Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104722
Title: Distributed economic dispatch control via saddle point dynamics and consensus algorithms
Authors: Bai, Lu
Ye, Maojiao
Sun, Chao
Hu, Guoqiang
Keywords: Distributed Control
Economic Dispatch
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Bai, L., Ye, M., Sun, C., & Hu, G. (2019). Distributed economic dispatch control via saddle point dynamics and consensus algorithms. IEEE Transactions on Control Systems Technology, 27(2), 898-905. doi:10.1109/TCST.2017.2776222
Series/Report no.: IEEE Transactions on Control Systems Technology
Abstract: In this brief, a distributed control algorithm is proposed to solve the economic dispatch problem. Without a central control unit, the generators work collaboratively to minimize the generation cost while balancing the supply and demand. The proposed method is based on consensus protocols and the saddle point dynamics. The consensus protocols are employed to estimate the global information in a distributed fashion, and the saddle point dynamics are leveraged to search for the optimal solution of the economic dispatch problem. By utilizing Lyapunov stability analysis, exponential stability of the optimal solution is derived if the capacity limits of the generators are not considered; with the capacity limits, practical stability of the optimal solution is obtained. No global information is needed in the proposed method, and the requirement on initial conditions of the state variables is mild. Several case studies on the IEEE 9-bus and IEEE 118-bus systems are presented to demonstrate the effectiveness of the proposed algorithms.
URI: https://hdl.handle.net/10356/104722
http://hdl.handle.net/10220/48605
ISSN: 1063-6536
DOI: 10.1109/TCST.2017.2776222
Schools: School of Electrical and Electronic Engineering 
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCST.2017.2776222
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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