Consensus + Innovations Approach for Distributed Multiagent Coordination in a Microgrid

Hug, Gabriela; Kar, Soummya; Wu, Chenye

doi:10.1109/tsg.2015.2409053

Cited by 312 publications

(183 citation statements)

References 23 publications

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“…When the cost function of the generator is expressed as a quadric equation, the economic optimal output of the DG is determined at a point where the incremental cost of the generation cost function of the DGs is equalized and the generation and consumption match up [18,19].…”

Section: Control Strategy In the Microgrid Levelmentioning

confidence: 99%

“…In this research, the MMG simulation model is constructed based on an IEEE 13 Node Test Feeder [20] and the parameters of the DGs in a single MG are utilized in [18]. The Metropolis method is applied in the simulation to find the weighted factor of the consensus algorithm.…”

Section: Case Studymentioning

confidence: 99%

“…To relieve the burden of the central control center, studies regarding the distributed method wherein each element controls itself through communication with the neighboring components have been proposed in many papers. Mainly, the distributed control strategy based on a consensus algorithm has been introduced in several studies [12][13][14][15][16][17][18]. In previous studies, they manage the single level system using consensus algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

Lee¹,

Choi²,

Lee

et al. 2017

Energies

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Microgrids (MGs) in which power generation and consumption occur locally have gained prominence, and MG demonstration tests have been widely carried out. In accordance with the increase in the number of MG installations, studies regarding the cooperative control of multiple MGs are proceeding in various forms. In this paper, the distributed control strategy of a multi-microgrid (MMG) is proposed. Distributed control is the method in which agents of the electric power facility autonomously control their facility through communication with the neighboring agents only. In this process, a consensus algorithm is utilized to obtain the global information required to control the overall system. In this distributed control strategy, a single MG is operated at an optimal economic point using the equality incremental cost constraints while maintaining the balance between the generation and demand. The control strategy of a MMG is that the flow of the point of common coupling (PCC) is maintained at a particular value needed by the utility and the internal change in power is distributed to the MGs according to their reserves. The proposed algorithm is verified in the MG level and the MMG level through a simulation model using PSCAD/EMTDC software in the C language.

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Section: Control Strategy In the Microgrid Levelmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

Lee¹,

Choi²,

Lee

et al. 2017

Energies

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“…• The energy management does not take QoL into account (e.g., in [28][29][30]): the formulations consider cost-based functions or assume linear / quadratic dynamic models of power usage.…”

Section: Demand Management From the Demand-sidementioning

confidence: 99%

Energy Management in Prosumer Communities: A Coordinated Approach

2016

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Abstract:The introduction of uncontrollable renewable energy is having a positive impact on our health, the climate, and the economy, but it is also pushing the limits of the power system. The main reason for this is that, in any power system, the generation and consumption must match each other at all times. Thus, if we want to further introduce uncontrollable generation, we need a large ability to manage the demand. However, the ability to control the power consumption of existing demand management approaches is limited, and most of these approaches cannot contribute to the introduction of reneweables, because they do not consider distributed uncontrolled consumption and generation in the control. Furthermore, these methods do not allow users to exchange or jointly manage their power generation and consumption. In this context, we propose an augmented energy management model for prosumers (i.e., producer and consumer). This model considers controlled and uncontrolled generation and consumption, as well as the prosumer's ability (i) to plan the intended power consumption; and (ii) to manage real-time deviations from the intended consumption. We apply this model to the energy management of prosumer communities, by allowing the prosumers to coordinate their power consumption plan, to manage the deviations from the intended consumption, and to help each other by compensating deviations. The proposed approach seeks to enhance the power system, and to enable a prosumer society that takes account social and environmental issues, as well as each prosumer's quality of life.

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“…A similar but modified consensus algorithm is used in [20] for optimal resource management in a MG, but the primary control is not detailed. Another revision on the consensus algorithm is used in [21] plus a multistep optimization which considers ESS for EA, however the primary control is again not mentioned.…”

Section: Literature Reviewmentioning

confidence: 99%

Fully Decentralized Multi-Agent System for Optimal Microgrid Control

Azevedo¹

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Consensus + Innovations Approach for Distributed Multiagent Coordination in a Microgrid

Cited by 312 publications

References 23 publications

Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

Energy Management in Prosumer Communities: A Coordinated Approach

Fully Decentralized Multi-Agent System for Optimal Microgrid Control

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