A Finite-Time Distributed Optimization Algorithm for Economic Dispatch in Smart Grids

The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 51; no. 4; pp. 2068 - 2079
Main Authors: Mao, Shuai, Dong, Ziwei, Schultz, Paul, Tang, Yang, Meng, Ke, Dong, Zhao Yang, Qian, Feng
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Consensus
Convergence
Cost function
distributed optimization algorithm
economic dispatch
finite time
Generators
Local optimization
Mathematical analysis
Optimization algorithms
Power dispatch
Smart grid
Smart grids
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2931846