GA, then we added the fuzzy system approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases; ten, twenty and thirty thermal unit power systems over a 24 hrs period. The produced schedule was compared with several other methods, such as Dynamic Programming (DP), Lagrangian Relaxation (LR), Standard Genetic Algorithm (SGA), Traditional Simulated Annealing (TSA), and Traditional Tabu Search (TTS). The result demonstrated the accuracy of the proposed CIGAFS approach.