Multi-objective short-term hydro-thermal scheduling using bacterial foraging algorithm

In this paper, the optimization problem of the short-term hydro-thermal scheduling (STHTS) is addressed considering the environmental aspects. To solve this multi-objective problem, an improved bacterial foraging algorithm (IBFA) is implemented. In addition to minimizing the cost function, the minimization of gaseous emission is also considered. Operating cost of thermal generating units, NO_x, SO₂ and CO₂ emission are minimized over the scheduling period considering various thermal and hydro constraints. The environmentally constrained STHTS problem, as it is the case of the classic one, is a dynamic large-scale nonlinear optimization problem which requires solving unit commitment and economic power load dispatch problems. The bacterial foraging algorithm (BFA) is a recently developed evolutionary optimization technique based on the foraging behavior of the E. coli bacteria. The BFA has been successfully employed to solve various optimization problems; however, for large-scale problems, it shows poor convergence properties. In fact, the basic BFA cannot be applied to solve complex problems such as the STHTS problem. To tackle this problem considering its high-dimensional search space, significant improvements are introduced to the basic BFA. The algorithm is validated using a well known hydro-thermal generation system. Results are obtained and the trade-off set of solutions is successfully captured.