电力数据资产价值化评估：研究框架与展望

doi:10.19595/j.cnki.1000-6753.tces.250205

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1864 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

With the deep implementation of the "dual carbon" policy and the advancement of intelligent measurement technologies, the new power system is undergoing a transformation toward digital intelligence and low-carbon development. Power data assets play a pivotal role in this transition. For example, power grids leverage artificial intelligence and other advanced technologies to utilize load data for future load forecasting and economic dispatch, enabling cost savings. Additionally, power enterprises can package and trade data assets in data markets, where data providers obtain economic benefits, and data purchasers derive value from data products. However, despite the widespread recognition of data value in the power system, the inherent complexity of data assets and their dependency on specific application scenarios have hindered the development of universally accepted and reliable evaluation methods. The challenges restrict the circulation and deep utilization of power data, potentially impeding the green, low-carbon, and intelligent transformation of power systems.
To address these challenges, this paper constructs a research framework for evaluating the value realization of power data assets, structured around four key stages: data resourceization, assetization, commoditization, and capitalization. The paper systematically explores the critical issues at each stage and their relationship with data valuation. Furthermore, it reviews and analyzes existing valuation methodologies in power systems, including the three fundamental approaches, real options analysis, indicator-based evaluation, and impact-based methods, to provide solutions to key valuation challenges. The study also compares these methods in terms of their advantages, limitations, applicability, and practical application scenarios. Additionally, it categorizes data valuation applications in power systems from three perspectives: valuation objects, valuation stages, and functional applications. Specifically, the valuation stages include the individual and interaction stages; the valuation objects encompass datasets, features, and data products; and the functional applications cover decision support, information identification, model interpretability, profit distribution, and data transactions.
Based on the analysis of valuation methods and their current applications, this study further explores future directions and challenges in power data valuation. Key areas of focus include privacy-aware data value extraction, value distribution in federated learning, integrated valuation under predictive decision-making frameworks, data market pricing and transaction mechanisms, benefit distribution and regulatory oversight, and the development of data valuation platforms. By advancing the application of data valuation in data management, market transactions, data sharing, and model interpretability, this paper aims to unlock the full potential of power data, facilitate its efficient circulation, and promote the high-efficiency operation of power systems.

Key words： Data value power systems valuation methods value mining

Received: 11 February 2025

PACS:

TM76

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250205 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/20250205

[1] 樊强, 刘东, 王宇飞, 等. 电力信息物理系统形态演进关键技术及其进展[J]. 中国电机工程学报, 2024, 44(21): 8341-8353.
Fan Qiang, Liu Dong, Wang Yufei, et al.Key technologies and trends of cyber physical power system morphology evolution[J]. Proceedings of the CSEE, 2024, 44(21): 8341-8352.
[2] 韩笑, 郭剑波, 蒲天骄, 等. 电力人工智能技术理论基础与发展展望(一): 假设分析与应用范式[J]. 中国电机工程学报, 2023, 43(8): 2877-2891.
Han Xiao, Guo Jianbo, Pu Tianjiao, et al.Theoretical foundation and directions of electric power artificial intelligence(Ⅰ): hypothesis analysis and application paradigm[J]. Proceedings of the CSEE, 2023, 43(8): 2877-2891.
[3] 左宇, 秦大林, 杜尔顺, 等. 交互式负荷预测:研究框架与展望[J/OL]. 中国电机工程学报,1-13[2025-01-22] .
Zuo Yu, Zuo Yu, Qin Dalin, DU Ershun, et al.Inter active load forecasting: research framework and outlook[J/OL]. Proceedings of the CSEE, 1-13[2025-01-22] .
[4] 于明凯. 数据价值驱动的新能源电力系统优化运行方法研究[D]. 北京: 华北电力大学, 2023.
Yu Mingkai.Research on optimal operation method of new energy power system driven by data value[D]. Beijing: North China Electric Power University, 2023.
[5] 文沛先. 基于数据信息技术驱动的光伏发电系统智能化故障诊断[J]. 太阳能学报, 2024, 45(12): 688.
Wen Peixian.Intelligent fault diagnosis of photovoltaic power generation system driven by data information technology[J]. Acta Energiae Solaris Sinica, 2024, 45(12): 688.
[6] Wang Bohong, Guo Qinglai, Yang Tianyu, et al.Data valuation for decision-making with uncertainty in energy transactions: a case of the two-settlement market system[J]. Applied Energy, 2021, 288: 116643.
[7] .刘桂锋, 吴雅琪, 韩牧哲, 等. 面向数据要素价值化的数据资源应用场景创新研究[J]. 情报理论与实践, 2025, 48(1): 53-62.
