PLANNING OF RENEWABLE ENERGY RESOURCES AND CONNECTION TO POWER GRIDS: OPTIMIZATION AND STABILITY

Abstract

In the current century, electrical networks have witnessed great developments and continuous increases in the demand ffossil-fuel-based energy, leading to excessive rise in the total production cost and the pollutant gases emitted by thermalplants. Under these circumstances, energy supply from different resources became necessary, such as renewable energy sources (RES) as an alternative solution. These sources, however, are characterized by uncertainty in their operationaprinciple, especially when the system operator needs to define the optimal contribution of each resource to ensureconomic efficiency and enhanced grid reliability. However, even with the huge demand met, networks still face otheproblems such as power loss and voltage instability. Therefore, FACTS devices appear as an effective solution, but theyremain expensive. This thesis addresses the growing complexity of modern power systems as they incorporate high sharof variable renewable energy. A unified framework is developed that couples probabilistic modelling of wind, solar, andhydro output using Monte Carlo simulation and specific probability density functions (Weibull distribution for winspeeds, lognormal distribution for solar irradiance, and Gumbel distribution for river flow) with an enhanced metaheuristioptimizer tailored for large-scale optimal power flow. Key developments in the Kepler Optimization Algorithm includea novel exploratory–exploitative search operator for deeper solution-space exploration and a non-dominated sortingscheme to support efficient multi-objective trade-offs. Additionally, the framework incorporates SVC and TCSC deviceby determining their optimal sizing and placement to reinforce the transmission lines and buses that demand the mosreactive-power support, thereby achieving a cost-effective trade-off between capital investment and operationaperformance. When validated on a large scale test system, this integrated solution enhances economic efficiency, reducenvironmental impact, and bolsters reliability under uncertainty. By combining advanced uncertainty quantificationcustomized metaheuristics, and targeted network reinforcement, this work provides a versatile, scalable methodology fplanning and operating resilient, low-carbon electrical grids.