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Research hotspots and trends analysis of energy systems optimal scheduling based on CiteSpace
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Junguang LIN1, 2, Yanhao FENG2, Xiaojie LIN2, Fan WU1, 2, Wei ZHONG2, Zitao YU2, 3, Jianjun YE4
Thermal Power Generation | 2023, 52(8) : 1 - 12
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Thermal Power Generation | 2023, 52(8): 1-12
• Technical and economic review •
Research hotspots and trends analysis of energy systems optimal scheduling based on CiteSpace
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Junguang LIN1, 2, Yanhao FENG2, Xiaojie LIN2, Fan WU1, 2, Wei ZHONG2, Zitao YU2, 3, Jianjun YE4
Affiliations
  • 1.Zhejiang Energy Group Research Institute Co., Ltd., Hangzhou 311100, China
  • 2.Institute of Thermal Science and Power Systems, Zhejiang University, Hangzhou 310027, China
  • 3.State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
  • 4.Zhejiang Zheneng Electric Power Co., Ltd., Xiaoshan Power Plant, Hangzhou 311251, China
Published: 2023-08-25 doi: 10.19666/j.rlfd.202212265
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The optimal scheduling of energy systems is important in ensuring the balance of energy supply and demand. Quantitative comparative studies of the development status, hot spots and trends in this field at home and abroad for more than 30 years, the research of energy systems optimal scheduling was analyzed in the CNKI and WoS databases from 1990—2022 by CiteSpace software. The results show that this field is in the conventional scientific stage but the literature has a high growth rate, the domestic literature growth rate is faster than the international and the inter-institutional exchange is close; the foreign hotspots are optimization algorithms, uncertainty and stability control for microgrids, dynamic scheduling with energy storage for integrated energy system (IES), as well as reinforcement learning and game theory for scheduling technique; the domestic hotspot trends are algorithms, dynamic optimization/bilayer optimization/time-sharing tariffs, multi-intelligent bodies, demand side management and deep learning for microgrids, as well as uncertainty, energy hubs, electricity to gas, integrated demand response, data driven, carbon trading, carbon capture and reinforcement learning for IES. The results show heuristic algorithms and deep learning techniques are expected to achieve a paradigm shift in future large-scale energy systems.

energy system  /  optimal scheduling  /  CiteSpace software  /  bibliometrics  /  research trend
Junguang LIN, Yanhao FENG, Xiaojie LIN, Fan WU, Wei ZHONG, Zitao YU, Jianjun YE. Research hotspots and trends analysis of energy systems optimal scheduling based on CiteSpace[J]. Thermal Power Generation, 2023 , 52 (8) : 1 -12 . DOI: 10.19666/j.rlfd.202212265
Funding
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  • Research Project of Zhejiang Energy R&D Institute Co., Ltd.(ZNKJ-2019-087)
  • National Key Research and Development Program(2019YFE0126000)
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Year 2023 volume 52 Issue 8
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Article Info
doi: 10.19666/j.rlfd.202212265
  • Online Date：2026-01-26
  • Published：2023-08-25
Article Data
Affiliations
History
  • Revised：2022-12-12
Funding
Research Project of Zhejiang Energy R&D Institute Co., Ltd.(ZNKJ-2019-087)
National Key Research and Development Program(2019YFE0126000)
Affiliations
    1.Zhejiang Energy Group Research Institute Co., Ltd., Hangzhou 311100, China
    2.Institute of Thermal Science and Power Systems, Zhejiang University, Hangzhou 310027, China
    3.State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
    4.Zhejiang Zheneng Electric Power Co., Ltd., Xiaoshan Power Plant, Hangzhou 311251, China
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