Resilient Penalty Function Method for Distributed Constrained Optimization under Byzantine Attack

Xu, Chentao; Liu, Qingshan; Huang, Tingwen

doi:10.1016/j.ins.2022.02.055

Cited by 25 publications

(10 citation statements)

References 39 publications

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“…To the best of our knowledge, the topic of resilient resource allocation has been addressed in a limited number of papers, including [23], [24], and [25]. In [23] and [24], the authors propose decentralized primal-dual optimization frameworks that incorporate a trusted coordinator connecting all agents. By utilizing the algorithm presented in [23] and [24], the computational burden is effectively distributed among different agents.…”

Section: Introductionmentioning

confidence: 99%

“…In [23] and [24], the authors propose decentralized primal-dual optimization frameworks that incorporate a trusted coordinator connecting all agents. By utilizing the algorithm presented in [23] and [24], the computational burden is effectively distributed among different agents. On the other hand, in [25], agents can cooperate within an undirected, fixed graph without requiring a central coordinator.…”

Section: Introductionmentioning

confidence: 99%

“…However, a notable drawback of these algorithms lies in the limitations imposed on the coupling constraint. For example, the coupling constraint is restricted to only being the average of the decision variables (as seen in [25]), or a function based on the average of the decision variables (as observed in [23] and [24]). In Section III, we elaborate on how the coupling constraint of the BESS planning problem, deviates from the setting proposed in prior works such as [23], [24], and [25].…”

Section: Introductionmentioning

confidence: 99%

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Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Kaheni,

Usai,

Franceschelli

2024

IEEE Trans. Automat. Sci. Eng.

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This paper deals with resilient and privacypreserving control to optimize the daily operation costs of networked Battery Energy Storage Systems (BESS) in a multiagent network vulnerable to various types of cyber-attacks. First, we formulate the optimization problem by defining the objective function and the local and coupling constraints. Next, we introduce a novel resilient decentralized control and optimization algorithm that can mitigate the effects of cyber-attacks, specifically false data injection attacks and hijacking, to enhance the network's resilience. The proposed method is based on filtering out outlier Lagrange multipliers in a suitable dual problem. Our proposed algorithm has two main advantages compared to the existing literature. Firstly, it can solve problems where the coupling constraint is not restricted to the average or a function of the average of decision variables. Secondly, our algorithm extends the well-known dual decomposition and Lagrange multiplier method to the decentralized control problem of BESSs. In the proposed algorithm presented in this paper, only the data relevant to the dual problem is exchanged among the agents. Noticing that the data of the dual problem does not contain any private information, mitigating privacy concerns associated with our proposed algorithm. We formally prove the convergence of our algorithm to a feasible and sub-optimal solution. Additionally, simulations demonstrate the effectiveness of our results.Note to Practitioners-Optimal coordinated control of BESSs increases the power system's reliability and reduces costs. With the expansion of the use of small-scale BESSs in household customers, it is possible to considerably increase the free capacity of power networks by optimally controlling these small BESSs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Kaheni,

Usai,

Franceschelli

2024

IEEE Trans. Automat. Sci. Eng.

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“…Distributed optimization algorithms have been generally used for solving all sorts of problems in the last decade. [1][2][3][4][5][6][7][8][9] A distributed algorithm combined with the equivalent network approximation was proposed to relax the limitations of centralized algorithm. 10 To solve the regional energy optimization problem, a distributed algorithm was designed with fewer information between adjacent energy units and organizations.…”

Section: Introductionmentioning

confidence: 99%

“…Distributed optimization algorithms have been generally used for solving all sorts of problems in the last decade 1‐9 . A distributed algorithm combined with the equivalent network approximation was proposed to relax the limitations of centralized algorithm 10 .…”

Section: Introductionmentioning

confidence: 99%

A trust‐based resilient consensus algorithm for distributed optimization considering node and edge attacks

Liu

2022

Intl J Robust & Nonlinear

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Summary Distributed optimization algorithms have various advantages and wide applications, while the distributed nature also makes them flimsy in the face of external attacks. In this paper two Byzantine attack models are designed, in which the edge attack model is seldom considered before. A resilient distributed consensus algorithm based on the trusted nodes and edges is proposed to defend against the two attack models. The agreement and optimality of the proposed consensus algorithm are guaranteed with theoretical analysis. The simulation results and comparison experiments verify the proposed algorithm to be more effective than the compared one. In the perspective of attackers, which attack model is more recommended is also analysed based on the experiments.

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A MOEA/D approach using two crossover strategies for the optimal dispatches of the combined cooling, heating, and power systems

Zou

Ouyang

2023

Applied Energy

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Resilient Penalty Function Method for Distributed Constrained Optimization under Byzantine Attack

Cited by 25 publications

References 39 publications

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

A trust‐based resilient consensus algorithm for distributed optimization considering node and edge attacks

A MOEA/D approach using two crossover strategies for the optimal dispatches of the combined cooling, heating, and power systems

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