基于CiteSpace的能源系统优化调度研究热点与趋势分析  

Research hotspots and trends analysis of energy systems optimal scheduling based on CiteSpace

作　　者：林俊光 冯彦皓 林小杰 吴凡 钟崴[2] 俞自涛[2,3] 叶建君 LIN Junguang;FENG Yanhao;LIN Xiaojie;WU Fan;ZHONG Wei;YU Zitao;YE Jianjun(Zhejiang Energy Group Research Institute Co.,Ltd.,Hangzhou 311100,China;Institute of Thermal Science and Power Systems,Zhejiang University,Hangzhou 310027,China;State Key Laboratory of Clean Energy Utilization,Zhejiang University,Hangzhou 310027,China;Zhejiang Zheneng Electric Power Co.,Ltd Xiaoshan Power Plant,Hangzhou 311251,China)

机构地区：[1]浙江浙能技术研究院有限公司,浙江杭州311100 [2]浙江大学热工与动力系统研究所,浙江杭州310027 [3]能源清洁利用国家重点实验室,浙江杭州310027 [4]浙江浙能电力股份有限公司萧山发电厂,浙江杭州311251

出　　处：《热力发电》2023年第8期1-12,共12页Thermal Power Generation

基　　金：浙江浙能技术研究院有限公司科技项目(No.ZNKJ-2019-087);国家重点研发计划项目(2019YFE0126000)。

摘　　要：能源系统优化调度对保证能源供需平衡有重要作用。对30余年国内外该领域发展状况、热点及趋势的量化对比,通过CiteSpace软件对1990—2022年CNKI和WoS数据库的能源系统优化调度进行分析。结果表明:该领域虽处于常规科学阶段但文献具有高增速,国内文献增速较国际快且机构间交流密切;国外热点为微电网的优化算法、不确定性和稳定性控制,综合能源系统的含储能动态调度,调度技术的强化学习和博弈论;国内热点趋势为微电网的算法-动态优化/双层优化/分时电价-多智能体-需求侧管理-深度学习及综合能源系统的不确定性-能源集线器-电转气-综合需求响应-数据驱动-碳交易、碳捕集和强化学习等。由分析结果可见,启发式算法和深度学习等在未来大规模能源系统下有望实现研究范式转变。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,an analysis of the CNKI and WoS from 1990-2022 through CiteSpace software was done.The results show that:the 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.

关 键 词：能源系统 优化调度 CiteSpace软件 文献计量学 研究趋势 

分 类 号：G353.1[文化科学—情报学] TK01[动力工程及工程热物理] TM73[电气工程—电力系统及自动化]