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 the system's operation is excessive and long-lived. The aim of TEP is to determine new transmission lines effectively for a 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 genetic algorithm (GA) approaches for TEP are reviewed in the literature and then a new hybrid GA with linear modeling is proposed. The proposed GA method has a flexible structure and the effectiveness of the method is assessed on Garver 6-bus, IEEE 24-bus, and South Brazilian test problems in the literature. It is observed that newly proposed hybrid GA shows a rapid convergence on the test problems. Scenarios are then generated for uncertainties such as change in demand, oil prices, environmental issues, precipitation amounts, renewable generation, and production failures. Numerical results demonstrate that test problems are resolved successively under uncertainty conditions with the proposed hybrid algorithm.