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Feasibility of Data Markets in Smart Grids: an Online Survey

Chakraborty, S and Gangopadhyay, S and Das, S (2022) Feasibility of Data Markets in Smart Grids: an Online Survey. In: IEEE Power and Energy Conference at Illinois, PECI 2022, 10-11 March 2022, Champaign.

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Official URL: https://doi.org/10.1109/PECI54197.2022.9744011


Novel data-driven algorithms are being developed using smart grid (SG) data. However, the success of data-driven algorithms such as machine learning algorithms heavily depends on the availability of SG data. Currently, SG data mostly stay with the data generators such as utilities, market operators and system operators. The flow of SG data among various stakeholders is currently limited due to the lack of a proper data exchange platform. Recently, the concept of data markets for SGs has been introduced to address this issue. However, the success of data markets in SGs heavily depends on the large-scale participation of the various stakeholders. So, there is a need to understand the opinion of the stakeholders on various aspects of SG data markets. This paper presents the results of an online survey that was conducted in order to investigate the feasibility of data trading in SGs. A comprehensive analysis of the survey responses is also presented. It is found that the majority of the survey participants support the formation of data markets in SGs to increase the accessibility of practical SG data. However, a section of the participants raised concerns regarding privacy, grid security and money availability. The results of this survey will be helpful in deciding future research directions in this area. © 2022 IEEE.

Item Type: Conference Proceedings
Publication: 2022 IEEE Power and Energy Conference at Illinois, PECI 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Data mining; Electric power transmission networks; Electronic data interchange; Learning algorithms; Machine learning; Online systems; Smart power grids, Data market; Data-driven algorithm; Grid data; Machine learning algorithms; Market system; Online surveys; Power; Power system; Smart grid; Utility markets, Surveys
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 19 May 2022 04:50
Last Modified: 19 May 2022 04:50
URI: https://eprints.iisc.ac.in/id/eprint/72058

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