LIU Guifeng, WU Yaqi, HAN Muzhe, et al.Research on the innovation of data resource application scenarios for the valorization of data elements[J]. Intelligence Theory and Practice, 2025, 48(1): 53-62
[8] 中共中央. 国务院关于构建数据基础制度更好发挥数据要素作用的意见.[EB/OL].
[2022.12.19] .https://www.gov.cn/zhengce/2022-12/19/content_5732695.htm.
[9] 国家能源局关于加快推进能源数字化智能化发展的若干意见[EB/OL].
[2023.03.28] .https://www.gov.cn/zhengce/zhengceku/2023-04/02/content_5749758.htm.
[10] 中国南方电网有限责任公司.中国南方电网有限责任公司数据资产定价方法(试行)[R/OL].
[2022-08-30] .https://power.in-en.com/html/power-2385119.shtml.
[11] Liu Ziming, Huang Bonan, Li Yushuai, et al.Pricing game and blockchain for electricity data trading in low-carbon smart energy systems[J]. IEEE Transactions on Industrial Informatics, 2024, 20(4): 6446-6456.
[12] 胥婷, 吴丹麦, 魏明月, 等. 开放数据视角下健康医疗数据价值评估指标体系研究[J]. 医学信息学杂志, 2022, 43(1): 16-22.
Xu Ting, Wu Danmai, Wei Mingyue, et al.Study on the index system of healthcare data value evaluation from the perspective of open data[J]. Journal of Medical Informatics, 2022, 43(1): 16-22.
[13] Xu Anran, Zheng Zhenzhe, Wu Fan, et al.Online data valuation and pricing for machine learning tasks in mobile health[C]//IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, London, United Kingdom, 2022: 850-859.
[14] Brinch M.Understanding the value of big data in supply chain management and its business processes[J]. International Journal of Operations & Production Management, 2018, 38(7): 1589-1614.
[15] Ghasemaghaei M, Calic G.Does big data enhance firm innovation competency? The mediating role of data-driven insights[J]. Journal of Business Research, 2019, 104: 69-84.
[16] Ghorbani A, Zou J.Data shapley: Equitable valuation of data for machine learning[C]//International conference on machine learning. PMLR, 2019: 2242-2251.
[17] Coyle D, Manley A.What is the value of data? A review of empirical methods[J]. Journal of Economic Surveys, 2024, 38(4): 1317-1337.
[18] Sim R H L, Xu X, Low B K H. Data valuation in machine learning:" ingredients", strategies, and open challenges[C]//IJCAI, 2022: 5607-5614.
[19] Coyle D, Diepeveen S, Wdowin J. (2020a). The value of data summary report. Cambridge: The Bennett Institute for Public Policy. https://www.bennettinstitute.cam.ac.uk/publications/value-data-summary-report/
[20] 高骞, 胡广伟, 林辉, 等. 电力大数据价值链及其价值创造模式研究[J]. 中国科技资源导刊, 2020, 52(1): 6-13, 34.
Gao Qian, Hu Guangwei, Lin Hui, et al.Analysis on the value chain and value creation models of power big data[J]. China Science & Technology Resources Review, 2020, 52(1): 6-13, 34.
[21] 李海舰, 赵丽. 数据成为生产要素: 特征、机制与价值形态演进[J]. 上海经济研究, 2021, 33(8): 48-59.
Li Haijian, Zhao Li.Data becomes a factor of production: characteristics, mechanisms, and the evolution of value form[J]. Shanghai Journal of Economics, 2021, 33(8): 48-59.
[22] 朱秀梅, 林晓玥, 王天东, 等. 数据价值化: 研究评述与展望[J]. 外国经济与管理, 2023, 45(12): 3-17.
Zhu Xiumei, Lin Xiaoyue, Wang Tiandong, et al.Data valued process: a review and prospects[J]. Foreign Economics & Management, 2023, 45(12): 3-17.
[23] 魏帅, 马芳. 浅谈如何提高电力营销系统基础数据质量[J]. 科技风, 2018(34): 194.
Wei Shuai, Ma Fang.Discussion on how to improve the quality of basic data of electric power marketing system[J]. Technology Wind, 2018(34): 194.
[24] 谢琳, 陶蕾, 叶瑞丽, 等. 基于多源量测信息的数据质量诊断方法研究与分析[J]. 电器与能效管理技术, 2024(5): 36-45.
Xie Lin, Tao Lei, Ye Ruili, et al.Research and analysis on data quality diagnosis method based on multi-source measuring information[J]. Electrical & Energy Management Technology, 2024(5): 36-45.
