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.
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, 2026, 41(5): 1425-1449.
Ding Zhengkai, Yan Jianfeng, Wang Beibei. The Valuation of Electricity Power Data Assets: Research Framework and Future Prospects. Transactions of China Electrotechnical Society, 2026, 41(5): 1425-1449.
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2026, Vol. 41 
