Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/149047
Title: Coordinated Active Power Dispatch for a Microgrid via Distributed Lambda Iteration
Authors: Jianqiang Hu;Michael Z. Q. Chen;Jinde Cao;Josep M. Guerrero
Year: 2017
Publisher: IEEE
Abstract: A novel distributed optimal dispatch algorithm is proposed for coordinating the operation of multiple micro units in a microgrid, which has incorporated the distributed consensus algorithm in multi-agent systems and the &x03BB;-iteration optimization algorithm in the economic dispatch of power systems. Specifically, the proposed algorithm considers the global active power constraint by adding a virtual pinner and it can deal with the optimization problem with any initial states. That is, it can realize the global optimization and avoid the defect of the initial conditions' sensitivity in the optimization problem. On the other hand, the proposed optimization algorithm can either be used for off-line calculation or be utilized for online operation and has the ability to survive single-point failures and shows good robustness in the iteration process. Numerical studies in a seven-bus microgrid demonstrate the effectiveness of the proposed algorithm.
URI: http://localhost/handle/Hannan/149047
volume: 7
issue: 2
More Information: 250,
261
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7876755.pdf2.84 MBAdobe PDF
Title: Coordinated Active Power Dispatch for a Microgrid via Distributed Lambda Iteration
Authors: Jianqiang Hu;Michael Z. Q. Chen;Jinde Cao;Josep M. Guerrero
Year: 2017
Publisher: IEEE
Abstract: A novel distributed optimal dispatch algorithm is proposed for coordinating the operation of multiple micro units in a microgrid, which has incorporated the distributed consensus algorithm in multi-agent systems and the &x03BB;-iteration optimization algorithm in the economic dispatch of power systems. Specifically, the proposed algorithm considers the global active power constraint by adding a virtual pinner and it can deal with the optimization problem with any initial states. That is, it can realize the global optimization and avoid the defect of the initial conditions' sensitivity in the optimization problem. On the other hand, the proposed optimization algorithm can either be used for off-line calculation or be utilized for online operation and has the ability to survive single-point failures and shows good robustness in the iteration process. Numerical studies in a seven-bus microgrid demonstrate the effectiveness of the proposed algorithm.
URI: http://localhost/handle/Hannan/149047
volume: 7
issue: 2
More Information: 250,
261
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7876755.pdf2.84 MBAdobe PDF
Title: Coordinated Active Power Dispatch for a Microgrid via Distributed Lambda Iteration
Authors: Jianqiang Hu;Michael Z. Q. Chen;Jinde Cao;Josep M. Guerrero
Year: 2017
Publisher: IEEE
Abstract: A novel distributed optimal dispatch algorithm is proposed for coordinating the operation of multiple micro units in a microgrid, which has incorporated the distributed consensus algorithm in multi-agent systems and the &x03BB;-iteration optimization algorithm in the economic dispatch of power systems. Specifically, the proposed algorithm considers the global active power constraint by adding a virtual pinner and it can deal with the optimization problem with any initial states. That is, it can realize the global optimization and avoid the defect of the initial conditions' sensitivity in the optimization problem. On the other hand, the proposed optimization algorithm can either be used for off-line calculation or be utilized for online operation and has the ability to survive single-point failures and shows good robustness in the iteration process. Numerical studies in a seven-bus microgrid demonstrate the effectiveness of the proposed algorithm.
URI: http://localhost/handle/Hannan/149047
volume: 7
issue: 2
More Information: 250,
261
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7876755.pdf2.84 MBAdobe PDF