A Distributed Double-Newton Descent Algorithm for Cooperative Energy Management of Multiple Energy Bodies in Energy Internet

This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single energy body and the optimal energy distribution on the interconnected lines between...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 17; H. 9; S. 5993 - 6003
Hauptverfasser: Li, Yushuai, Gao, David Wenzhong, Gao, Wei, Zhang, Huaguang, Zhou, Jianguo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Communications systems
Consumption
Convergence
Distributed algorithm
Distributed algorithms
Economics
energy body (EB)
Energy consumption
Energy distribution
Energy Internet
energy internet (EI)
Energy management
Informatics
Microgrids
Minimum cost
Newton descent
Pipelines
Pricing
Routers
Smart grids
ISSN:1551-3203, 1941-0050
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Zusammenfassung:This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single energy body and the optimal energy distribution on the interconnected lines between any pair of energy bodies. First, we define the physical and communication structure of the system formed by many energy bodies, each of which is viewed as a multienergy prosumer. Then, a distributed energy management model is proposed to achieve not only maximum profits of overall energy generation and consumption, but also minimum cost of energy delivery. To address this issue, a distributed double-Newton descent (DDND) algorithm is proposed, which possesses two advantages. On the one hand, by employing second-order information, the concept of Newton descent is embedded into the implementation of the proposed algorithm, resulting in faster convergence speed. On the other hand, the proposed algorithm performs in a fully distributed fashion. As a consequence, each participant can locally obtain its optimal operation as well as the global energy market clearing prices; meanwhile, each energy router can locally obtain the optimal exchanged energy with its neighbor energy routers. Moreover, we prove that the proposed DDND algorithm can asymptotically converge to the global optimal point. As a result, the correctness of the DDND algorithm can be guaranteed in theory. Finally, simulation results validate the effectiveness of the proposed algorithm.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3029974