Multi-Type Building Integrated Agricultural Microgrid Planning Method Driven by Data Mechanism Fusion

Nan Wei (), Zhi An, Qichao Chen, Zun Guo, Yichuan Fu, Yingliang Guo and Chenyang Li ()
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Nan Wei: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Zhi An: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Qichao Chen: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Zun Guo: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Yichuan Fu: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Yingliang Guo: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Chenyang Li: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China

Energies, 2025, vol. 18, issue 18, 1-18

Abstract: With the integration of numerous distributed energy resources (DERs) and buildings with diverse energy demands, the inherent vulnerability of agricultural microgrids poses escalating security threats. Harnessing the regulatory capabilities of diverse building loads and energy storage systems to mitigate voltage excursions caused by DER generation in microgrids is of significant importance. Therefore, a data mechanism fusion-driven microgrid planning method is proposed in this paper, aiming to enhance the security of microgrids and optimize the utilization of DERs. A comprehensive agricultural microgrid model that incorporates intricate constraints of various types of buildings is established, including greenhouses, refrigeration houses and residences. Based on this model, a site selection and capacity determination planning methodology is proposed, taking into account wind turbines (WTs), photovoltaics (PVs), electric boilers (EBs), battery energy storage systems (BESSs), and heat storage devices. To address the limitations of traditional greenhouse models in accurately predicting indoor temperatures, a temperature field prediction method for greenhouses is proposed by leveraging a generalized regression neural network (GRNN) to train and modify the model indicators. Case studies based on a modified IEEE 33-bus system verified the effectiveness and rationality of the proposed method.

Keywords: agricultural microgrid; building; microgrid planning; temperature field prediction; GRNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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