Abstract
This study investigates the use of division algorithms to optimize the size of a desalination system integrated with a microgrid based on a wind turbine plant and the battery storage to supply freshwater based on cost, reliability, and energy losses. Cumulative exergy demand is used to identify and minimize the energy losses in the optimized system. Division algorithms are used to overcome the drawback of low convergence speed encountered by the well-known method genetic algorithm. The findings indicated that there is a positive relationship between cost, cumulative exergy, and reliability. More specifically, when the loss of power supply probability is 10%, compared to when it is 0%, the total cumulative exergy demand and total life cycle cost are reduced by 34.76% when the battery is full and 45.44% when the battery is empty and there is a 44.43% decrease in total life cycle cost, respectively. However, the more reliable system, the less exergy is lost during the production of 1 m3 freshwater by desalination integrated into wind turbine plant.
Highlights
Loss of power supply probability (LPSP) is introducedas asaa critical parameter to determine the reliability of a power-generating system
The results indicate that exergy removal from nature in freshwater production by Seawater Reverse Osmosis Desalination (SWROD)/Wind Turbine (WT)/Battery Bank Storage (BBS) is primarily in the form of “renewable, kinetic” in the range of 5.56–9.46 MJ/functional unit (FU) and followed by “non-renewable, fossil” in the range of 1.8–2.17
Electricity is usually generated by fossil fuels, whose adverse effects the environment are well known
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Low CExD, and cost-effective system based on renewable energy resources, it is required to optimize the size of the system. Kiehbadroudinezhad et al [26] developed a novel algorithm named division algorithm (DA) to optimize reliable and cost-effective desalination based on renewable energy resources. They claimed that in solving problems, DA is flexible, simple, precise, and fast compared to GA. Model, and optimize desalination integrated with renewable energy in terms of size, reliability, and cost, but the CExD of systems is not still widely discussed. In the fifth section, the optimization results will be presented and discussed
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