A review of multi agent based decentralised energy management issues

Brazier, Frances M. T.; Poutré, Han La; Abhyankar, A. R.; Saxena, Kritika; Singh, S. N.; Tomar, Krishna

doi:10.1109/energyeconomics.2015.7235106

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…Hence, MASs can make a possible environment for SG's players -e.g. electrical generation, consumers, system operators, aggregators, etc.-to act autonomously and communicate with each other [2].…”

Section: Introductionmentioning

confidence: 99%

A Review of Multi-agent Based Energy Management Systems

Gazafroudi

Paz

Prieto‐Castrillo

et al. 2017

Advances in Intelligent Systems and Computing

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Section: Introductionmentioning

confidence: 99%

A Review of Multi-agent Based Energy Management Systems

Gazafroudi

Paz

Prieto‐Castrillo

et al. 2017

Advances in Intelligent Systems and Computing

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“…As power systems increase in size and complexity, centralized control becomes more challenging [3]. This is particularly the case due to the increased introduction of distributed renewable energy resources (DER) [9]. As a consequence of increased flexibility of the demand-side for choosing supply sources, traditional, centrally controlled nature of energy systems management is changing to coordination among a large number of supply sources and responsive loads [19].…”

Section: Introductionmentioning

confidence: 99%

“…To handle this complexity, different types of decentralized organization of power systems that enhance local control with respect to power supply and demand [4,5,15] have been proposed. Methods for minimizing centralized control are proposed in [4,5,9]. These methods use complex adaptive systems to represent power systems, and intelligent agents as their components.…”

Section: Introductionmentioning

confidence: 99%

Dynamic, self-organized clusters as a means to supply and demand matching in large-scale energy systems

Čaušević

Warnier

Brazier

2017

2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)

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CopyrightOther than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policyPlease contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim. Centralized management of power systems is becoming more challenging due to the increased introduction of distributed renewable energy resources, along with demand increase and aging infrastructures. To address these challenges, this paper proposes new mechanisms for decentralized energy management. Based on self-organization of consumers, prosumers and producers into virtual groups, called clusters, supply and demand of electricity is locally matched. Distributed multi-agent systems are used as a way to represent virtual cluster members. The mechanisms are illustrated, and static and dynamic virtual clusters are compared. Dynamic reconfiguration is achieved by varying the time periods for which clustering is performed. The proposed clustering mechanisms demonstrate that large-scale centralized energy systems can operate in a decentralized fashion when only local information is available.

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“…The main idea of multi-agent control method is dividing the complex large-scale system into several subsystems, which have the features of autonomy and communicating with each other. An agent has several characteristics to be intelligent [150,151]:…”

Section: B Mas-based Distributed Coordination Control Of the Microgrmentioning

confidence: 99%

“…The overviews of the application of multi-agent system in power systems are proposed in [122,123]. The distributed security of the multi-agent systems is presented in [124].…”

Section: Introductionmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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A review of multi agent based decentralised energy management issues

Cited by 15 publications

References 20 publications

A Review of Multi-agent Based Energy Management Systems

A Review of Multi-agent Based Energy Management Systems

Dynamic, self-organized clusters as a means to supply and demand matching in large-scale energy systems

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

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