Exploring False Demand Attacks in Power Grids with High PV Penetration

Ashish Neupane, Weiqing Sun

2023

Abstract

The push for renewable energy has certainly driven the world towards sustainability. However, the incorporation of clean energy into the electric power grid does not come without challenges. When synchronous generators are replaced by inverter based Photovoltaic (PV) generators, the voltage profile of the grid gets considerably degraded. The effect in voltage profile, added with the unpredictable generation capacity, and lack of good reactive power control eases opportunities for sneaky False Data Injection (FDI) attacks that could go undetected. The challenge is to differentiate these two phenomena. In this paper, an attack is explored in a grid environment with a high PV penetration, and challenges associated with designing a detector that accounts for inefficiencies that comes with it is discussed. The detector is a popular Kalman Filter based anomaly detection engine that tracks deviation from the predicted behaviour of the system. Chi-squared fitness test is used to check if the current states are within the normal bounds of operation. We identify the vulnerability in using static and dynamic threshold detectors which are directly affected by day-ahead demand prediction algorithms that have not been fully evolved yet. Finally, we use some of the widely used machine learning based anomaly detection algorithms to overcome the drawbacks of model-based algorithms.

Paper Citation

in Harvard Style

Neupane A. and Sun W. (2023). Exploring False Demand Attacks in Power Grids with High PV Penetration. In Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-624-8, pages 124-134. DOI: 10.5220/0011695800003405

in Bibtex Style

@conference{icissp23,
author={Ashish Neupane and Weiqing Sun},
title={Exploring False Demand Attacks in Power Grids with High PV Penetration},
booktitle={Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2023},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011695800003405},
isbn={978-989-758-624-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Exploring False Demand Attacks in Power Grids with High PV Penetration
SN - 978-989-758-624-8
AU - Neupane A.
AU - Sun W.
PY - 2023
SP - 124
EP - 134
DO - 10.5220/0011695800003405