Application of Multi‐agent Particle Swarm Algorithm in Distribution Network Reconfiguration

Dasong, Shu; Huang, Zhihui; Li, Junye; Zou, Xiaoyang

doi:10.1049/cje.2016.10.015

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…By adopting this method, the optimization scheduling scheme can be greatly improved under the uncertain environment in MGC. In [59] [60,61]. Each particle represents an agent, which shows the advantage of realizing the optimal state rapidly so as to share the information with the global agents.…”

Section: A Modeling Methods For Distributed Multi-agent Systemmentioning

confidence: 99%

“…Graph theoretic topology model [45][46][47][48][49][50][51] ·Simple model structure ·High redundancy and easy to expand ·Robustness is greatly affected by graph Non-cooperative dynamic game model [52][53][54] ·Each agent can achieve the optimal balanced state ·Algorithm is complex and time-consuming Genetic algorithm [55][56][57] ·High prediction accuracy ·Fast convergence ·Scalability and parallelism operation ·Most of the parameters depend on experience ·Slow dynamic response PSO algorithm [58][59][60][61] ·Simple model structure ·Fast computation speed ·Efficient economic scheduling ·Improve the frequency and voltage of MG ·Not handling the discrete optimization problems…”

Section: Merits Drawbacksmentioning

confidence: 99%

“…Distributed multi-agent control method has been widely used to establish optimal model to enhance reliability and energy management, optimization, and improve the performance of ancillary services [44]. Various methods for system modelling, including topology models and mathematic models, in MAS have been reviewed in this paper, such as the graph topology model [45][46][47][48][49][50][51], non-cooperative game model [52][53][54], genetic algorithm [55][56][57] and particle swarm optimization (PSO) [58][59][60][61], etc. Moreover, consensus protocol is the rule of interaction among multiple agents in the complex systems, which describes the process of information interaction among agents and their neighbors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

346

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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Section: A Modeling Methods For Distributed Multi-agent Systemmentioning

confidence: 99%

Section: Merits Drawbacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

346

182

View full text Add to dashboard Cite

show abstract

“…Other mathematical models such as particle swarm optimization (PSO) algorithm [101], [102] and genetic algorithm have been implemented to apply for MAS. PSO provides effective economic scheduling and fast computation speed, but unable to handle discrete optimization problems.…”

Section: E Mas Based Distributed Coordinated Control Techniquementioning

confidence: 99%

A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation

et al. 2021

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The present research investigations in power management fraternity are focused towards the realization of smart microgrid (MG) technologies. Due to intrinsic advantages of Direct Current (DC) system in terms of compatibility with power generation sources, modern loads and storage devices, DC MG has becoming popular over Alternate Current (AC) system. A secondary voltage and current control schemes of DC MG system are mainly based on the distributed consensus control of Multi-agent system (MAS) to balance generation and the demand. The basic concern of the cooperative control of MAS is consensus, which is to design a suitable control law such that the output of all agents can achieve synchronization. The distributed consensus algorithm requires much less computational power and information exchange in between the neighbor's agent. Meanwhile the other controllers such as model predictive control (MPC) requires accurate dynamic models with high computational cost and it suffers from lack of flexibility. The hierarchical consensus control technique is classified into three levels according to their features, namely primary, secondary, and tertiary. MAS is a popular distributed platform to efficiently manage the secondary control level for synchronization and communication among the power converters in autonomous MGs. In this article, various primary control techniques for local voltage control, voltage restoration in the secondary control level and tertiary control for energy management techniques are discussed. With this, the key emphasis to reduce the voltage deviation and disturbances in a heterogeneous DC MG network solutions are discussed. Furthermore, to analyze the system response and the charging and discharging characteristics of the battery unit, the developed second order heterogeneous consensus controller is compared with the traditional homogeneous consensus control and droop control methods. Finally a detailed discussion on simulation case study using heterogeneous consensus control method over the traditional methods are provided using MATLAB/Simulink platform.

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“…Relevant HSI characteristics include geological features, diverse vegetation, rainfall, and temperature, all of which are considered Suitability index variables (SIV). BBO has higher search precision, faster convergence speed and better stability than other intelligent optimization algorithms such as GA [13] and PSO [14,15] . Many papers have applied BBO to continuous optimization problems [16−21] , but few have addressed the flow shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

An Effiective Biogeography‐Based Optimization Algorithm for Flow Shop Scheduling with Intermediate Buffiers

Liu¹,

Wang²,

Zhang³

2018

Chin. j. electron.

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Application of Multi‐agent Particle Swarm Algorithm in Distribution Network Reconfiguration

Cited by 14 publications

References 5 publications

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

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

A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation

An Effiective Biogeography‐Based Optimization Algorithm for Flow Shop Scheduling with Intermediate Buffiers

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