An evolutionary programming-based tabu search method for solving multi area unit commitment problem with import and export constraints [articol]

Abstract

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming-based tabu search (EPTS) method. The objective of this paper is to determine the optimal or a near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie- lines. The evolutionary programming-based tabu search method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie- lines depends upon the operating cost of generation at each hour and tieline transfer limits. The tie -line transfer limits were considered as a set of constraints during optimization process to ensure the system security and reliability. The overall algorithm can be implemented on an IBM PC, which can process a fairly large system in a reasonable period of time. Case study of four areas with different load pattern each containing 26 units connected via tie- lines has been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based tabu search method with conventional dynamic programming (DP), evolutionary programming (EP), Partical Swarm Optimization (PSO), Simulated Annealing (SA), Evolutionary Programming based Partical Swarm Optimization (EPPSO), Evolutionary Programming based Simulated Annealing (EPSA)) method. Experimental results shows that the application of this evolutionary programming-based tabu search method have the potential to solve multi area unit commitment problem with lesser computation time.