A novel GA-based and meta-heuristics method for short-term unit commitment problem

This paper presents a hybrid chaos search genetic algorithm / fuzzy system and simulated annealing method (CGAFS-SA) 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. We combined a genetic algorithm with the chaos search. First, it generates a set of feasible unit commitment schedules, and then puts the solution to the SA. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The SA method on the other hand, has good local optimal search capabilities. Through this combined approach an optimal solution can be found. Numerical simulations were carried out using four cases; ten, twenty, thirty and forty thermal units power systems over a 24-hour period. The result demonstrated the accuracy of the proposed CGAFS-SA approach.