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Collaborative optimization of distribution network and 5G base stations considering its communication load migration and energy storage dynamic backup flexibility
•A 5G BS model considering communication load migration and energy storage dynamic backup is established.•A coordinated optimization model of the interacted distribution and 5G communication networks is proposed.•An improved ADMM-based distributed algorithm is designed for the coordinated optimal op...
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Published in: | International journal of electrical power & energy systems 2024-09, Vol.160, p.110124, Article 110124 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | •A 5G BS model considering communication load migration and energy storage dynamic backup is established.•A coordinated optimization model of the interacted distribution and 5G communication networks is proposed.•An improved ADMM-based distributed algorithm is designed for the coordinated optimal operation of two networks.•The e ffectiveness of the proposed model and algorithm w as validated in the case study .
5G base stations have experienced rapid growth, making their demand response capability non-negligible. However, the collaborative optimization of the distribution network and 5G base stations is challenging due to the complex coupling, competing interests, and information asymmetry among different stakeholders. In this paper, a distributed collaborative optimization approach is proposed for power distribution and communication networks with 5G base stations. Firstly, the model of 5G base stations considering communication load demand migration and energy storage dynamic backup is established. Afterward, a collaborative optimal operation model of power distribution and communication networks is designed to fully explore the operation flexibility of 5G base stations, and then an improved distributed algorithm based on the ADMM is developed to achieve the collaborative optimization equilibrium. Finally, the effectiveness of the proposed distributed collaborative optimization model is validated by a modified IEEE 33-bus power distribution and communication networks with 5G base stations. |
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ISSN: | 0142-0615 |
DOI: | 10.1016/j.ijepes.2024.110124 |