[25] 计蓉, 侯慧娟, 盛戈皞, 等. 基于粒子群优化堆叠降噪自编码器的电力设备状态数据质量提升[J/OL]. 上海交通大学学报, 1-12[2025-01-22] .
Ji Rong, Hou Huijuan, Sheng Gehao, et al.Quality improvement of power equipment condition data based on particle swarm optimization stacked noise reduction self-encoder[J/OL]. Journal of Shanghai Jiao Tong University, 1-12[2025-01-22] .
[26] 孔祥玉, 马玉莹, 艾芊, 等. 新型电力系统多元用户的用电特征建模与用电负荷预测综述[J]. 电力系统自动化, 2023, 47(13): 2-17.
Kong Xiangyu, Ma Yuying, Ai Qian, et al.Review on electricity consumption characteristic modeling and load forecasting for diverse users in new power system[J]. Automation of Electric Power Systems, 2023, 47(13): 2-17.
[27] 计蓉, 侯慧娟, 盛戈皞, 等. 基于组合赋权法和模糊综合评价的电力设备状态数据质量评估[J]. 高电压技术, 2024, 50(1): 274-281.
Ji Rong, Hou Huijuan, Sheng Gehao, et al.Quality assessment of power equipment condition data based on combined assignment method and fuzzy comprehensive evaluation[J]. High Voltage Engineering, 2024, 50(1): 274-281.
[28] 李锦狄, 刘建戈, 张鹏宇, 等. 关于电网数据资产化与价值评估的探索[J]. 中国信息化, 2020(12): 67-68.
Li Jindi, Liu Jiange, Zhang Pengyu, et al.Exploration on capitalization and value evaluation of power grid data[J]. China Informatization, 2020(12): 67-68.
[29] 邢汇笛, 龚钢军, 翟明岳, 等. 电力数据共享安全防护与隐私保护综述[J]. 综合智慧能源, 2024, 46(5): 30-40.
Xing Huidi, Gong Gangjun, Zhai Mingyue, et al.Research on security and privacy protection of electric power data sharing[J]. Integrated Intelligent Energy, 2024, 46(5): 30-40.
[30] 王蓓蓓, 朱竞, 王嘉乐, 等. 电表数据隐私保护下的联邦学习行业电力负荷预测框架[J]. 电力系统自动化, 2023, 47(13): 86-93.
Wang Beibei, Zhu Jing, Wang Jiale, et al.Federated-learning based industry load forecasting framework under privacy protection of meter data[J]. Automation of Electric Power Systems, 2023, 47(13): 86-93.
[31] Zhou Yangze, Wen Qingsong, Song Jie, et al.Load data valuation in multi-energy systems: an end-to-end approach[J]. IEEE Transactions on Smart Grid, 2024, 15(5): 4564-4575.
[32] 谷云东, 刘浩. 基于最优特征组合改进极限梯度提升的负荷预测[J]. 计算机应用研究, 2021, 38(9): 2767-2772.
Gu Yundong, Liu Hao.Load forecasting based on optimal feature combination improved XGBoost[J]. Application Research of Computers, 2021, 38(9): 2767-2772.
[33] 仲林林, 刘柯妤. 面向电力巡检图像目标检测的联邦学习激励机制[J]. 电工技术学报, 2024, 39(17): 5434-5449.
Zhong Linlin, Liu Keyu.Federated-learning incentive mechanism for object detection in power inspection images[J]. Transactions of China Electrotechnical Society, 2024, 39(17): 5434-5449.
[34] 数据价值化与数据要素市场发展报告[EB/OL].
[2024.09.09] . http://www.caict.ac.cn/kxyj/qwfb/ztbg/202409/P020240926365684089988.pdf.
[35] 徐漪. 大数据的资产属性与价值评估[J]. 产业与科技论坛, 2017, 16(2): 97-99.
Xu Yi.Asset attribute and value evaluation of big data[J]. Industrial & Science Tribune, 2017, 16(2): 97-99.
[36] 李永红, 张淑雯. 数据资产价值评估模型构建[J]. 财会月刊, 2018(9): 30-35.
Li Yonghong, Zhang Shuwen.Establishment of the value evaluation model of data assets[J]. Finance and Accounting Monthly, 2018(9): 30-35.
[37] 陈芳, 余谦. 数据资产价值评估模型构建: 基于多期超额收益法[J]. 财会月刊, 2021(23): 21-27.
