Transmission Network Expansion Planning under Uncertainty using the Conditional Value at Risk and Genetic Algorithms

摘要

The aim of this work is to study the Transmission Network Expansion Planning (TNEP) problem considering uncertainty on the demand side. Such problem consists of deciding how should an electrical network be expanded so that the future demand is ensured. We expanded the power transport problem formulation so that power losses are included in the objective function. Uncertainty is included through stochastic programming based on scenario analysis; different degrees of uncertainty are considered. Further, an explicit risk measure is added to mathematical model using the Conditional Value at Risk (CVaR). Weighting the relative importance of minimizing expansion and operational costs against the value of the CVaR simulates the attitude of the investor towards risk and shows to be of significant importance when planning the future. The problem was optimized using Genetic Algorithms. This work provided insight on how investment decisions change when considering several levels of uncertainty and risk aversion, in an extended formulation of the TNEP problem.