Collaborative Optimization of PV Greenhouses and Clean Energy Systems in Rural Areas

• Published in: IEEE transactions on sustainable energy Vol. 14; no. 1; pp. 1 - 15
• Main Authors: Fu, Xueqian, Zhou, Yazhong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agricultural production; agrivoltaic system; Atmospheric models; Carbon; carbon neutralization; Clean energy; Collaboration; Coordination models; Energy; Energy costs; Farm buildings; Greenhouses; Integrated energy systems; Modernization; Optimization; Photosynthesis; photovoltaic; Photovoltaic cells; Photovoltaics; Regeneration; Rural areas; Rural development; rural energy system; Thermal energy; China
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2022.3223684

As an important infrastructure supporting rural development, an integrated energy system plays an irreplaceable role in China's rural revitalization strategy. The deployment of rural energy projects is an effective way for rural areas to achieve double carbon goals and accelerate agricultural modernization. Based on the actual rural energy systems in northern China, this paper takes the rural energy system with photovoltaic greenhouses as the research object. Both the agrometeorological and energy meteorological models are established considering the meteorological sensitivity of agricultural production and photovoltaic generation. We propose a novel method for optimizing the collaboration between photovoltaic greenhouse load control and rural energy systems. The combined coordination model of agriculture and energy networks is established, and the combined model involves carbon, electrical energy, and thermal energy. Supplemental greenhouse lighting and greenhouse heating consume most of the energy and are finely modeled with focused attention on photosynthesis. Finally, a real-world 47-bus distribution network and three photovoltaic greenhouses in northern China are simulated as an analytical example. The simulation results showed that by using the proposed optimization method, a 3996 m 2 greenhouse with a 25% photovoltaic coverage ratio can save 15% on energy costs.