Sizing Optimization And Design Of An Autonomous Ac Microgrid For Commercial Loads Using Harris Hawks Optimization Algorithm

In this study, the sizing optimization and design of an autonomous AC microgrid is performed using the Harris Hawks Optimization (HHO) algorithm. The objective is to demonstrate that more economical and reliable microgrids can be designed using the HHO algorithm. For this purpose, an autonomous AC grid is designed for the Gazi University Technology Park building located in Ankara. A yearlong energy consumption of the building and meteorological data have been measured and recorded. Based on the data, a microgrid structure consisting of photovoltaics (PV), wind turbines (WT), battery energy storage systems (BESS), and diesel generators (DG) is proposed. Finding the optimum capacities of these components is crucial to achieve the low cost and reliability objectives with true autonomous characteristics. On one hand oversizing that may arise from random assessments without any sizing optimization will increase the investment, operation, and maintenance costs. On the other hand, insufficient capacity planning based on prompt decisions can end up with problems in maintaining the energy continuity. Therefore, sizing optimization is a requirement for an effective microgrid design. In this context, after the sizing problem is solved using the HHO algorithm, the results are used in the design of the proposed microgrid. The simulation of the designed microgrid in MATLAB has demonstrated a successful performance providing savings ranging from 1.18% to 18.23% in the total net present cost (TNPC) value compared to other algorithms. The performance of the HHO algorithm is compared with four state-of-the-art metaheuristic algorithms, namely the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), the Gray Wolf Optimization (GWO), and the Salp Swarm Algorithm (SSA). Consequently, it is shown that the HHO algorithm has a competitive performance in design of cost effective and reliable microgrids.