Chen Fang, Yu Qian.Construction of data asset value evaluation model-based on multi-period excess return method[J]. Finance and Accounting Monthly, 2021(23): 21-27.
[38] 周艳秋. 数字经济驱动下数据资产价值评估研究[D]. 北京: 首都经济贸易大学, 2022.
Zhou Yanqiu.Research on value evaluation of data assets driven by digital economy[D]. Beijing: Capital University of Economics and Business, 2022.
[39] 邹贵林, 陈雯, 吴良峥, 等. 电网数据资产定价方法研究: 基于两阶段修正成本法的分析[J]. 价格理论与实践, 2022(3): 89-93, 204.
Zou Guilin, Chen Wen, Wu Liangzheng, et al.Research on grid power data asset pricing method: Analysis based on two-stage modified costing[J]. Price (Theory & Practice), 2022(3): 89-93, 204.
[40] Myers S C.Determinants of corporate borrowing[J]. Journal of Financial Economics, 1977, 5(2): 147-175.
[41] 刘国磊. 典型应用场景下电网企业数据资产价值测度研究[D]. 北京: 华北电力大学, 2023.
Liu Guolei.Research on value measurement of data assets of power grid enterprises in typical application scenarios[D]. Beijing: North China Electric Power University, 2023.
[42] Batini C, Cappiello C, Francalanci C, et al.Methodologies for data quality assessment and improvement[J]. ACM Computing Surveys, 2009, 41(3): 1-52.
[43] 孙俊烨. 电网企业数据资产价值评估研究: 以国家电网为例[D]. 保定: 河北大学, 2023.
Sun Junye.Research on value evaluation of data assets of power grid enterprises: taking state grid as an example[D]. Baoding: Hebei University, 2023.
[44] 沈赋, 张微, 徐潇源, 等. 基于随机森林和最大互信息系数关键特征选择的配电网拓扑辨识研究[J]. 电力系统保护与控制, 2024, 52(17): 1-11.
Shen Fu, Zhang Wei, Xu Xiaoyuan, et al.Topological identification of distribution networks based on key feature selection using RF and MIC[J]. Power System Protection and Control, 2024, 52(17): 1-11.
[45] Shapley L S.A value for n-person games[J]. Contributions to the Theory of Games, 1953, 2.
[46] Wang Bohong, Guo Qinglai, Yu Yang.Mechanism design for data sharing: an electricity retail perspective[J]. Applied Energy, 2022, 314: 118871.
[47] Sun Zelin, Von Krannichfeldt L, Wang Yi.Trading and valuation of day-ahead load forecasts in an ensemble model[J]. IEEE Transactions on Industry Applications, 2023, 59(3): 2686-2695.
[48] Quinlan J R.C4. 5: programs for machine learning[M]. Elsevier, 2014.
[49] Ribeiro M T, Singh S, Guestrin C.“Why should I trust you?”: explaining the predictions of any classifier[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA, 2016: 1135-1144.
[50] Fisher A, Rudin C, Dominici F.All models are wrong, but Many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously[J]. Journal of Machine Learning Research, 2019, 20: 177.
[51] Lundberg S, Lee S I. A unified approach to interpreting model predictions[EB/OL].2017: 1705.07874.
https://arxiv.org/abs/1705.07874v2.
[52] 刘雁文, 胡炎, 邰能灵. 基于决策树的智能变电站运维专家系统规则提取方法[J]. 电力科学与技术学报, 2019, 34(1): 123-128.
Liu Yanwen, Hu Yan, Tai Nengling.Rule extraction method of operation and maintenance expert system for an intelligent substation based on the decision tree[J]. Journal of Electric Power Science and Technology, 2019, 34(1): 123-128.
[53] Kim M, Jun J A, Song Yujin, et al.Explanation for building energy prediction[C]//2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2020: 1168-1170.
[54] An Jun, Yu Jiachen, Li Zonghan, et al.A data-driven method for transient stability margin prediction based on security region[J]. Journal of Modern Power Systems and Clean Energy, 2020, 8(6): 1060-1069.
[55] Ma Hongnan, McAreavey K, McConville R, et al. Explainable AI for non-experts: energy tariff forecasting[C]//2022 27th International Conference on Automation and Computing (ICAC), Bristol, United Kingdom, 2022: 1-6.
[56] Kuzlu M, Cali U, Sharma V, et al.Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools[J]. IEEE Access, 2020, 8: 187814-187823.
[57] 吴泽方. 电网企业数据资产价值评估及价值实现研究[D]. 长沙: 长沙理工大学, 2021.
Wu Zefang.Research on value evaluation and value realization of data assets in power grid enterprises[D]. Changsha: Changsha University of Science & Technology, 2021.
