Research On Economic Operation Problem Of Power Plants

In power stations, the investment is quite expensive, and the resources in operating them are considerably becoming sparse of which the focus turns to optimizing the operating cost of the power station. In today's world, it becomes an utmost necessity to meet the demand as well as optimize the generation.The objective of this dissertation is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours and dispatch the load between the running units economically.Dissertation presents a new algorithm by integrating genetic algorithm(GA),evolutionary programming(EP) , tabu search(TS) and Guo's algorithm,to solve the unit commitment (UC) and non-convex economic dispatch problem(NED).According to the character of problem, this dissertation uses the different algorithm in the different stage of computation. In the stage of unit commitment, fast genetic algorithm, which adopting binary encoding method, is introduced. However, in the stage of economic dispatch, evolutionary programming-based tabu search was employed. This problem involves the economic dispatch with valve-point effects(EDVP),economic dispatch with piecewise quadratic cost function(EDPQ),and economic dispatch with prohibited operating zones(EDPO). The economic dispatch problem was solved in two phases;the cost-curve-selection sub problem,and the typical ED solving sub problem.Using a hybrid EP and TS resolved the first phase. The second phase was resolved by Guo's algorithm. In the solving process,EP with repairing strategy was used to generate feasible solutions,TS was used to prevent prematurely, and Guo's algorithm was used to enhance the performance.Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms.