基于知识图谱的国内外电力市场研究综述

doi:10.19595/j.cnki.1000-6753.tces.210611

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (5439 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The deepening reform of electricity market is at a critical stage in China. At present, a large amount of research has been accumulated at home and abroad, the corresponding systematic analysis is necessary. At the same time, new research topics continue to emerge, making it difficult for tracking research frontier. The knowledge graph can be applied to extract a structured knowledge pedigree from massive literature data, and its visual graphics can show the evolution process and network structure of the field, so it can provide a new way for the evolution trajectory and hot spots tracking of power market research. Based on the knowledge map analysis of 1495 domestic and 5106 foreign electricity market-related literature in the past ten years, this paper refined domestic and foreign research trajectories and academic hot spots by constructing representative research data sets in the market field, and synthesized timelines and co-word network maps with visualization. The evolutionary path of domestic and foreign power market research in the past ten years has been explored. The core frontiers of the power market in the past five years are analyzed, the key points of the map with emergence and intermediary centrality are measured, and the research of the power market in all aspects and multiple dimensions was tracked. The proposed approach of knowledge graph provides scholars with an efficient and fast-applied literature analysis method, which has broad application prospects.

Key words： Knowledge graph electricity market evolution trajectory hot spots dynamic tracking

Received: 28 April 2021

PACS:

F407.61

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Bian Xiaoyan
	Zhang Luyao
	Zhou Bo
	Xu Bo
	Lin Shunfu

Cite this article:

Bian Xiaoyan,Zhang Luyao,Zhou Bo等. Review on Domestic and International Electricity Market Research Based on Knowledge Graph[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2777-2788.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210611 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I11/2777

