Adaptive bacterial foraging and genetic algorithm for unit commitment problem with ramp rate constraint

Summary Solving unit commitment (UC) problem is one of the most critical tasks in electric power system operations. Therefore, proposing an accurate method to solve this problem is of great interest. The original bacterial foraging (BF) algorithm suffers from poor convergence characteristics for larger constrained optimization problems. In addition, the stopping criterion used in the original BF algorithm increases the computation burden of the original algorithm in many cases. To overcome these drawbacks, a hybridized adaptive BF and genetic algorithm (HABFGA) algorithm is proposed in this paper to solve the UC problem with ramp rate constraint. The HABFGA algorithm can be derived by combining adaptive stopping criterion, BF algorithm and genetic algorithm; therefore, the drawbacks of the original BF algorithm can be treated before employing it to solve the complex UC problem. To illustrate the effectiveness of the HABFGA algorithm, several standard and real test systems with different numbers of generating units are used. The results of HABFGA algorithm are compared with the results obtained using other published methods employing same test systems. This comparison shows the effectiveness and the superiority of the proposed method over other published methods. Copyright © 2015 John Wiley & Sons, Ltd.