Optimizing Power Grids with Wind and FACTS Devices using Multi-objective Exponential Distribution Optimizer for Enhanced System Performance

This research introduces the Multi-objective Exponential Distribution Optimizer (MOEDO), a novel algorithm tailored to solve complex optimization challenges in power grid management, focusing on Optimal Power Flow (OPF) problems. MOEDO enhances the basic Exponential Distribution Optimizer by incorporating advanced techniques such as non-dominated sorting and crowding distance, alongside an Information Feedback Mechanism (IFM). This integration aims to optimize the efficiency of power systems by reducing fuel consumption and better integrating renewable energy sources within an IEEE-30 bus framework, particularly through the use of stochastic wind energy and various Flexible AC Transmission Systems (FACTS). The MOEDO excels over traditional algorithms like MOEO, MOFDA, and MOGNDO by offering faster convergence, greater solution diversity, and superior management of power flows, making it highly effective in handling the complexities of modern power grids. By combining heuristic strategies with advanced technologies, MOEDO provides a sophisticated framework designed to meet the dynamic demands of contemporary power systems, enhancing system performance and sustainability. This study underscores the necessity of evolving optimization methodologies to efficiently tackle real-world challenges in the power sector.