Planning and optimization of the renewable-energy-based micro-grid for rural electrification.

This research provides a basis for similar implementation projects for more operational and cost-effective performance of a renewable energy-based hybrid microgrid system. The renewable energies explored during this project were Photovoltaic (PV), Wind, and Battery Energy Storage System (BESS). The hybrid renewable energy optimization findings of this project are crucial since they respond to the problems and needs that many digitized power system networks have addressed over the recent years.Microgrid design and optimization with renewable energies for rural electrification is an interesting aspect of power system design, implementation, and evaluation with cost-reducing intentions. Therefore, it increases the renewable share and the job market demand but decreases the carbon footprint. Microgrid design and optimization with renewable energies for rural electrification is of great significance.The furtherance of this approach is achieved through the application of optimization techniques based on classical and heuristic algorithms. Subsequently, the thesis has applied classical optimization methods, namely Linear Programming (LP), as well as heuristic optimization methods, namely the Dynamic Arithmetic Optimization Algorithm (DAOA), and Grey Wolf Optimization (GWO) algorithm. The development of procedures for implementing the steps of these algorithms is reported in the thesis. MATLAB is used to validate the developed optimization methods and to analyze the simulation results. The optimization techniques were employed to minimize total system operating cost while ensuring reliability and energy balance within the microgrids.The thesis systematically formulates the daily operational and maintenance costs. The optimized models were validated using realistic hybrid renewable energy and load demand data to analyze system performance for the developed DAOA, LP, and GWO optimization methods