Smart SVC Placement for Loss Reduction in Power Systems: Tackling Generator Outages with IGEPO Optimization

Abstract

Increasing load demand and generator outages in power transmission networks can cause significant power losses and voltage instability. Addressing these challenges requires an optimal installation strategy for Static Var Compensators (SVC), which involves determining the best locations and sizes for the SVCs to ensure efficient, reliable, and cost-effective operation. Traditional optimization techniques often struggle with issues like local optima and inadequate exploration. This paper presents a novel approach, the Integrated Grasshopper Evolutionary Programming Optimization (IGEPO), which combines the Grasshopper Optimization Algorithm with Evolutionary Programming. The IGEPO method is applied to optimize the placement and sizing of SVCs, with the goal of minimizing losses, particularly during generator outages. Comparative studies on the IEEE 30-Bus RTS demonstrate that IGEPO outperforms both standalone Evolutionary Programming and the Grasshopper Optimization Algorithm in power system planning under normal conditions and during contingencies due to generator outages. Results are presented for the pre-SVC installation under both normal conditions and during generator outages to observe the impact of the outages and the subsequent SVC installation. The proposed algorithm has potential for broader applications and could be explored further in future studies.