Optimizing Electrical Networks with Metaheuristics, FACTS, and RES

This thesis presents a methodology to enhance the performance of electrical networks by integrating Flexible AC Transmission Systems (FACTS) with renewable energy sources, including wind and solar power. Addressing a complex challenge that demands precise system planning, we applied an Optimal Power Flow (OPF) approach using the Spider Wasp Optimizer (SWO) algorithm. The objectives include minimizing power losses, reducing fuel costs and emissions, and optimizing voltage profiles. For multi-objective scenarios, the method was extended to the Multi-Objective Spider Wasp Optimizer (MOSWO) and validated using standard mathematical and geometric test functions. Comparative results demonstrate the superior performance of the proposed algorithms. Furthermore, in the economic integration of renewables, with or without optimal placement of Static Var Compensators (SVCs), the approach effectively reduces generation costs through efficient scheduling of thermal, wind, and solar units. Optimal coordination of FACTS devices and renewable resources significantly improves system efficiency and economic outcomes while respecting operational constraints.