Agent-Based Distributed Control Schemes for Distributed Energy Storage Systems Under Cyber Attacks

Sharma, Desh Deepak; Singh, S. N.; Lin, Jeremy; Foruzan, Elham

doi:10.1109/jetcas.2017.2700947

Cited by 32 publications

(27 citation statements)

References 24 publications

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“…Noncompliance of some agents in a network that applies a distributed approach has been discussed in some papers. 10,11 For instance, a secure dual-decomposition-based DMPC, in which each agent should monitor two neighbors that provide extreme control input values and disregard these extreme values, has been proposed. 10 Furthermore, a cyber-attack problem of a consensus-based distributed control scheme for distributed energy storage systems has also been addressed, 11 where the approach involves a fuzzy-logic-based detection and a consensus-based leader-follower distributed control scheme.…”

Section: Introductionmentioning

confidence: 99%

“…10,11 For instance, a secure dual-decomposition-based DMPC, in which each agent should monitor two neighbors that provide extreme control input values and disregard these extreme values, has been proposed. 10 Furthermore, a cyber-attack problem of a consensus-based distributed control scheme for distributed energy storage systems has also been addressed, 11 where the approach involves a fuzzy-logic-based detection and a consensus-based leader-follower distributed control scheme. Related to the cyber-security issue of cyber-physical systems, the work of Pasqualetti et al 12 provides a mathematical framework for attack detection and monitoring, particularly for deterministic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in this paper, we also provide a convergence analysis of the attack identification and mitigation method. Since the control approach considered in this paper is based on MPC, it is more related to the work of Velarde et al 10 than that of Sharma et al 11 However, different from the method proposed by Velarde et al, 10 our methodology deals with the attacks by computing control inputs that are robust with respect to such attacks. Furthermore, our approach is also able to identify the adversarial agents in certain cases and can also deal with more general systems in which there are more than one adversarial agent in the network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resilient distributed model predictive control for energy management of interconnected microgrids

Ananduta

Maestre

Ocampo‐Martínez

et al. 2019

Optim Control Appl Methods

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Summary Distributed energy management of interconnected microgrids that is based on model predictive control (MPC) relies on the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might perform one type of adversarial actions (attacks) and they do not comply with the decisions computed by performing a distributed MPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we formulate the economic dispatch problem, taking into account the attacks as a chance‐constrained problem, and employ a two‐step randomization‐based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed‐integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resilient distributed model predictive control for energy management of interconnected microgrids

Ananduta

Maestre

Ocampo‐Martínez

et al. 2019

Optim Control Appl Methods

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“…Furthermore, [8] addresses a cyber-attack problem of a consensus-based distributed control scheme for distributed energy storage systems. The proposed approach in [8] includes a fuzzy-logic-based detection and a consensus based leader-follower distributed control scheme. Related to the cyber-security issue of cyber physical systems, in particular power systems, the work of [9] provides a mathematical framework for attack detection and monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, we also provide a decentralized sub-optimality certificate of our proposed approach. Differently from [8], we consider a DMPC scheme to act as an energy management. Thus, our work is more related to [7] than the approaches discussed in [8], [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Resilient Distributed Energy Management for Systems of Interconnected Microgrids

Ananduta

Maestre

Ocampo‐Martínez

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.

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A cyber‐secure model to decentralized co‐expansion planning of gas and electricity networks

2021

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Flexibilities offered by gas-fired power plants have strongly increased their usage in power systems. Increasing the penetration rate of gas-fired power plants has made it necessary to coordinate gas network expansion planning with electricity network expansion planning. In this paper, a decentralized model for expansion of gas and electricity networks is introduced that coordinates investment in these networks while taking into account their privacy.Alternating direction method of multipliers is used to decompose the centralized gas and electricity expansion problem into two separate expansion prob-

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Agent-Based Distributed Control Schemes for Distributed Energy Storage Systems Under Cyber Attacks

Cited by 32 publications

References 24 publications

Resilient distributed model predictive control for energy management of interconnected microgrids

Resilient distributed model predictive control for energy management of interconnected microgrids

Resilient Distributed Energy Management for Systems of Interconnected Microgrids

A cyber‐secure model to decentralized co‐expansion planning of gas and electricity networks

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