Intelligent Optimization and Scheduling Method for Distributed Energy Access to Rural Power Grid Based on Deep Learning

Zhaojing Wang, Junxi Wang, Lei Song, Chun Shi, Yao Fu, Zhenyi Wang · 2024

Summary. This paper develops a deep learning method to optimize how distributed renewable energy sources like solar and wind integrate into rural power grids. The approach uses machine learning to predict rural electricity demand and schedules energy storage to minimize operating costs while managing the unpredictable output from weather-dependent sources. Results show the method increases power density, maintains higher active power levels, and improves energy storage efficiency.

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Wang, Z., Wang, J., Song, L., Shi, C., Fu, Y., & Wang, Z.. (2024). Intelligent Optimization and Scheduling Method for Distributed Energy Access to Rural Power Grid Based on Deep Learning. https://doi.org/10.1109/ifeea64237.2024.10878528

@article{wang-2024-intelligent-optimization-scheduling-method-distributed,
  title   = {Intelligent Optimization and Scheduling Method for Distributed Energy Access to Rural Power Grid Based on Deep Learning},
  author  = {Zhaojing Wang and Junxi Wang and Lei Song and Chun Shi and Yao Fu and Zhenyi Wang},
  year    = {2024},
  doi     = {10.1109/ifeea64237.2024.10878528},
  url     = {https://doi.org/10.1109/ifeea64237.2024.10878528}
}

TY  - JOUR
TI  - Intelligent Optimization and Scheduling Method for Distributed Energy Access to Rural Power Grid Based on Deep Learning
AU  - Zhaojing Wang
AU  - Junxi Wang
AU  - Lei Song
AU  - Chun Shi
AU  - Yao Fu
AU  - Zhenyi Wang
PY  - 2024
DO  - 10.1109/ifeea64237.2024.10878528
UR  - https://doi.org/10.1109/ifeea64237.2024.10878528
ER  -

Details

DOI: 10.1109/ifeea64237.2024.10878528
Countries: China
Regions: Asia
Categories: energy, agtech, general-innovation
Added: 2026-06-01