使用蜜獾算法的高效能源管理系统用于智能农业

Samuel Omaji, Glory Nosawaru Edegbe, J. Ogbiti, Esosa Enoyoze, Ijegwa David Acheme
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引用次数: 0

摘要

如今,优化对于解决能源危机至关重要,尤其是在智能家居中。然而,全球现有的基于优化的智能农业能源管理方法还需要进一步改进,这也是本研究的动机所在。为解决这一问题,需要一个高效的农场能源管理系统。因此,本研究采用蜜獾优化算法,提出了一种用于智能农业的农场能源管理系统(FEMS)。在所提出的系统中,制定了一个多目标优化问题,以找到实现一系列目标的最佳解决方案,如电费、负荷最小化和峰均比最小化,同时考虑农民的舒适度。所提出的系统考虑了商业化农业与可再生能源(RES)的整合。此外,针对电力市场中的实时定价(RTP)和使用时间定价(ToU)方案,拟议系统通过农用设备的调度最大限度地降低了负荷消耗和电费成本。在 MATLAB 2018A 中进行了大量实验,以确定拟议系统的功效。拟议的 FEMS 由十六种农用设备组成,包括可再生能源在内,并具有各自的额定功率。仿真结果表明,在考虑 ToU 市场价格时,无 FEMS 系统的电费成本高达 50.69%,而不含可再生能源的 FEMS 系统的电费成本为 43.04%,含可再生能源的 FEMS 系统的电费成本为 6.27%。就 RTP 市场价格而言,无 FEMS 系统的电力成本为 42.30%,而无可再生能源的 FEMS 系统为 30.64%,有可再生能源的 FEMS 系统为 27.24%。此外,在考虑 ToU 市场价格时,无 FEMS 系统的最大负荷消耗为 246.80 千瓦,而无可再生能源的 FEMS 系统为 151.40 千瓦,有可再生能源的 FEMS 系统为 18.85 千瓦。此外,在 RTP 市场价格下,无 FEMS 系统的最大负荷消耗为 246.80 千瓦,而无可再生能源的 FEMS 系统为 186.40 千瓦,有可再生能源的 FEMS 系统为 90.68 千瓦。这项研究的意义在于提出一种基于蜜獾优化算法的概念化 FEMS。所提出的系统可提供农用设备调度,减轻电网负担,对大型和小型农户来说都具有成本效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture
Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.
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