Planning and Energy Self-Supply Strategy for Distributed Photovoltaic Microgrids on Highways Considering Regional Layout Constraints

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Detalles Bibliográficos
Publicado en:	Processes vol. 13, no. 5 (2025), p. 1377
Autor principal:	Shi Ze
Otros Autores:	Wu, Hao, Xiao Tianxiang, Huang Xiliu, Long, Shao, Ma, Zhenyu, Cao Pulin
Publicado:	MDPI AG
Materias:	China Electrical loads Distributed generation Optimization Roads & highways Environmental conditions Solar power generation Mountainous areas Service areas Systems stability Energy storage Long short-term memory Energy utilization Photovoltaic cells Efficiency Optimization models Dormitories Alternative energy Sustainable development Load fluctuation Renewable resources Energy conservation Construction costs Industrial Internet of Things Layouts Regional planning Power supply Emissions Algorithms Seasonal variations Constraints Terrain Climate change
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	With the widespread adoption of highways in the mountainous regions of southwestern China, the electricity load of numerous tunnels and service areas has increased rapidly. Constructing photovoltaic (PV) microgrids in service areas has become an important means of energy conservation, consumption reduction, and carbon emission mitigation. However, constrained by mountainous terrain, the PV power generation conditions in highway service areas exhibit significant micro-terrain variations, making it difficult to effectively evaluate PV utilization efficiency. This paper proposes a dynamic block optimization model for PV microgrids that considers regional layout constraints. The model utilizes an intelligent adjustment mechanism to plan PV panel layouts in highway service areas, optimizing energy utilization efficiency and economic benefits. Additionally, long short-term memory (LSTM) networks are employed for short-term PV output prediction to address the challenges posed by varying weather and seasonal changes. This approach comprehensively considers the intermittency and instability of PV power generation, enabling dynamic block optimization to autonomously adjust the PV power output in response to load fluctuations. Through simulation case studies, the model is validated to effectively improve the utilization rate and economic performance of PV microgrids under various environmental conditions and demonstrates superior performance compared with traditional static block methods.
ISSN:	2227-9717
DOI:	10.3390/pr13051377
Fuente:	Materials Science Database