Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods

Daogui Tang, Yi-Ping Fang () and Enrico Zio ()
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Daogui Tang: LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay, WHUT - Wuhan University of Technology
Yi-Ping Fang: LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay, LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay
Enrico Zio: CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres

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Abstract: The two-way information exchange between customers and the utility in smart grids enables demand-response programs of customers and the integration of distributed renewable energy resources. However, this also makes the demand-response programs vulnerable to cyber attacks. In this paper, we study cyber attacks that target customers' demand-response programs in smart grids by injecting false consumption and generation information. Then, as a countermeasure, an online detector based on convolutional neural networks is designed to detect the cyber attacks and mitigate impacts. The vulnerability of power distribution systems with and without the proposed detector is analyzed with reference to a case study concerning the IEEE 34 bus test feeder. The results show that the power distribution systems is vulnerable to the studied cyber attack and the proposed detector can achieve high accuracy and mitigate the impact of cyber attacks with fixed change rates, whereas the attacks with variable change rates are inherently challenging to detect.

Keywords: Convolutional neural network; Cyber attacks detector; Demand-response; Distributed renewable energy resources; Smart grids; Computer crime; Convolution; Crime; Cyber attacks; Electric power distribution; Network security; Renewable energy resources; Sales (search for similar items in EconPapers)
Date: 2023-07
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Citations: View citations in EconPapers (4)

Published in Reliability Engineering and System Safety, 2023, 235, pp.109212. ⟨10.1016/j.ress.2023.109212⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04103525

DOI: 10.1016/j.ress.2023.109212

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