314 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
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use of
decentralized optimization algorithms and secured communications for
coordinating renewable generation (e.g., wind/solar), dispatchable devices
(e.g., coal/gas/nuclear generations), demand response, battery & storage
facilities, and topology optimization. The Alternating Direction Method of
Multipliers (ADMM) has been widely used in the community to address such
decentralized optimization problems and, in particular, the AC Optimal Power
Flow (AC-OPF). This paper studies how machine learning may help in speeding up
the convergence of ADMM for solving AC-OPF. It proposes a novel decentralized
machine-learning approach, namely ML-ADMM, where each agent uses deep learning
to learn the consensus parameters on the coupling branches. The paper also
explores the idea of learning only from ADMM runs that exhibit high-quality
convergence properties, and proposes filtering mechanisms to select these runs.
Experimental results on test cases based on the French system demonstrate the
potential of the approach in speeding up the convergence of ADMM significantly.Comment: 11 page
Analysis of distributed ADMM algorithm for consensus optimization in presence of error
ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to distributed consensus optimization problem results in a fully distributed iterative solution which relies on processing at the nodes and communication between neighbors. Local computations usually suffer from different types of errors, due to e.g., observation or quantization noise, which can degrade the performance of the algorithm. In this work, we focus on analyzing the convergence behavior of distributed ADMM for consensus optimization in presence of additive node error. We specifically show that (a noisy) ADMM converges linearly under certain conditions and also examine the associated convergence point. Numerical results are provided which demonstrate the effectiveness of the presented analysis
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