Short-term economic emission power scheduling of hydrothermal systems using improved chaotic hybrid differential evolution

Increasing concerns over atmospheric pollution forces the power-producing utilities to retain their generations within maximum allowable emission levels. Therefore, in present-day power system operations, the minimization of emission pollutants along with the total fuel cost has become an important aspect in short-term generation scheduling of hydrothermal power systems. This paper presents an improved hybrid approach based on the application of chaos theory in a differential evolution (DE) algorithm for the solution of this biobjective constrained optimization problem. In this proposed methodology, self-adjusted parameter setting in DE is obtained by using chaotic sequences. Secondly, a chaotic hybridized local search mechanism is embedded in DE to avoid it from trapping at local optima and to enhance its search space exploring ability. Furthermore, new heuristic strategies are developed to effectively handle the complex hydraulic and thermal constraints. The feasibility and usefulness of the developed approach are demonstrated by its application on a standard hydrothermal test system compromising four multicascaded hydel plants and three thermal plants and the following three case studies are investigated: economic power scheduling, economic emission scheduling, and economic emission power scheduling. The simulation results illustrate the superiority of the proposed approach as compared to other recently established techniques.