Genetic algorithm-based power transmission expansion planning

Large-scale combinatorial problems such as the network expansion problem present an amazingly high number of alternative configurations with practically the same investment, but with substantially different structures (configurations obtained with different sets of circuit/transformer additions). This paper presents a development of a genetic algorithm (GA) and its application to a least-cost and reliable transmission expansion-planning (TEP) problem. TEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process that is computationally impossible in a real-world TEP problem. In this paper, a GA incorporating a stochastic reproduction technique and an artificial initial population scheme are developed to provide a faster search mechanism. The objective is to optimize the system adequacy (cost, and reliability). The proposed genetic string represents the number of reinforcement ways, which is limited to a certain ways for each line of the system, subjected in total to a prespecified maximum number of reinforcements as well as the project budget. Excellent performance is reported in the test results