Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

IF 9 1区工程技术 Q1 ENERGY & FUELS

Energy Pub Date : 2024-11-02 DOI:10.1016/j.energy.2024.133653

Saleh Al Dawsari , Fatih Anayi , Michael Packianather

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引用次数: 0

Abstract

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

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利用矮獴优化算法对混合可再生能源系统进行技术经济分析，以降低成本并提高可靠性

全球能源危机，尤其是偏远地区的能源危机，增加了人们对可再生能源（RES）的兴趣，以满足日益增长的能源需求。将可再生能源与储能系统集成在一起，为缓解波动和间歇性提供了一种前景广阔的解决方案，但成本和可靠性问题仍然令人担忧。本研究通过真实世界的气象和负荷需求数据，探讨了各种微电网配置的优化设计，将光伏（PV）、风力涡轮机（WT）、电池储能系统（BESS）和柴油发电机（DG）系统结合在一起，用于沙特阿拉伯的纳季兰市。矮獴优化算法（DMOA）与沙蜂群算法（SSA）和鲸鱼优化算法（WOA）一起被用于最大限度地降低平准化能源成本（LCOE），同时提高系统可靠性。结果表明，光伏/BESS 配置虽然具有成本效益（LCOE 为 0.038 美元/千瓦时），但无法满足可靠性约束，供电损失概率 (LPSP) 为 0.679。相比之下，光伏、风电、BESS 和 DG 配置的 LPSP 为 1.9 × 10^--8%，LCOE 为 0.199 美元/千瓦时，为该地区的能源需求提供了稳健可靠的解决方案。本文介绍了 DMOA 在优化混合可再生能源系统方面的新应用，证明了它在实现成本与可靠性之间的平衡方面的有效性。这一策略为面临能源挑战的类似地区提供了一种可行的可持续能源规划方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Energy 工程技术-能源与燃料

CiteScore

15.30

自引率

14.40%

发文量

审稿时长

14.2 weeks

期刊介绍： Energy is a multidisciplinary, international journal that publishes research and analysis in the field of energy engineering. Our aim is to become a leading peer-reviewed platform and a trusted source of information for energy-related topics. The journal covers a range of areas including mechanical engineering, thermal sciences, and energy analysis. We are particularly interested in research on energy modelling, prediction, integrated energy systems, planning, and management. Additionally, we welcome papers on energy conservation, efficiency, biomass and bioenergy, renewable energy, electricity supply and demand, energy storage, buildings, and economic and policy issues. These topics should align with our broader multidisciplinary focus.