Improved Binary Differential Evolution for Transmission Expansion Planning

Increasing load and penetrating solar PhotoVoltaic (PV) systems tend higher in the future. Transmission Expansion Planning (TEP) is commonly used to solve this issue and is typically formed by using the peak load scenario, which is often assumed to represent the worst-case condition. Metaheuristic algorithms are predominantly used to address the TEP problem, which is frequently characterized by solutions becoming trapped in local optima. To address this challenge, this study proposes a TEP approach, utilizing an improved Binary Differential Evolution (BDE) algorithm in which the mutation factor and crossover rate are dynamically adjusted using a logistic map, enabling more effective exploration of the solution space. The simulation results are tested using the IEEE 14-bus system. The simulation results demonstrate that the improved BDE algorithm can decrease the likelihood of entrapment in local optima by providing a lower total cost by 15.89% when compared with the conventional BDE algorithm.