A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

AC transmission expansion planning (ACTEP) is one of the most critical issues in electric power system expansion planning. In existing research on ACTEP, the reduction of power losses is often overlooked due to the significant computational workload associated with ACTEP problem. While in some instances, minimizing power loss is included as an objective function in the TEP problem, this approach may impact the addition of new lines. Consequently, to address this issue, a four-stage strategy is proposed in this paper for resolving ACTEP problem while considering power loss reduction. Specifically, the reduction of power losses is addressed after the deployment of new transmission lines. Moreover, a hybrid approach, referred to as differential evolution (DE) combined with teaching–learning-based optimization (TLBO) algorithms, called (DE-TLBO), is proposed for optimizing reactive power planning and determining the size of thyristor-controlled series compensators (TCSC) to minimize power loss in ACTEP problem. Simulation results conducted on Graver 6 bus, IEEE 24 bus, and modified IEEE 118 bus systems demonstrate the efficacy of the proposed algorithm when compared to conventional methods such as differential evolution (DE), modified artificial bee colony, and real genetic algorithms (RGA). Additionally, the proposed method also illustrates the effectiveness of utilizing TCSC to reduce power loss in the Graver 6 bus and the IEEE 24 bus systems by 3.72% and 11.95%, respectively.