Multi‐objective interval planning for 5G base station virtual power plants considering the consumption of photovoltaic and communication flexibility
Abstract Large‐scale deployment of 5G base stations has brought severe challenges to the economic operation of the distribution network, furthermore, as a new type of adjustable load, its operational flexibility has provided a potential way to promote the consumption and utilization of photovoltaic....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/stg2.12178 |
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| Summary: | Abstract Large‐scale deployment of 5G base stations has brought severe challenges to the economic operation of the distribution network, furthermore, as a new type of adjustable load, its operational flexibility has provided a potential way to promote the consumption and utilization of photovoltaic. In this paper, a multi‐objective interval collaborative planning method for virtual power plants and distribution networks is proposed. First, on the basis of in‐depth analysis of the operating characteristics and communication load transmission characteristics of the base station, a 5G base station of virtual power plants participating in the cellular respiratory demand response model is constructed. In view of the inherent contradiction between system economy and environmental performance, a multi‐objective interval optimization model for collaborative planning of virtual power plants and distribution networks is established with the lowest system investment and operating costs and the lowest carbon emissions as the optimization goals. The established model is transformed into a deterministic optimization problem, which is solved by NSGA‐II algorithm. The modified IEEE‐33 node system is used in the case analysis to analyse the impact of different planning schemes and response characteristics on the system economy. The calculation results verify the effectiveness of the proposed method. |
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| ISSN: | 2515-2947 |