[1] 曾鹏骁, 孙瑜, 季天瑶, 等. 金融输电权市场的收入充裕度问题研究及其对我国的启示[J]. 电网技术, 2021, 45(9): 3367-3380.
Zeng Pengxiao, Sun Yu, Ji Tianyao, et al.Research on revenue adequacy of financial transmission rights market and its enlightenment to China[J]. Power System Technology, 2021, 45(9): 3367-3380.
[2] 国家发展改革委关于完善风电上网电价政策的通知(发改价格〔2019〕882号)[EB/OL]. 北京: 国家发展改革委, 2019[2019-5-21].
https://www.ndrc.gov.cn/xxgk/zcfb/tz/201905/t20190524_962453.html
[3] 樊宇琦, 丁涛, 孙瑜歌, 等. 国内外促进可再生能源消纳的电力现货市场发展综述与思考[J]. 中国电机工程学报, 2021, 41(5): 1729-1752.
Fan Yuqi, Ding Tao, Sun Yuge, et al.Review and cogitation for worldwide spot market development to promote renewable energy accommodation[J]. Proceedings of the CSEE, 2021, 41(5): 1729-1752.
[4] 丁一, 谢开, 庞博, 等. 中国特色、全国统一的电力市场关键问题研究(1): 国外市场启示、比对与建议[J]. 电网技术, 2020, 44(7): 2401-2410.
Ding Yi, Xie Kai, Pang Bo, et al.Research on key issues of a unified national power market with chinese characteristics (1): enlightenment, comparison and suggestions from foreign markets[J]. Power System Technology, 2020, 44(7): 2401-2410.
[5] 宋永华, 包铭磊, 丁一, 等. 新电改下我国电力现货市场建设关键要点综述及相关建议[J]. 中国电机工程学报, 2020, 40(10): 3172-3187.
Song Yonghua, Bao Minglei, Ding Yi, et al.Summary of the key points of my country's power spot market construction under the new electricity reform and relevant suggestions[J]. Proceedings of the CSEE, 2020, 40(10): 3172-3187.
[6] 肖云鹏, 王锡凡, 王秀丽, 等. 面向高比例可再生能源的电力市场研究综述[J]. 中国电机工程学报, 2018, 38(3): 663-674.
Xiao Yunpeng, Wang Xifan, Wang Xiuli, et al.Summary of research on electricity market facing high proportion of renewable energy[J]. Proceedings of the CSEE, 2018, 38(3): 663-674.
[7] 陈悦, 陈超美, 刘则渊, 等. CiteSpace知识图谱的方法论功能[J]. 科学学研究, 2015, 33(2): 242-253.
Chen Yue, Chen Chaomei, Liu Zeyuan, et al.Methodological function of CiteSpace knowledge graph[J]. Studies in Science of Science, 2015, 33(2): 242-253.
[8] 侯海燕. 基于知识图谱的科学计量学进展研究[D]. 大连: 大连理工大学, 2006.
[9] Newman M, Girvan M.Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 423-433.
[10] 舒万里. 中文领域本体学习中概念和关系抽取的研究[D]. 重庆: 重庆大学, 2012.
[11] Kleinberg J.Bursty and hierarchical structure in streams[J]. Data Mining and Knowledge Discovery, 2003, 7(4): 91-101.
[12] 李杰, 陈超美. CiteSpace: 科技文本挖掘及可视化[M]. 北京: 首都经济贸易大学出版社, 2016.
[13] Egghe L.Theory and practise of the g-index[J]. Scientometrics, 2006, 69(1): 131-152.
[14] 王宣元, 刘敦楠, 刘蓁, 等. 泛在电力物联网下虚拟电厂运营机制及关键技术[J]. 电网技术, 2019, 43(9): 3175-3183.
Wang Xuanyuan, Liu Dunnan, Liu Zhen, et al.Operation mechanism and key technologies of virtual power plant under ubiquitous internet of things[J]. Power System Technology, 2019, 43(9): 3175-3183.
[15] 赵昊天, 王彬, 潘昭光, 等. 支撑云-群-端协同调度的多能园区虚拟电厂:研发与应用[J]. 电力系统自动化, 2021, 45(5): 111-121.
Zhao Tianhao, Wang Bin, Ban Shaoguang, et al.Research and application of park-level multi-energy virtual power plants supporting cloud-cluster-end multi-level synergetic dispatch[J]. Automation of Electric Power Systems, 2021, 45(5): 111-121.
[16] 吉斌, 朱敏健, 张怀宇, 等. 基于区块链技术的电力交易流程建模研究[J]. 电气技术, 2020, 21(6): 26-34.
Ji Bing, Zhu Minjian, Zhang Huaiyu, et al.Research on modeling of electric power transaction process based on blockchain technology[J]. Electrical Engineering, 2020, 21(6): 26-34.
[17] 卫志农, 余爽, 孙国强, 等. 虚拟电厂的概念与发展[J]. 电力系统自动化, 2013, 37(13): 1-9.
Wei Zhinong, Yu Shuang, Sun Guoqiang, et al.Concept and development of virtual power plant[J]. Automation of Electric Power Systems, 2013, 37(13): 1-9.
[18] 刘浩文, 刘东, 陈张宇, 等. 多级协同虚拟电厂环境下的无功辅助服务优化出清[J]. 电网技术, 2021, 45(7): 2533-2541.
Liu Haowen, Liu Dong, Chen Zhangyu, et al.Optimal reactive power service clearing based on multilevel cooperative VPP[J]. Power System Technology, 2021, 45(7): 2533-25415.
[19] 胡俊杰, 王坤宇, 艾欣, 等. 交互能源:实现电力能源系统平衡的有效机制[J]. 中国电机工程学报, 2019, 39(4): 953-966.
Hu Junjie, Wang Kunyu, Ai Xin, et al.Transactive energy: An effective mechanism for balancing electric energy system[J]. Proceedings of the CSEE, 2019, 39(4): 953-966.
[20] 陈新和, 裴玮, 邓卫. 基于代理模型的分布式能源现货市场运营模式[J]. 电力自动化设备, 2020, 40(10): 107-116.
Chen Xinhe, Pei Wei, Deng Wei.Surrogate model based operation mode of distributed energy spot market[J]. Electric Power Automation Equipment, 2020, 40(10): 107-116.
[21] 罗桓桓, 王昊, 葛维春, 等. 考虑报价监管的动态调峰辅助服务市场竞价机制设计[J]. 电工技术学报, 2021, 36(9): 1935-1947, 1955.
Luo Huanhuan, Wang Hao, Ge Weichun, et al.Design of dynamic peak regulation ancillary service market bidding mechanism considering quotation supervision[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1935-1947, 1955.
[22] 丁一, 惠红勋, 林振智, 等. 面向电力需求侧主动响应的商业模式及市场框架设计[J]. 电力系统自动化, 2017, 41(14): 2-9, 189.
Ding Yi, Hui Hongxun, Lin Zhenzhi, et al.Design of business model and market framework oriented to active demand response of power demand side[J]. Automation of Electric Power Systems, 2017, 41(14): 2-9, 189.
