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Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Energy Theft in Smart Grids
T2 - A Survey on Data-Driven Attack Strategies and Detection Methods
AU - Althobaiti, Ahlam
AU - Jindal, Anish
AU - Marnerides, Angelos
AU - Roedig, Utz
PY - 2021/11/30
Y1 - 2021/11/30
N2 - The convergence of legacy power system components with advanced networking and communication facilities have led towards the development of smart grids. Smart grids are envisioned to be the next generation innovative power systems, guaranteeing resilience, reliability and sustainability and to facilitate energy production, distribution and management. Nonetheless, the development of such systems entails challenges covering a broad spectrum ranging from operational management up to data-driven power accounting and network security. Given the highly distributed properties of the modern grid, energy theft can now be observed at various transmission and distribution levels. Apart from the financial gain for a malicious actor, energy theft can also affect critical grid processes with a direct impact on its overall resilience and safety. This survey reviews recent energy theft strategies as well as detection methods from a data-driven perspective. By considering various operational and functional layers within modern smart grids we critically assess how energy theft can be formulated. Moreover, we provide an overview of the grid demand, supply and control chain with a focus on energy theft and associated security flaws that currently exist in the smart grid ecosystem. Different attack detection models for theft detection in the smart grid are categorized. Lastly, we discuss various open issues in the scope of data-driven energy theft detection methods and provide future directions to carry out research in this field.
AB - The convergence of legacy power system components with advanced networking and communication facilities have led towards the development of smart grids. Smart grids are envisioned to be the next generation innovative power systems, guaranteeing resilience, reliability and sustainability and to facilitate energy production, distribution and management. Nonetheless, the development of such systems entails challenges covering a broad spectrum ranging from operational management up to data-driven power accounting and network security. Given the highly distributed properties of the modern grid, energy theft can now be observed at various transmission and distribution levels. Apart from the financial gain for a malicious actor, energy theft can also affect critical grid processes with a direct impact on its overall resilience and safety. This survey reviews recent energy theft strategies as well as detection methods from a data-driven perspective. By considering various operational and functional layers within modern smart grids we critically assess how energy theft can be formulated. Moreover, we provide an overview of the grid demand, supply and control chain with a focus on energy theft and associated security flaws that currently exist in the smart grid ecosystem. Different attack detection models for theft detection in the smart grid are categorized. Lastly, we discuss various open issues in the scope of data-driven energy theft detection methods and provide future directions to carry out research in this field.
KW - Energy theft
KW - Data-driven methods
KW - Smart grid
KW - Cybersecurity
U2 - 10.1109/ACCESS.2021.3131220
DO - 10.1109/ACCESS.2021.3131220
M3 - Journal article
VL - 9
SP - 159291
EP - 159312
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -