Abstract:
This thesis develops an optimization methodology based on Ordinal Optimization (OO) and DC Optimal Power Flow (DC-OPF) to determine the optimal sizing and placement of energy sources in a distribution network. The primary goal is to ensure reliable, affordable, and sustainable electricity supply with a cost-effective solution. The system consists of three types of energy sources: photovoltaic (PV) generator, battery energy storage (BES), and diesel generators (DG). The methodology consists of three main phases, which begins by sampling the extensive large search space of PV sizes and battery capacities to a relatively small subset of combinations denoted by Θ_N. This subset is then evaluated using a simple model over a year to determine the levelized cost of electricity (LCOE) of each design in this subset. The evaluated designs are then sorted in ascending order of LCOE to select the ones with the most appropriate sizes of PV, battery, and diesel generators. The top-ranked designs are then assessed using an accurate model based on DC-OPF, implemented by the MATPOWER 7.1 tool, from which we deduce the LCOE over a year of these top-s designs. Finally, the optimal design that has the minimum LCOE is identified. The DC-OPF simulates the system's operation and allow us to obtain the best distribution of PV and battery resources over the network. To minimize the use of diesel fuel and control CO2 emissions, we included the carbon tax in the evaluation of the LCOE. The functionality and performance of the developed methodology is tested on a standard IEEE 5-busbar network and on a real case distribution network for Younine, a village located in the West Beqaa district, where data on power consumption trends, available space and solar radiation were acquired.