[23] 包铭磊, 丁一, 绍常政, 等. 北欧电力市场评述及对我国的经验借鉴[J]. 中国电机工程学报, 2017, 37(17): 4881-4892.
Bao Minglei, Ding Yi, Shao Changzheng, et al.Review of nordic electricity market and its suggestions for China[J]. Proceedings of the CSEE, 2017, 37(17): 4881-4892.
[24] 陈国平, 梁志峰, 董昱. 基于能源转型的中国特色电力市场建设的分析与思考[J]. 中国电机工程学报, 2020, 40(2): 369-379.
Chen Guoping, Liang Zhifeng, Dong Yu.Analysis and reflection on the marketization construction of electric power with chinese characteristics based on energy transformation[J]. Proceedings of the CSEE, 2020, 40(2): 369-379.
[25] Keles D, Bublitz A, Zimmermann F, et al.Analysis of design options for the electricity market: The German case[J]. Applied Energy, 2016, 183(1): 884-901.
[26] Ableitner L, Tiefenbeck V, Meeuw A, et al.User behavior in a real-world peer-to-peer electricity market[J]. Applied Energy, 2020, 270(1): 501-528.
[27] Morstyn T, Teytelboym A, McCulloch M D. Designing decentralized markets for distribution system flexibility[J]. IEEE Transations on Power Systems, 2019, 34(3): 2128-2139.
[28] Wu Yiwei, Shi Jian, Lim G J, et al.Optimal management of transactive distribution electricity markets with co-optimized bidirectional energy and ancillary service exchanges[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 4650-4661.
[29] Xiao Yunpeng, Wang Xifan, Pinson P, et al.Transactive energy based aggregation of prosumers as a retailer[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3302-3312.
[30] Barman M, Choudhury N B D, Sutradhar S. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India[J]. Energy, 2018, 145(1): 710-720.
[31] Bouktif S, Fiaz A, Ouni A, et al.Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting[J]. Energies, 2020,13(2): 391-414.
[32] 唐成鹏, 张粒子, 刘方, 等. 基于多智能体强化学习的电力现货市场定价机制研究(一): 不同定价机制下发电商报价双层优化模型[J]. 中国电机工程学报, 2021, 41(2): 536-553.
Tang Chenpeng, Zhang Lizi, Liu Fang, et al.Research on pricing mechanism of electricity spot market based on multi-agent reinforcement learning (part I): bi-level optimization model for generators under different pricing mechanisms[J]. Proceedings of the CSEE, 2021, 41(2): 536-553.
[33] 吴辰晔, 孙健. 凸包定价模式下的电力市场潜在市场力分析方法[J]. 电力系统自动化, 2021, 45(6): 101-108.
Wu Chenye, Sun Jiang.Analysis method for potential market power of electricity market in convex hull pricing mode[J]. Automation of Electric Power Systems, 2021, 45(6): 101-108l
[34] 吴界辰, 艾欣, 胡俊杰. 需求侧资源灵活性刻画及其在日前优化调度中的应用[J]. 电工技术学报, 2020, 35(9): 1973-1984.
Wu Jiechen, Ai Xin, Hu Junjie.Methods for characterizing flexibilities from demand-side resources and their applications in the day-ahead optimal scheduling[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 1973-1984.
[35] 孙毅, 刘迪, 李彬, 等. 深度强化学习在需求响应中的应用[J]. 电力系统自动化, 2019, 43(5): 183-191.
Sun Yi, Liu Di, Li Bin, et al.Application of deep reinforcement learning in demand response[J]. Automation of Electric Power Systems, 2019, 43(5): 183-191.
[36] 叶畅, 苗世洪, 刘昊, 等. 联盟链框架下主动配电网电力交易主体合作演化策略[J]. 电工技术学报, 2020, 35(8): 1739-1753.
Ye Chang, Miao Shihong, Liu Hao, et al.Cooperative evolution strategy of power transaction entities in active distribution network under the framework of alliance chain[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1739-1753.
[37] 孙玲玲, 贾清泉, 张弓, 等. 考虑运营交易的微电网优化配置方法[J]. 中国电机工程学报, 2020, 40(7): 2221-2233.
Sun Lingling, Jia Qingquan, Zhang Gong, et al.Optimal configuration method of microgrid considering operation transaction[J]. Proceedings of the CSEE, 2020, 40(7): 2221-2233.
[38] Wang Yunqi, Qiu Jing, Tao Yuechuan, et al.Carbon-Oriented operational planning in coupled electricity and emission trading markets[J]. IEEE Transations on Power Systems, 2020, 35(4): 3145-3157.
[39] Porras A, Fernandez-Blanco R, Morales J M, et al.An efficient robust approach to the day-ahead operation of an aggregator of electric vehicles[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 4960-4970.
[40] 贾雨龙, 米增强, 余洋, 等. 计及不确定性的柔性负荷聚合商随机-鲁棒投标决策模型[J]. 电工技术学报, 2019, 34(19): 4096-4107.
Jia Yulong, Mi Zengqiang, Yu Yang, et al.Fast and coordinated charging strategy of electric bus considering time-of-use price[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4096-4107.
[41] 陈丽娟, 秦萌, 顾少平, 等. 计及电池损耗的电动公交车参与V2G的优化调度策略[J]. 电力系统自动化, 2020, 44(11): 52-60.
Chen Lijuan, Qin Meng, Gu Shaopin, et al.Optimal dispatching strategy of electric bus participating in vehicle-to-grid considering battery loss[J]. Automation of Electric Power Systems, 2020, 44(11): 52-60.
[42] 吴界辰, 艾欣. 交互能源机制下的电力产消者优化运行[J]. 电力系统自动化, 2020, 44(19): 1-18.
Wu Jiechen, Ai Xin.Optimal operation of prosumers based on transactive energy mechanism[J]. Automation of Electric Power Systems, 2020, 44(19): 1-18.