Economic and Economic-Emission Operation of All-Thermal and Hydro-Thermal Power Generation Systems Using Bacterial Foraging Optimization

Abstract

Electric power is a basic requirement for present day life and its various economic sectors. To satisfy the ever-increasing needs for electricity, the number of generating units, transmission lines and distribution systems is rising steadily. In addition, electric power systems are among the most complex industrial systems of the modern age. Beside complexity, the generation of electric power is a main source of gaseous emissions and pollutants. The planning and operation of electric power systems must be done in a way that the load demand is met reliably, cost-effectively and in an environmentally responsible manner. Practitioners strive to achieve these goals for successful planning and operations utilizing various optimization tools. It is clear that the objectives to be satisfied are mostly conflicting. In particular, minimizing the fuel cost and the gaseous emissions are two conflicting and non-commensurate objectives. Therefore, multi-objective optimization techniques are employed to obtain trade-off relationships between these incompatible objective functions in order to help decision makers take proper decisions. In this thesis, two main power system operation problems are addressed. These are the economic load dispatch (ED) and the short-term hydro-thermal generation scheduling (STHTS). They are treated first as single-objective optimization problems then they are tackled as multi-objective ones considering the environmental aspects. These problems, single and multi-objective, are nonlinear non-convex constrained optimization problems with high-dimensional search spaces. This makes them a real challenge for any optimization technique. To obtain the optimal or close to optimal solutions, a modified bacterial foraging algorithm is proposed, developed and successfully applied. The bacterial foraging algorithm is a metaheuristic non-calculus-based optimization technique. The proposed algorithm is validated using diverse benchmark optimization examples before implementing it to solve the problems of this thesis. Various practical constraints are considered in the different cases of each problem. These include transmission losses, valve-point effects for both the ED and the STHTS problems and water availability and reservoir configurations for the STHTS problem. In all cases the optimal or near-optimal solution is obtained. For the multi-objective optimization cases, the Pareto optimal solution set that shows the trade-off relationship between the conflicting objectives is successfully captured.