Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators

Abstract

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems with considering transmission security and bus voltage constraints for practical application. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic crossover and mutation schemes are proposed to deal with continuous/discrete control variables, respectively. The objective of OPF is defined that not only to minimize total generation cost but also to enhance transmission security, to reduce transmission loss, to improve the bus voltage profile under normal or contingent states. Moreover, the valve-point loading effect of thermal units should be taken into consideration. The effectiveness of the proposed method is demonstrated for a 26-bus and the IEEE 57-bus systems, and it is compared with the evolutionary programming (EP) in terms of solution quality and evolutionary computing efficiency. The experimental results show that the MIGA-based OPF method is superior to the EP.