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

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摘要

随着新型电力系统的数字化转型,电力数据资产已成为电力系统执行决策的核心因素,其价值已经得到广泛认可。然而,如何对电力数据资产价值进行有效评估成为学术界面临的重大挑战。现少有全面的方法来评估电力数据价值,而且由于数据资产本身的复杂性以及其在特定应用场景下的依赖性,具体应用哪种方法难以达成共识,以上使得数据难以充分释放其价值为电力系统服务。本文首先构建了电力数据资产价值化评估的研究框架,从电力数据价值化全流程以及关键问题着手,综述了现有估值方法以及数据估值在电力系统中的应用现状;最后,展望了数据价值挖掘以及评估在未来电力系统中的潜在应用并讨论了可能存在的问题与不足,为电力数据资产价值化研究与应用提供参考,以期促进能源领域数据的高效利用与流通。

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	丁正凯
	颜剑峰
	王蓓蓓

关键词 ：数据价值, 电力系统, 估值方法, 价值挖掘

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

收稿日期: 2025-02-11

PACS:

TM76

基金资助:

教育部人文社科基金资助项目（21YJAZH083）

通讯作者: 王蓓蓓女,1983年生,副教授,博士生导师,研究方向为电力市场、需求侧管理等。E-mail：wangbeibei@seu.edu.cn

作者简介: 丁正凯男,1996年生,博士研究生,研究方向为智能电网安全、大数据价值挖掘等。E-mail：15895190157@163.com

引用本文:

丁正凯, 颜剑峰, 王蓓蓓. 电力数据资产价值化评估：研究框架与展望[J]. 电工技术学报, 0, (): 20250205-20250205.

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