3,728 research outputs found
Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators
Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious
targets set for the near future, the management of large EV fleets must be seen
as a priority. Specifically, we study a scenario where EV charging is managed
through self-interested EV aggregators who compete in the day-ahead market in
order to purchase the electricity needed to meet their clients' requirements.
With the aim of reducing electricity costs and lowering the impact on
electricity markets, a centralised bidding coordination framework has been
proposed in the literature employing a coordinator. In order to improve privacy
and limit the need for the coordinator, we propose a reformulation of the
coordination framework as a decentralised algorithm, employing the Alternating
Direction Method of Multipliers (ADMM). However, given the self-interested
nature of the aggregators, they can deviate from the algorithm in order to
reduce their energy costs. Hence, we study the strategic manipulation of the
ADMM algorithm and, in doing so, describe and analyse different possible attack
vectors and propose a mathematical framework to quantify and detect
manipulation. Importantly, this detection framework is not limited the
considered EV scenario and can be applied to general ADMM algorithms. Finally,
we test the proposed decentralised coordination and manipulation detection
algorithms in realistic scenarios using real market and driver data from Spain.
Our empirical results show that the decentralised algorithm's convergence to
the optimal solution can be effectively disrupted by manipulative attacks
achieving convergence to a different non-optimal solution which benefits the
attacker. With respect to the detection algorithm, results indicate that it
achieves very high accuracies and significantly outperforms a naive benchmark
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids
This paper presents a Temporal Graph Neural Network (TGNN) framework for
detection and localization of false data injection and ramp attacks on the
system state in smart grids. Capturing the topological information of the
system through the GNN framework along with the state measurements can improve
the performance of the detection mechanism. The problem is formulated as a
classification problem through a GNN with message passing mechanism to identify
abnormal measurements. The residual block used in the aggregation process of
message passing and the gated recurrent unit can lead to improved computational
time and performance. The performance of the proposed model has been evaluated
through extensive simulations of power system states and attack scenarios
showing promising performance. The sensitivity of the model to intensity and
location of the attacks and model's detection delay versus detection accuracy
have also been evaluated.Comment: 5 pages, 6 figures, accepted at ISGT conference of 202
Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends
Secure data aggregation is an important process that enables a smart meter to perform efficiently and accurately. However, the fault tolerance and privacy of the user data are the most serious concerns in this process. While the security issues of Smart Grids are extensively studied, these two issues have been ignored so far. Therefore, in this paper, we present a comprehensive survey of fault-tolerant and differential privacy schemes for the Smart Gird. We selected papers from 2010 to 2021 and studied the schemes that are specifically related to fault tolerance and differential privacy. We divided all existing schemes based on the security properties, performance evaluation, and security attacks. We provide a comparative analysis for each scheme based on the cryptographic approach used. One of the drawbacks of existing surveys on the Smart Grid is that they have not discussed fault tolerance and differential privacy as a major area and consider them only as a part of privacy preservation schemes. On the basis of our work, we identified further research areas that can be explored
Recommended from our members
Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures
The electricity industry is now at the verge of a new era. An era that promises, through the evolution of the existing electrical grids to Smart Grids, more efficient and effective power management, better reliability, reduced production costs and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the Smart Grid and the Smart Home, which we present as an integral part of the Smart Grid. Based on several scenarios we aim to present some of the most representative threats to the Smart Home / Smart Grid environment. The threats detected are categorized according to specific security goals set for the Smart Home/Smart Grid environment and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes the paper
Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping
This paper proposes a fully decentralized adaptive re-weighted state
estimation (DARSE) scheme for power systems via network gossiping. The enabling
technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which
allows to harness the computation capability of each area (i.e. a database
server that accrues data from local sensors) to collaboratively solve for an
accurate global state. The DARSE scheme mitigates the influence of bad data by
updating their error variances online and re-weighting their contributions
adaptively for state estimation. Thus, the global state can be estimated and
tracked robustly using near-neighbor communications in each area. Compared to
other distributed state estimation techniques, our communication model is
flexible with respect to reconfigurations and resilient to random failures as
long as the communication network is connected. Furthermore, we prove that the
Jacobian of the power flow equations satisfies the Lipschitz condition that is
essential for the GGN algorithm to converge to the desired solution.
Simulations of the IEEE-118 system show that the DARSE scheme can estimate and
track online the global power system state accurately, and degrades gracefully
when there are random failures and bad data.Comment: to appear in IEEE JSA
- …