[58] 王瑶, 吴云来, 俞铁铭, 等. 基于特征选择和XGBoost算法考虑极端天文、气象事件影响的光伏性能预测方法[J]. 太阳能学报, 2024, 45(5): 547-555.
Wang Yao, Wu Yunlai, Yu Tieming, et al.Forecasting method of photovoltaic power generation based on feature selection and XGBoost algorithm considering influence of extreme astronomical and meteorological events[J]. Acta Energiae Solaris Sinica, 2024, 45(5): 547-555.
[59] 商立群, 李洪波, 侯亚东, 等. 基于特征选择和优化极限学习机的短期电力负荷预测[J]. 西安交通大学学报, 2022, 56(4): 165-175.
Shang Liqun, Li Hongbo, Hou Yadong, et al.Short-term power load forecasting based on feature selection and optimized extreme learning machine[J]. Journal of Xi’an Jiaotong University, 2022, 56(4): 165-175.
[60] 马良玉, 程东炎, 梁书源, 等. 基于LightGBM-VIF-MIC-SFS的风电机组故障诊断输入特征选择方法[J]. 热力发电, 2024, 53(1): 154-164.
Ma Liangyu, Cheng Dongyan, Liang Shuyuan, et al.Input feature selection method for wind turbine fault diagnosis based on LightGBM-VIF-MIC-SFS[J]. Thermal Power Generation, 2024, 53(1): 154-164.
[61] 甄永赞, 阮程. 基于强化学习的混合元启发式暂态电压稳定特征选择方法及可解释性研究[J]. 电网技术, 2024, 48(4): 1519-1532.
Zhen Yongzan, Ruan Cheng.Reinforcement learning-based hybrid element heuristic transient voltage stability feature selection and its interpretability[J]. Power System Technology, 2024, 48(4): 1519-1532.
[62] Moon J, Rho S, Baik S W.Toward explainable electrical load forecasting of buildings: a comparative study of tree-based ensemble methods with Shapley values[J]. Sustainable Energy Technologies and Assessments, 2022, 54: 102888.
[63] 章超波, 刘永政, 李宏波, 等. 基于加权残差聚类的建筑负荷预测区间估计[J]. 浙江大学学报(工学版), 2022, 56(5): 930-937.
Zhang Chaobo, Liu Yongzheng, Li Hongbo, et al.Weighted residual clustering-based building load prediction interval estimation[J]. Journal of Zhejiang University (Engineering Science), 2022, 56(5): 930-937.
[64] Dai X, Cheng S, Chong A.Deciphering optimal mixed-mode ventilation in the tropics using reinforcement learning with explainable artificial intelligence[J]. Energy and Buildings, 2023, 278: 112629.
[65] Wang Bohong, Xia Tian, Guo Qinglai, et al.Data valuation in electricity transactions with uncertainty considering risk preferences[C]//2022 IEEE 5th International Electrical and Energy Conference (CIEEC), Nangjing, China, 2022: 917-921.
[66] ELI5.
A library for debugging_inspecting machine learning classifiers and explaining their predictions.[EB/OL].
[2023-11-06] .https://github.com/eli5-org/eli5.
[67] Wang Bohong, Guo Qinglai, Yang Tianyu, et al.Evaluation of information value for solar power plants in market environment[C]//2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China, 2020: 3574-3580.
[68] 刘嘉诚, 刘俊, 赵宏炎, 等. 基于DKDE与改进mRMR特征选择的短期光伏出力预测[J]. 电力系统自动化, 2021, 45(14): 13-21.
Liu Jiacheng, Liu Jun, Zhao Hongyan, et al.Short-term photovoltaic output forecasting based on diffusion kernel density estimation and improved max-relevance and Min-redundancy feature selection[J]. Automation of Electric Power Systems, 2021, 45(14): 13-21.
[69] 杨晓梅, 郭林明, 肖先勇, 等. 基于可调品质因子小波变换和随机森林特征选择算法的电能质量复合扰动分类[J]. 电网技术, 2020, 44(8): 3014-3020.
Yang Xiaomei, Guo Linming, Xiao Xianyong, et al.Classification of multiple power quality disturbances based on TQWT and random forest feature selection algorithm[J]. Power System Technology, 2020, 44(8): 3014-3020.
[70] 向颖, 严慧峰, 余旭阳, 等. 基于特征优选及改进自组织神经网络的非侵入式负荷辨识[J]. 中国电机工程学报, 2022, 42(增刊1): 106-114.
