Assessment of most critical project delays on a multi-stage transmission expansion plan using particle swarm optimization

Transmission reinforcement projects of a Transmission Expansion Plan can suffer implementation delays that can impact the short-term power system operation in unpredicted ways. These delays may lead project managers to paying particular care to the development of those plans proved to be most critical from this point of view. This paper proposes a multi-objective dynamic-programming based method to evaluate the impacts of such delays in a short-term transmission plan with respect to load shedding, reliability and losses. It uses the Binary Pareto-Optimal Particle Swarm Optimization as optimization tool. For the trade-off analysis and the final decision to be made, the paper proposes indices to rank the projects in the plan from most to least critical, should they be delayed, based on the severity of the impacts and the probability of occurrence, both on an annual basis and for the whole planning horizon. The proposed method has been tested on an actual Brazilian 40-bus subtransmission system for a 3-year and a 6-year planning horizon, and with the 24-bus IEEE RTS reliability test system for a 3-year planning horizon. To assess its accuracy and efficiency, the results are compared to those using the Non-dominated Sorting Genetic Algorithm II method (NSGA-II) and to the exhaustive search that considers every delay configuration, showing the same accuracy as the exhaustive search and better convergence properties than NSGA-II. The most critical projects are accurately ranked helping the project manager to decide which projects should receive higher attention to guarantee its construction on the planned date. The method obtained a reduction in computational time of 35% for the Brazilian system and of 42% for the IEEE RTS in relation to the exhaustive approach, demonstrating its efficiency.