Parallel and Distributed Optimization Method With Constraint Decomposition for Energy Management of Microgrids Article Swipe
Qiang Li
,
Yuexi Liao
,
Kunming Wu
,
Leiqi Zhang
,
Jiayang Lin
,
Minyou Chen
,
Josep M. Guerrero
,
Derek Abbott
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tsg.2021.3097047
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tsg.2021.3097047
Energy management in power systems is a thorny optimization problem. With the sizes of systems rising, centralized optimization methods are restricted by their complexities of communications, while distributed optimization methods have emerged as a powerful tool for dealing with increasingly complex systems. However, convergence rates of some widely used distributed optimization methods, such as the standard alternating direction method of multipliers (ADMM), still have room for improvement. In this paper, a parallel and distributed optimization method for energy management of microgrids (MGs) is proposed to boost the convergence rate without sacrificing the accuracy of the optima, in which agents calculate, exchange and update in parallel. At first, a decomposition method is presented, where the objective functions and constraints of an original optimization problem with separable variables are decomposed into local objective functions and constraints for agents, which is the key to our method. Further, agents solve their local optimization problems independently and then exchange determined optima with their neighbors. Finally, the method is evaluated to solve economic dispatch with demand response for microgrids. The simulation results show that compared to the standard ADMM, for a given accuracy, the number of iterations in our method is only one third or even less than that of ADMM. Furthermore, our method can minimize the cost functions of distributed generation on supply side and maximize the profit functions of flexible loads on the demand side.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tsg.2021.3097047
- OA Status
- green
- Cited By
- 50
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3184545914
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3184545914Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tsg.2021.3097047Digital Object Identifier
- Title
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Parallel and Distributed Optimization Method With Constraint Decomposition for Energy Management of MicrogridsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-08-24Full publication date if available
- Authors
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Qiang Li, Yuexi Liao, Kunming Wu, Leiqi Zhang, Jiayang Lin, Minyou Chen, Josep M. Guerrero, Derek AbbottList of authors in order
- Landing page
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https://doi.org/10.1109/tsg.2021.3097047Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://vbn.aau.dk/da/publications/607de71b-ca42-4e19-ba62-1ea2012ee499Direct OA link when available
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Mathematical optimization, Optimization problem, Computer science, Convergence (economics), Decomposition method (queueing theory), Local optimum, Distributed generation, Power (physics), Mathematics, Economic growth, Quantum mechanics, Discrete mathematics, Economics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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50Total citation count in OpenAlex
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2025: 14, 2024: 11, 2023: 11, 2022: 10, 2021: 4Per-year citation counts (last 5 years)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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