Planeamiento de la transmisión considerando seguridad e incertidumbre en la demanda empleando programación no lineal y técnicas evolutivas

Abstract

This paper proposes a methodology for solving the Transmission Expansion Planning Problem considering single contingencies (N-1) and future demand uncertainty. To solve this problem, a specialized Chu-Beasley Genetic Algorithm (CBGA) is used so that investment plans can be suggested. These plans are evaluated through a Higher Order Interior Point Method for Linear Programming or through a Predictor Corrector Method. Additionally, initialization of the CBGA is carried out using Non-linear Interior Point. The methodology is validated using three test systems from the specialized literature: 46-Bus South-Brazilian, IEEE 24-Bus, and a 6-Bus Garver system. Results demonstrate the validity of this approach to solving the transmission planning problem when contingencies are considered; which is attained by finding expansion plans of minimum cost.

Keywords

• genetic algorithm
• contingencies
• demand uncertainty
• interior point method
• optimization