Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand

Abstract

Transmission network expansion planning (TNEP) is an important component of power system planning. It<br />determines the characteristics and performance of the future electric power network and influences the power<br />system operation directly. Different methods have been proposed for the solution of the static transmission<br />network expansion planning (STNEP) problem till now. But in all of them, STNEP problem considering the<br />network losses, voltage level and uncertainty in demand has not been solved by improved binary particle swarm<br />optimization (IBPSO) algorithm. Binary particle swarm optimization (BPSO) is a new population-based<br />intelligence algorithm and exhibits good performance on the solution of the large-scale and nonlinear<br />optimization problems. However, it has been observed that standard BPSO algorithm has premature<br />convergence when solving a complex optimization problem like STNEP. To resolve this problem, in this study,<br />an IBPSO approach is proposed for the solution of the STNEP problem considering network losses, voltage<br />level, and uncertainty in demand. The proposed algorithm has been tested on a real transmission network of the<br />Azerbaijan regional electric company and compared with BPSO. The simulation results show that considering<br />the losses even for transmission expansion planning of a network with low load growth is caused that<br />operational costs decreases considerably and the network satisfies the requirement of delivering electric power<br />more reliable to load centers. In addition, regarding the convergence curves of the two methods, it can be seen<br />that precision of the proposed algorithm for the solution of the STNEP problem is more than BPSO.