优化混合能源系统中的风能和太阳能集成,增强可持续性

Energy Storage Pub Date : 2024-11-18 DOI:10.1002/est2.70096
Amir Hossein Forghani, Alireza Arab Solghar, Hassan Hajabdollahi
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引用次数: 0

摘要

混合能源系统由作为原动机的柴油发动机、电制冷机和吸收式制冷机、备用锅炉以及通过热蒸汽压缩的多效蒸馏(MED-TVC)装置组成,已被用于满足一个住宅区的要求。本研究的重点是重新设计和优化该系统,以改善环境条件,减少污染物,并最大限度地减少化石能源的使用。研究考察了可再生能源系统的可行性和设计,包括风力涡轮机 (WT)、光电板 (PV) 和平板集热器 (FPC)。该系统采用遗传算法(GA)进行优化。混合系统采用了 21 个设计变量,其中 24 个设计变量与可再生能源一起用于优化。年度总成本(TAC)包括投资、运营和污染排放罚款,被选为最小化的目标函数。结果表明,在伊朗霍尔木兹甘省的 Bandar Abbas,使用风力发电机并不符合成本效益,而太阳能可以提高系统的性能。在使用组合系统的情况下,目标函数值为 2.0472 × 106 美元/年,而在使用可再生能源的情况下,目标函数值为 1.6795 × 106 美元/年。因此,拟议的组合式可再生能源系统将目标函数值降低了 17.96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Wind and Solar Integration in a Hybrid Energy System for Enhanced Sustainability

A hybrid energy system, comprising a diesel engine as the prime mover, electrical and absorption chillers, a backup boiler, and a multi-effect distillation through thermal vapor compression (MED-TVC) unit, has been utilized to meet the requirements of a residential complex. This study focuses on redesigning and optimizing the system to enhance environmental conditions, reduce pollutants, and minimize the use of fossil energy. The feasibility and design of renewable energy systems, including wind turbines (WTs), photovoltaic panels (PVs), and flat plate collectors (FPCs), have been examined. Genetic algorithm (GA) has been employed for optimization. The hybrid system employs 21 design variables, with 24 design variables chosen for optimization alongside renewable energies. The total annual cost (TAC), encompassing investment, operation, and pollution emission fines, has been chosen as the objective function for minimization. The results indicate that the use of WTs has not been cost-effective, and solar energy can enhance the system's performance in Bandar Abbas, Hormozgan province in Iran. In the case of using a combined system, the objective function value was 2.0472 × 106 $/year, and when using renewable energies, the objective function became 1.6795 × 106 $/year. Thus, the proposed combined-renewable system has reduced the objective function by 17.96%.

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