Optimization of Dynamic Transmission Network Expansion Planning Using Improved Binary Particle Swarm Optimization Algorithm

Abstract

A transmission system is one of the most important components of a power system for relaying electric power to load centers. Increasing capacities of existing generating units and construction of new generating plants to supply the additional electric power demand has resulted in congestion of transmission networks. Congestion is as a result of reaching or exceeding the voltage, transmission lines’ loading or steady-state stability limits. Persistent congestion is alleviated by construction of additional transmission lines. The Transmission Network Expansion Planning (TNEP) task is needed to determine the best set of transmission lines that can be added to a power system at minimum expansion cost without violating the network constraints during a defined planning period. In this research, voltage limit violations are penalized in a constrained Dynamic Transmission Network Expansion Planning (DTNEP) optimization problem. The number of transmission lines and their optimal location required to minimize the costs of line construction and transmission losses associated with the transmission network operations are determined. Improved Binary Particle Swarm Optimization (IBPSO) algorithm is applied to optimize the DTNEP results. IBPSO algorithm allows discrete TNEP problems to be solved by Particle Swarm Optimization (PSO) algorithm. IBPSO algorithm addresses the limitation of the BPSO algorithm by jumping out of the local optimal position to explore the search space area. The developed model is tested on Garver’s 6-bus and IEEE 30-bus systems using MATLAB. The obtained DTNEP results minimize the costs of constructing new transmission lines when compared to using Linear Programming and Linear population-size reduction Success History Adaptation Differential Evolution Semi-Parameter Adaptation hybrid Covariance Matrix Adaptation (LSHADE-SPACMA). Congestion in the network was alleviated by ensuring the transmission lines’ thermal loading were maintained at 80 % of their capacities and bus voltage limits ( 5 % of nominal voltage) were obeyed. Alleviating congestion in the network improved the adequacy of the transmission network system allowing for increased active power transfer. The developed methodology may be applied to a large power system for further studies. IBPSO algorithm may be applied with other metaheuristic methods to improve speed of convergence for DTNEP problems