Short-term hydro-thermal scheduling using an improved bacterial foraging algorithm

This paper presents an improved bacterial foraging algorithm (IBFA) to find the optimal short-term hydro-thermal generation scheduling (STHTS). The STHTS problem is a dynamic large-scale nonlinear optimization problem which requires solving the unit commitment and economic power load dispatch problems. The bacterial foraging algorithm (BFA) is a recently developed evolutionary optimization technique which based on the foraging behavior of the E. coli bacteria. The BFA has been successfully implemented to solve various optimization problems; however, for large-scale problems such as the STHTS problem, it shows poor convergence properties. To tackle this complex problem considering its high-dimension search space, critical improvements are introduced to the basic BFA. The chemotactic step is modified to decrease linearly instead of being static and hence, to make the convergence dynamic. The proposed algorithm is validated using two test systems. Results are compared against those obtained by other algorithms previously applied to solve the STHTS problem.