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.

中文翻译：

---

考虑可再生能源和电动汽车的 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基于一对一的优化器，作为并网微电网的新型能源管理方法，以解决环境和经济问题。建议的方法的特点是其强大的探索功能，使该技术能够到达全局解决方案并避免本地解决方案，并且易于部署。正在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“N 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，同时使用 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景以及电动汽车的充电状态。除了灰狼优化器、Runge Kutta 优化器、鲈鱼群优化算法、河马优化算法和基于 Newton Raphson 的优化器之外，还与已发布的磷虾群优化器以及其他编程算法进行了全面比较。此外，通过使用 Kruskal Wallis、Friedman、ANOVA 和 Wilcoxon 秩检验，对建议的方法进行了统计验证。在可再生资源正常工作的情况下，推荐的策略在运营成本节省和减排方面优于已发布的磷虾群优化器，分别为 53.85% 和 46.62%。而在可再生资源的额定运行期间，净节省和减排分别为 10.14% 和 38.91%。 此外，与已发布的方法相比，在智能充电模式下连接电动汽车时，最大节省的成本为 55.69%。建议的策略可以推荐为管理微电网能源的有效方法。