Enhancing cybersecurity in virtual power plants by detecting network based cyber attacks using an unsupervised autoencoder approach

The increasing adoption of the Internet of Things (IoT) in energy systems has brought significant advancements but also heightened cyber security risks. Virtual Power Plants (VPPs), which aggregate distributed renewable energy resources into a single entity for participation in energy markets, are p...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 32374 - 23
Main Authors: Singh, Kumari Nutan, Goswami, Arup Kumar, Chudhury, Nalin Behari Dev, Shuaibu, Hassan Abdurrahman, Ustun, Taha Selim
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 05.09.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/4073
639/166/987
639/4077/4073
639/4077/909
Algorithms
Alternative energy sources
Architecture
Auto-encoder (AE)
Communication
Consumers
Cyber security
Cybersecurity
Deep learning
Denial of service attacks
Energy consumption
Energy market (EM)
Energy resources
Energy storage
False data injection attack (FDIA)
Humanities and Social Sciences
Infrastructure
Internet of Things
Interoperability
Machine learning
multidisciplinary
Neural networks
Power plants
Privacy
Renewable energy sources
Renewable energy sources (RES)
Renewable resources
Science
Science (multidisciplinary)
Smart grid technology
Software
Virtual power plant (VPP)
Virtual power plant (VPP)
Renewable energy sources (RES)
Energy market (EM)
Cyber security
False data injection attack (FDIA)
Auto-encoder (AE)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The increasing adoption of the Internet of Things (IoT) in energy systems has brought significant advancements but also heightened cyber security risks. Virtual Power Plants (VPPs), which aggregate distributed renewable energy resources into a single entity for participation in energy markets, are particularly vulnerable to cyber-attacks due to their reliance on modern information and communication technologies. Cyber-attacks targeting devices, networks, or specific goals can compromise system integrity. Common attack types include Denial of Service (DoS), Man-in-the-Middle (MITM), and False Data Injection Attacks (FDIA).Among these threats, FDIA are especially concerning as they manipulate critical operational data, such as bid prices and energy quantities, to disrupt system reliability, market stability, and financial performance. This study proposes an unsupervised Autoencoder (AE) deep learning approach to detect FDIA in VPP systems. The methodology is validated on a 9-bus and IEEE-39 bus test system modeled in MATLAB Simulink, encompassing renewable energy sources, energy storage systems, and variable loads. Time-series data generated over 1,000 days is used for training, validation, and testing the AE model. The results demonstrate the model’s ability to detect anomalies with high accuracy by analyzing reconstruction errors. By identifying false data, the approach ensures system reliability, protects against financial losses, and maintains energy market stability. This work highlights the importance of advanced machine learning techniques in enhancing cyber security for IoT-based energy systems and ensuring secure VPP operations.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-01863-w