Artificial Immune Systems and Differential Evolution Based Approaches Applied to Multi-Stage Transmission Expansion Planning

Abstract

Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the demand in an adequate quality level to customers along the planning horizon, while minimizing investment, operational, and interruption costs. Optimization approaches based on metaheuristics have demonstrated a good potential to find high quality solutions. Their success is related to the ability to avoid local optima by exploring the basic structure of each problem. Numerous advantages can be linked to these tools: a simple software complexity, an ability to mix integer and non-integer variables, and a faster time-response. This paper presents a performance comparison between two optimization tools based on artificial immune systems and differential evolution to solve the multi-stage TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and operational costs related to ohmic transmission losses. The multi-stage nature of the TEP is also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.