Xiang Ying, Yan Huifeng, Yu Xuyang, et al.Non-invasive load identification based on feature optimization and improved self-organizing neural network[J]. Proceedings of the CSEE, 2022, 42(S1): 106-114.
[71] 池程芝, 潘震, 徐钊, 等. 基于多特征选择算法的功率变换器故障分类方法[J]. 西北工业大学学报, 2022, 40(3): 645-650.
Chi Chengzhi, Pan Zhen, Xu Zhao, et al.Power converter fault classification method based on multi-feature selection algorithm[J]. Journal of Northwestern Polytechnical University, 2022, 40(3): 645-650.
[72] 赵耀, 沈翀, 李东东, 等. 极端条件下基于特征层面信号融合的电励磁双凸极电机匝间短路故障诊断[J]. 电工技术学报, 2023, 38(10): 2661-2674.
Zhao Yao, Shen Chong, Li Dongdong, et al.Inter-turn short circuit diagnosis of wound-field doubly salient machine based on multi-signal fusion on feature level under extreme conditions[J]. Transactions of China Electrotechnical Society, 2023, 38(10): 2661-2674.
[73] 高晗, 蔡国伟, 杨德友, 等. 基于累积贡献率和可解释人工智能的静态电压稳定裕度估计特征量筛选方法[J]. 电力自动化设备, 2023, 43(4): 168-176.
Gao Han, Cai Guowei, Yang Deyou, et al.Feature selection approach based on FCC-eAI in static voltage stability margin estimation[J]. Electric Power Automation Equipment, 2023, 43(4): 168-176.
[74] Yu Mingkai, Wang Jianxiao, Yan Jie, et al.Pricing information in smart grids: a quality-based data valuation paradigm[J]. IEEE Transactions on Smart Grid, 2022, 13(5): 3735-3747.
[75] 王小君, 窦嘉铭, 刘曌, 等. 可解释人工智能在电力系统中的应用综述与展望[J]. 电力系统自动化, 2024, 48(4): 169-191.
Wang Xiaojun, Dou Jiaming, Liu Zhao, et al.Review and prospect of explainable artificial intelligence and its application in power systems[J]. Automation of Electric Power Systems, 2024, 48(4): 169-191.
[76] Beltrán S, Castro A, Irizar I, et al.Framework for collaborative intelligence in forecasting day-ahead electricity price[J]. Applied Energy, 2022, 306: 118049.
[77] Shen Xiaodong, Liu Huixin, Qiu Gao, et al.Interpretable interval prediction-based outlier-adaptive day-ahead electricity price forecasting involving cross-market features[J]. IEEE Transactions on Industrial Informatics, 2024, 20(5): 7124-7137.
[78] 杨佳泽, 王灿, 王增平. 新型电力系统背景下的智能负荷预测算法研究综述[J/OL]. 华北电力大学学报(自然科学版), 1-14[2024-09-14] .Yang Jiaze, Wang Can, Wang Zengping. A review of research on intelligent load forecasting algorithms in the context of new power systems[J/OL]. Journal of North China Electric Power University (Natural Science Edition), 1-14[2024-09-14] .
[79] Shams Amiri S, Mottahedi S, Lee E R, et al.Peeking inside the black-box: explainable machine learning applied to household transportation energy consumption[J]. Computers, Environment and Urban Systems, 2021, 88: 101647.
[80] Chung W J, Liu Chunde.Analysis of input parameters for deep learning-based load prediction for office buildings in different climate zones using eXplainable Artificial Intelligence[J]. Energy and Buildings, 2022, 276: 112521.
[81] 余长青. 基于人工智能方法的风电消纳能力预测及消纳措施优化[D]. 重庆: 重庆大学, 2020.
Yu Changqing.Prediction of wind power absorptive capacity and optimization of absorptive measures based on artificial intelligence method[D]. Chongqing: Chongqing University, 2020.
[82] 白玉莹. 计及风资源时空特征的风电功率超短期预测研究[D]. 吉林: 东北电力大学, 2021.
Bai Yuying.Study on ultra-short-term forecast of wind power considering the temporal and spatial characteristics of wind resources[D]. Jilin: Northeast Dianli University, 2021.
[83] Chang Xiaomin, Li Wei, Ma Jin, et al.Interpretable machine learning in sustainable edge computing: a case study of short-term photovoltaic power output prediction[C]//ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020: 8981-8985.
[84] 蒋莹莹, 田建艳, 姬政雄, 等. 可解释性验证的光伏出力实时纠偏概率预测模型[J]. 高电压技术, 2024, 50(9): 3944-3954.
