A Consensus-Based Distributed Computational Intelligence Technique for Real-Time Optimal Control in Smart Distribution Grids

Utkarsh, Kumar; Trivedi, Anupam; Srinivasan, Dipti; Reindl, Thomas

doi:10.1109/tetci.2016.2635130

Cited by 60 publications

(29 citation statements)

References 33 publications

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“…There are problems in communication and computation of the centralized systems with the high number of distributed generators (DG) and controllable loads (CL). To alleviate the same problem, authors in [90] proposed consensus protocols to address load fluctuations in DG. The system is tested on two different operating systems with a network of 30 nodes and 119 nodes, respectively.…”

Section: B Role Of Bc and Consensus Mechanisms In Sgmentioning

confidence: 99%

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

et al. 2020

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Modern industry 4.0 applications are shifting towards decentralized automation of computing and cyber-physical systems (CPS), which necessitates building a robust, secure, and efficient system that performs complex interactions with other physical processes. To handle complex interactions in CPS, trust and consensus among various stakeholders is a prime concern. In a similar direction, consensus algorithms in blockchain have evolved over the years that focus on building smart, robust, and secure CPS. Thus, it is imperative to understand the key components, functional characteristics, and architecture of different consensus algorithms used in CPS. Many consensus algorithms exist in the literature with a specified set of functionalities, performance, and computing services. Motivated from these facts, in this survey, we present a comprehensive analysis of existing state-of-the-art consensus mechanisms and highlight their strength and weaknesses in decentralized CPS applications. In the first part, we present the scope of the proposed survey and identify gaps in the existing surveys. Secondly, we present the review method and objectives of the proposed survey based on research questions that address the gaps in existing studies. Then, we present a solution taxonomy of decentralized consensus mechanisms for various CPS applications. Then, open issues and challenges are also discussed in deploying various consensus mechanisms in the CPS with their merits and demerits. The proposed survey will act as a road-map for blockchain developers and researchers to evaluate and design future consensus mechanisms, which helps to build an efficient CPS for industry 4.0 stakeholders.

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Section: B Role Of Bc and Consensus Mechanisms In Sgmentioning

confidence: 99%

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, in cluster‐based microgrids GTA is complex to utilize. A consensus‐based real‐time distributed computational intelligence technique (DCI) is applied to address the issues related to GTA 143 . DCI control strategy shares the information related to estimated states of DERs and ESSs and not the control variable, so grid security is achieved.…”

Section: Co‐operative Control Strategies For Ess and Dermentioning

confidence: 99%

A review on control strategies for microgrids with distributed energy resources, energy storage systems, and electric vehicles

Nikam¹,

Kalkhambkar²

2020

Int Trans Electr Energ Syst

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“…To settle a combinatorial optimisation problem over extensive distribution networks, previous researches proposed three broad structures including central structure, distributed structure, and decentralised structure [16–20]. The centralised or coordinated control methods are as a special case of the optimal power flow which aims to minimise the objective and assume that the network details are available by accurate measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The local agents are appointed to disseminate a set of information states such as average voltage, frequency, or output power of PV stations. Distributed control strategies are able to reduce the computational burden effectively and more effective for online applications with frequent updating requirements of control settings using limited communication links [19, 20]. Although the calculations are done in a distributed manner, each agent must run a metaheuristic optimisation algorithm separately that increases the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Distributed‐hierarchical control strategy to coordinate peer‐to‐peer energy transactions and node voltages at low voltage distribution networks

Fazeli

2020

IET Smart Grid

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A Consensus-Based Distributed Computational Intelligence Technique for Real-Time Optimal Control in Smart Distribution Grids

Cited by 60 publications

References 33 publications

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

A review on control strategies for microgrids with distributed energy resources, energy storage systems, and electric vehicles

Distributed‐hierarchical control strategy to coordinate peer‐to‐peer energy transactions and node voltages at low voltage distribution networks

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