Transmission expansion planning based on a hybrid genetic algorithm approach under uncertainty

Abstract

Transmission expansion planning (TEP) is one of the key decisions in power systems. Its impact on thesystem’s operation is excessive and long-lived. The aim of TEP is to determine new transmission lines effectively fora current transmission grid to fulfill the model objectives. However, to obtain a solution, especially under uncertainty,is extremely difficult due to the nonlinear mixed-integer structure of the TEP problem. In this paper, first geneticalgorithm (GA) approaches for TEP are reviewed in the literature and then a new hybrid GA with linear modelingis proposed. The proposed GA method has a flexible structure and the effectiveness of the method is assessed onGarver 6-bus, IEEE 24-bus, and South Brazilian test problems in the literature. It is observed that newly proposedhybrid GA shows a rapid convergence on the test problems. Scenarios are then generated for uncertainties suchas change in demand, oil prices, environmental issues, precipitation amounts, renewable generation, and productionfailures. Numerical results demonstrate that test problems are resolved successively under uncertainty conditions withthe proposed hybrid algorithm.