Load management as a smart grid concept for sizing and designing of hybrid renewable energy systems

TLDR

Applying smart grid concepts such as load management can reduce the size of HRES components and reduce the cost of generated energy considerably. The study aims to optimally size hybrid renewable energy systems for remote areas by dividing the load into high‑ and low‑priority parts to achieve highest reliability and lowest cost. The authors model a system comprising photovoltaic arrays, wind turbines, batteries, fuel cells, and a diesel generator, and use a smart particle swarm optimization algorithm in MATLAB to determine optimal component sizes, comparing results with and without load division and validating them with HOMER simulations. The results show that the percentage of load division is inversely proportional to the cost of generated energy.

Abstract

Optimal sizing of hybrid renewable energy systems (HRES) to satisfy load requirements with the highest reliability and lowest cost is a crucial step in building HRESs to supply electricity to remote areas. Applying smart grid concepts such as load management can reduce the size of HRES components and reduce the cost of generated energy considerably. In this article, sizing of HRES is carried out by dividing the load into high- and low-priority parts. The proposed system is formed by a photovoltaic array, wind turbines, batteries, fuel cells and a diesel generator as a back-up energy source. A smart particle swarm optimization (PSO) algorithm using MATLAB is introduced to determine the optimal size of the HRES. The simulation was carried out with and without division of the load to compare these concepts. HOMER software was also used to simulate the proposed system without dividing the loads to verify the results obtained from the proposed PSO algorithm. The results show that the percentage of division of the load is inversely proportional to the cost of the generated energy.