Efficient framework for energy management of microgrid installed in Aljouf region considering renewable energy and electric vehicles

IF 9.9 1区 工程技术 Q1 ENERGY & FUELS Energy Conversion and Management Pub Date : 2024-11-06 DOI:10.1016/j.enconman.2024.119212
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Abstract

This paper proposes an efficient one-to-one-based optimizer as a new energy management method for a grid-connected microgrid in order to address both environmental and economic concerns. The suggested approach is distinguished by its robust exploration capabilities that allow the technique to reach the global solution and avoid local ones, along with its ease of deployment. The microgrid under consideration consists of conventional resources, microturbine, fuel cell, storage batteries, and electric vehicles, as well as renewable energy sources like photovoltaic and wind turbine. Real-time 24-hour solar irradiance, wind speed, and air temperature data of Sakaka, Aljouf region in Saudi Arabia located at 29° 58′ 15.13″N latitude and 40° 12′ 18.03″E longitude are utilized while the stochastic natures of renewable resources have been modeled using Beta and Weibull probability distribution functions. Various scenarios of renewable resources’ generations as well as electric vehicle’s charging states are analyzed. A thorough comparison is made with the published krill herd optimizer, in addition to other programmed algorithms such as grey wolf optimizer, Runge Kutta optimization, salp swarm algorithm, hippopotamus optimization algorithm, and Newton Raphson based optimizer. Also, the suggested approach is validated statistically through the use of Kruskal Wallis, Friedman, ANOVA, and Wilcoxon rank tests. With renewable resources working normally, the recommended strategy outperformed the published krill herd optimizer in terms of operating cost savings and emission reductions, which were 53.85 % and 46.62 %, respectively. While during the rated operation of renewable resources, the net savings and emission reductions were 10.14 % and 38.91 %, respectively. Additionally, the greatest cost savings during connecting electric vehicles at smart charging mode was 55.69 % as compared to the published approach. The suggested strategy can be recommended as an effective method for managing microgrid energy.
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考虑到可再生能源和电动汽车,在 Aljouf 地区安装的微电网能源管理高效框架
本文提出了一种高效的一对一优化器,作为并网型微电网的一种新的能源管理方法,以解决环境和经济问题。所建议的方法具有强大的探索能力,能够找到全局解决方案,避免局部解决方案,而且易于部署。所考虑的微电网包括常规资源、微型涡轮机、燃料电池、蓄电池和电动汽车,以及光伏和风力涡轮机等可再生能源。利用位于北纬 29° 58′ 15.13″ 和东经 40° 12′ 18.03″ 的沙特阿拉伯 Aljouf 地区 Sakaka 的 24 小时实时太阳辐照度、风速和气温数据,并使用 Beta 和 Weibull 概率分布函数对可再生资源的随机性质进行建模。分析了可再生资源发电的各种情况以及电动汽车的充电状态。除了灰狼优化算法、Runge Kutta 优化算法、salp swarm 算法、河马优化算法和基于牛顿-拉斐逊的优化算法等其他编程算法外,还与已发布的磷虾群优化算法进行了全面比较。此外,还通过使用 Kruskal Wallis、Friedman、方差分析和 Wilcoxon 秩检验对建议的方法进行了统计验证。在可再生资源正常运行的情况下,推荐的策略在运营成本节约和减排量方面优于已发布的磷虾群优化器,分别为 53.85 % 和 46.62 %。而在可再生资源额定运行期间,净节约率和减排率分别为 10.14 % 和 38.91 %。此外,与已公布的方法相比,在智能充电模式下连接电动汽车时可节省 55.69% 的成本。建议的策略可作为微电网能源管理的有效方法加以推荐。
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来源期刊
Energy Conversion and Management
Energy Conversion and Management 工程技术-力学
CiteScore
19.00
自引率
11.50%
发文量
1304
审稿时长
17 days
期刊介绍: The journal Energy Conversion and Management provides a forum for publishing original contributions and comprehensive technical review articles of interdisciplinary and original research on all important energy topics. The topics considered include energy generation, utilization, conversion, storage, transmission, conservation, management and sustainability. These topics typically involve various types of energy such as mechanical, thermal, nuclear, chemical, electromagnetic, magnetic and electric. These energy types cover all known energy resources, including renewable resources (e.g., solar, bio, hydro, wind, geothermal and ocean energy), fossil fuels and nuclear resources.
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