Jiang Yingying, Tian Jianyan, Ji Zhengxiong, et al.Interpretability verification of real-time deviation correction probability prediction model of photovoltaic output[J]. High Voltage Engineering, 2024, 50(9): 3944-3954.
[85] Santos O L D, Dotta D, Wang Meng, et al. Performance analysis of a DNN classifier for power system events using an interpretability method[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107594.
[86] Sairam S, Seshadhri S, Marafioti G, et al.Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes[J]. Renewable Energy, 2022, 185: 1425-1440.
[87] 吕云龙, 胡琴, 胡紫园, 等. 考虑样本不平衡条件下风机叶片覆冰诊断及其可解释性研究[J/OL]. 电工技术学报, 1-13[2025-02-01] .
Lv Yunlong, Hu Qin, Hu Ziyuan, et al.Diagnosis of wind turbine blade ice cover and its interpretability under sample imbalance[J/OL]. Transactions of China Electrotechnical Society, 1-13[2025-02-01] .
[88] 许格健. 电池储能系统典型单元运行状态参数预测方法研究[D]. 吉林: 东北电力大学, 2020.
Xu Gejian.Study on prediction method of operating state parameters of typical cells in battery energy storage system[D]. Jilin: Northeast Dianli University, 2020.
[89] 吴洋, 辛茹, 邹文滔, 等. 提升电力现货市场出清结果可解释性的综合分析方法[J]. 南方电网技术, 2022, 16(6): 113-123.
Wu Yang, Xin Ru, Zou Wentao, et al.Comprehensive analysis method for enhancing the explainability of electricity spot market clearing results[J]. Southern Power System Technology, 2022, 16(6): 113-123.
[90] 陈明昊, 朱月瑶, 孙毅, 等. 计及高渗透率光伏消纳与深度强化学习的综合能源系统预测调控[J]. 电工技术学报, 2024, 39(19): 6054-6071, 6103.
Chen Minghao, Zhu Yueyao, Sun Yi, et al.The predictive-control optimization method for park integrated energy system considering the high penetration of photovoltaics and deep reinforcement learning[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 6054-6071, 6103.
[91] Utama C, Meske C, Schneider J, et al.Reactive power control in photovoltaic systems through (explainable) artificial intelligence[J]. Applied Energy, 2022, 328: 120004.
[92] Zhang Ke, Zhang Jun, Xu Peidong, et al.Explainable AI in deep reinforcement learning models for power system emergency control[J]. IEEE Transactions on Computational Social Systems, 2022, 9(2): 419-427.
[93] 周挺, 杨军, 詹祥澎, 等. 一种数据驱动的暂态电压稳定评估方法及其可解释性研究[J]. 电网技术, 2021, 45(11): 4416-4425.
Zhou Ting, Yang Jun, Zhan Xiangpeng, et al.Data-driven method and interpretability analysis for transient voltage stability assessment[J]. Power System Technology, 2021, 45(11): 4416-4425.
[94] 陈明华, 刘群英, 张家枢, 等. 基于XGBoost的电力系统暂态稳定预测方法[J]. 电网技术, 2020, 44(3): 1026-1034.
Chen Minghua, Liu Qunying, Zhang Jiashu, et al.XGBoost-based algorithm for post-fault transient stability status prediction[J]. Power System Technology, 2020, 44(3): 1026-1034.
[95] 陈明华. 电力系统暂态稳定性智能评估方法研究[D]. 成都: 电子科技大学, 2019.
Chen Minghua.Research on intelligent evaluation method of power system transient stability[D]. Chengdu: University of Electronic Science and Technology of China, 2019.
[96] Wang Jianxiao, Gao Feng, Zhou Yangze, et al.Data sharing in energy systems[J]. Advances in Applied Energy, 2023, 10: 100132.
[97] Pinson P, Han Liyang, Kazempour J.Regression markets and application to energy forecasting[J]. TOP, 2022, 30(3): 533-573.
[98] Li Chuang, He Aoli, Wen Yanhua, et al.Optimal trading mechanism based on differential privacy protection and Stackelberg game in big data market[J]. IEEE Transactions on Services Computing, 2023, 16(5): 3550-3563.
[99] 郭庆来, 王博弘, 田年丰, 等. 能源互联网数据交易: 架构与关键技术[J]. 电工技术学报, 2020, 35(11): 2285-2295.
Guo Qinglai, Wang Bohong, Tian Nianfeng, et al.Data transactions in energy Internet: architecture and key technologies[J]. Transactions of China Electrotechnical Society, 2020, 35(11): 2285-2295.
