Results in Engineering (Mar 2024)
Simulation of electrical energy supply required by maad koush pelletizing complex using renewable energy sources and simulation with homer energy software
Abstract
Undoubtedly, in today's world, energy is one of the most vital and limited resources that mankind seeks to create and use. Therefore, optimization of energy consumption or energy supply from different sources is one of the most fundamental concerns of countries and societies. In this research, it has been tried to find the optimized solution from different sources (Hybrid Energy System) to supply required electric energy in a steel industrial unit. As we know, steel is one of the main energy industries in the country. In this research, the energy supply of Mad Kosh pelletizing complex has been addressed by optimizing and checking the available resources using Homer Energy software, as we know that Homer Energy software is one of the most widely used software in the field of energy simulation and model. In this article, the required load in the complex is extracted in the form of a load curve. Next, based on the region and geographical location where the pelletizing complex is located, information on temperature, wind, etc. Has been extracted (the closest region to the Bastak Bandar Abbas complex) and finally five scenarios has been defined for the simulation, and these 5 scenarios have been examined (use of solar energy, use of wind energy, use of diesel generator energy, use of hybrid energy system, use of hybrid energy system with the priority of using generator) is important in this article. The optimization is based on all the scenarios that have the possibility of being present in this energy supply, which are defined in Knight according to the location of this manufacturing plant in the geographical area, and based on the amount of cost and the amount of consumption, the most optimal real state is proposed, which seems Due to the comprehensiveness of the review in all suppliers, it will give us a reliable point of optimization so that we can finally conclude the most optimal and least expensive scenario.
