用于稳定电力系统的光伏和沼气混合系统

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引用次数: 0

摘要

应对气候变化的紧迫性凸显了实施新能源政策的必要性。最近,越来越多的国家和公司开始探索替代能源,以取代化石燃料。个人对光伏太阳能电池板这种可持续发电方式的兴趣日益高涨。另一方面,光伏太阳能系统在很大程度上依赖于天气条件。因此,光伏系统产生的电力并不可靠。利用沼气发电可能是一种极具吸引力的替代方案。本研究提出了一个结合光伏太阳能电池板和沼气的混合发电系统方案。该系统以光伏太阳能系统为主。利用先进的机器学习技术计算出光伏发电功率的预测值。随后,将预测功率与必要负荷的近似值并列。如果光伏系统无法提供必要的电力需求,建议采用沼气系统,以实现稳定可靠的电力供应。此外,这种方法还能预测每天的废物需求量,从而为电网提供可靠的电力供应。利用建议的沼气容量和废物量计算方案,对冬季的大容量生产进行了测试。该研究引入了数学公式来解决系统的每日生物量需求,并实施了一个自动控制系统来监督运行。利用建议的方程和控制流程图方法,可有效促进对牛粪产生的甲烷进行精确量化,将误差率从 12.82% 降低到 8.28%。此外,它还能及时调整以优化设备性能。这种方法有助于工程师简化评估,并为今后改进设计参数奠定基础。
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Hybrid photovoltaic and biogas system for stable power system

The urgency of addressing climate change has underscored the necessity for implementing a new energy policy. Lately, an increasing number of countries and corporations have initiated the exploration of alternative energy sources to replace fossil fuels. There is a rising interest among individuals in photovoltaic solar panels as a sustainable means of generating electricity. Photovoltaic solar systems, on the other hand, rely significantly on weather conditions. As a result, the electricity generated by photovoltaic (PV) systems is unreliable. Harnessing biogas might serve as a captivating alternative for generating electricity. The study presents a proposal for a hybrid power system that combines PV solar panels and biogas. This system regards the PV solar system as the primary system. A forecast of PV production power is calculated using advanced machine-learning techniques. Subsequently, the projected power is juxtaposed with an approximation of the necessary load. If the PV system is unable to provide the necessary power demand, it is advisable to employ a biogas system to achieve a consistent and reliable power supply. Furthermore, this method offers a forecast of the daily waste demand for a reliable electrical grid. High-capacity manufacturing during the winter season is tested using a proposed solution for calculating biogas capacity and waste amount. The study introduces mathematical equations to address the daily biomass requirements of the system and implements an automated control system to oversee operations. The utilization of the proposed equations and control flow chart methodology effectively facilitates the precise quantification of methane generated from beef manure, reducing the margin of error from 12.82% to 8.28%. Additionally, it enables prompt adjustments to optimize equipment performance. This approach assists engineers in streamlining assessments and lays the groundwork for future improvements in design parameters.

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