With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.

中文翻译：

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基于神经模糊的并网微电网模型预测能源管理

随着人口的不断增长和技术使用的增加，对电能的需求已大大增加。以化石燃料为基础的发电量增加对环境造成严重影响。结果，人们对可再生资源的兴趣增加了，因为它们对环境友好，从长远来看可能是经济的。但是，可再生能源的间歇性是主要缺点。重要的是将它们与网格的其余部分集成在一起，以便可以从中受益，同时可以减轻其负面影响。在本文中，建议将能量管理算法用于由负载，光伏（PV）系统和电池组成的并网微电网，以有效利用能量。使用从日光照射和同一区域的温度估算获得的PV功率和消耗估算，开发了一种基于模型预测控制的能源管理方法。通过确定系统的可能工作状态，设计了基于神经模糊推理系统的能量管理算法。将该系统与基于规则的控制策略进行了比较。结果表明，所开发的控制算法可确保为微电网提供可靠的能量，同时最大程度地提高了可再生能源的利用率。通过确定系统的可能运行状态来设计。将该系统与基于规则的控制策略进行了比较。结果表明，所开发的控制算法可确保为微电网提供可靠的能量，同时最大程度地提高了可再生能源的利用率。通过确定系统的可能运行状态来设计。将该系统与基于规则的控制策略进行了比较。结果表明，所开发的控制算法可确保为微电网提供可靠的能量，同时最大程度地提高了可再生能源的利用率。