A discrete evolutionary PSO based approach to the multiyear transmission expansion planning problem considering demand uncertainties

This paper presents a multiyear dynamic model to the Transmission Expansion Planning, TEP, problem to identify the most suitable set of projects as well as their scheduling along the planning horizon. The candidate plans are evaluated using a fitness function that incorporates operation and investment costs plus a set of penalty terms. These terms are associated with the level of losses, non-zero values for the power not supplied namely for the entire system and for n − 1 contingencies, financial limits, maximum number of projects to implement in each year or all along the horizon and the capability to accommodate not only the expected demand, but also uncertainties affecting the demand forecasts. Given the discrete nature of the problem, we adopted an enhanced approach of the PSO algorithm to solve it. This includes an evolutionary adaptation of the PSO movement rule as well as several modifications to ensure that along the iterative process each candidate solution is technically feasible given its discrete nature. The paper also reports the results of a set of tests to evaluate several design decisions related with the development of the Discrete Evolutionary PSO, DEPSO, as well as to compare the results of its application to the TEP with results reported by other researchers.

► Adaptation of the PSO based approach to address integer optimization problems. ► Formulation of the multiyear Transmission Expansion Planning, TEP problem as a mixed integer optimization problem. ► Integration of demand uncertainties modeled by triangular fuzzy sets. ► solution algorithm of the TEP problem using the enhanced PSO based approach. ► Illustration of the developed approach using two Case Studies.