A PSO based approach for multi-stage transmission expansion planning in electricity markets

• We solve the multi-stage transmission expansion planning (TEP) problem.
• The TEP problem is solved in a competitive pool-based electricity market.
• We propose a modified PSO (MPSO) approach to solve the TEP problem.
• The MPSO controls diversity to overcome the problem of premature convergence.
• The results show the MPSO is a reliable, accurate, fast and convergent method.

This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N − 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely modified PSO (MPSO), uses a diversity controlled PSO to overcome the problem of premature convergence in basic PSO (BPSO) plus an initial high diversity swarm to cover the search space efficiently. The MPSO model is applied to the Garver six-bus system and to the IEEE 24-bus test system and compared to the BPSO model and a genetic algorithm based model.