Distributed Renewable Energy Sources (DRES) are considered as instrumental within modern smart grids and more broadly to the various ancillary services contained within the energy trading market. Thus, the adequate power production profiling and forecasting of DRES deployments is of vital importance such as to support various grid optimisation and accounting processes. The variety of DRES installation companies in conjunction with the diversity of ownership on DRES machinery, controller firmware and Supervisory Control and Data Acquisition (SCADA) software leads to cases where centralised SCADA measurements are not entirely available or are provided under a subscription-based model. In this work, we consider this pragmatic scenario and introduce a SCADA-agnostic approach that utilises freely available weather measurements for explicitly profiling and forecasting power generation as produced in real wind turbine deployments. For this purpose, we leverage various machine learning (ML) libraries to demonstrate the applicability of our system and further compare it with forecasting outputs obtained when using SCADA measurements. Through this study, we demonstrate a viable and exogenous profiling solution achieving similar accuracy with SCADA-based schemes under much lower computational costs.
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 datadriven 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.
Energy theft is an old and multifaceted phenomenon affecting our society on a global scale from both an operational as well as from a monetary perspective. The relatively recent decentralisation of the grid infrastructure with the integration of Distributed Renewable Energy Resources (DRES) in synergy with the widely adopted demand-response business model, has undoubtedly broadened the spectrum of attack surface enabling energy theft. Conventional data-driven energy theft detection schemes have a strong dependency on assessing the spatio-temporal patterns of SCADA measurements aggregated at the Distribution System Operator (DSO) or Transmission System Operator (TSO) with minimal consideration of the intrinsic weather patterns related to individual DRES deployments. Hence, theft scenarios instrumented by DRES owners consuming the energy they produce (i.e., prosumers) can effectively be stealthy and hard to spot. Therefore, in this work we introduce a data-driven, SCADA-agnostic energy theft detection framework explicit to DRES-based scenarios. We provide a comprehensive formalisation of a DRES-based theft attack model and further assess the performance of our framework by utilising and relating freely available third-party weather measurements with real solar and wind turbine deployments in Australia and France. Evidently, our proposed framework yields an energy theft detection accuracy rate of over 98% with optimal computational costs. Thus, reasonably addressing the highly demanding requirements of low-cost and accurate real-time energy theft detection in modern power grids. CCS CONCEPTS• Hardware → Smart grid; • Security and privacy → Systems security; • Computing methodologies → Machine learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.