[100] Oh H, Park S, Lee G M, et al.Personal data trading scheme for data brokers in IoT data marketplaces[J]. IEEE Access, 2019, 7: 40120-40132.
[101] McMahan H B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data[EB/OL].2016: 1602.05629.
https://arxiv.org/abs/1602.05629v4.
[102] Dwork C.Differential privacy: a survey of results[M]//Theory and Applications of Models of Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008: 1-19.
[103] Bos J W, Castryck W, Iliashenko I, et al.Privacy-friendly forecasting for the smart grid using homomorphic encryption and the group method of data handling[M]//Progress in Cryptology: AFRICACRYPT 2017.
Cham: Springer International Publishing, 2017: 184-201.
[104] Yao A C.Protocols for secure computations[C]//23rd Annual Symposium on Foundations of Computer Science (sfcs 1982), Chicago, IL, USA, 1982: 160-164.
[105] 许伦, 刘文杰, 李昀, 等. 零知识证明在新型电力系统中的应用分析及展望[J]. 中国电机工程学报, 2024, 44(增刊1): 114-130.
Xu Lun, Liu Wenjie, Li Yun, et al.Application analysis and prospect of zero-knowledge proof in new power system[J]. Proceedings of the CSEE, 2024, 44(S1): 114-130.
[106] 凡航, 徐葳, 范晓昱, 等. 隐私计算在新型电力系统中的应用分析与展望[J]. 电力系统自动化, 2023, 47(19): 187-199.
Fan Hang, Xu Wei, Fan Xiaoyu, et al.Application analysis and prospect of privacy-preserving computation in new power system[J]. Automation of Electric Power Systems, 2023, 47(19): 187-199.
[107] .陆俊, 肖琦, 龚钢军, 等. 基于动态差分隐私的联邦学习配电台区数据异常识别[J/OL]. 电网技术, 1-12[2025-02-01] .
Lu Jun, Xiao Qi, Gong Gangjun, et al.Federated learning distribution station data anomaly identification based on dynamic differential privacy[J/OL]. Power System Technology, 1-12[2025-02-01] .
[108] 黎海涛, 刘伊然, 杨艳红, 等. 基于改进联邦竞争深度Q网络的多微网能量管理策略[J]. 电力系统自动化, 2024, 48(8): 174-184.
Li Haitao, Liu Yiran, Yang Yanhong, et al.Energy management strategy for multi-microgrid based on improved federated dueling deep Q network[J]. Automation of Electric Power Systems, 2024, 48(8): 174-184.
[109] Wang Guan, Dang C X, Zhou Ziye.Measure contribution of participants in federated learning[C]//2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019: 2597-2604.
[110] Yoon J, Arik S O, Pfister T. Data valuation using reinforcement learning[EB/OL].2019: 1909.11671.
https://arxiv.org/abs/1909.11671v1.
[111] Han Xiao, Wang Leye, Wu Junjie, et al. Data valuation for vertical federated learning: a model-free and privacy-preserving method[EB/OL].2021: 2112.08364.
https://arxiv.org/abs/2112.08364v3.
[112] 王勇, 李国良, 李开宇. 联邦学习贡献评估综述[J]. 软件学报, 2023, 34(3): 1168-1192.
Wang Yong, Li Guoliang, Li Kaiyu.Survey on contribution evaluation for federated learning[J]. Journal of Software, 2023, 34(3): 1168-1192.
[113] Zhang Jialun, Wang Yi, Hug G.Cost-oriented load forecasting[J]. Electric Power Systems Research, 2022, 205: 107723.
[114] 李达, 郭庆雷, 冯景丽. 基于区块链的分布式电力交易隐私结算模型[J]. 电网技术, 2023, 47(9): 3608-3624.
Li Da, Guo Qinglei, Feng Jingli.Private settlement model of distributed power transactions based on blockchains[J]. Power System Technology, 2023, 47(9): 3608-3624.
[115] 凡航, 徐葳, 王倩雯, 等. 多方安全计算框架下的智能合约方法研究[J]. 信息安全研究, 2022, 8(10): 956-963.
Fan Hang, Xu Wei, Wang Qianwen, et al.Research on smart contract method in the framework of secure multi-party computation[J]. Journal of Information Security Research, 2022, 8(10): 956-963.
[116] 李凤华, 李晖, 牛犇, 等. 数据要素流通与安全的研究范畴与未来发展趋势[J]. 通信学报, 2024, 45(5): 1-11.
Li Fenghua, Li Hui, Niu Ben, et al.Research category and future development trend of data elements circulation and security[J]. Journal on Communications, 2024, 45(5): 1-11.