Hybrid quantum-classical stochastic programming for co-planning 5G base
This study proposes a hybrid quantum-classical two-stage stochastic programming approach for the co-planning of BSs and PVs in urban communities.
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Given the significant increase in electricity consumption in 5G networks, which contradicts the concept of communication operators building green communication networks, the current research focus on 5G base stations is mainly on energy-saving measures and their integration with optimized power grid operation.
The 5G communication base station can be regarded as a power consumption system that integrates communication, power, and temperature coupling, which is composed of three major pieces of equipment: the communication system, energy storage system, and temperature control system.
This model encompasses numerous energy-consuming 5G base stations (gNBs) and their backup energy storage systems (BESSs) in a virtual power plant to provide power support and obtain economic incentives, and develop virtual power plant management functions within the 5G core network to minimize control costs.
To properly examine an energy-optimised network, it is very crucial to select the most suitable EE metric for 5G networks. EE is the ratio of transmitted bits for every joule of energy expended. Therefore, while measuring it, different perspectives need to be considered such as from the network or user's point of view.
This study proposes a hybrid quantum-classical two-stage stochastic programming approach for the co-planning of BSs and PVs in urban communities.
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In this paper, hybrid energy utilization was studied for the base station in a 5G network. To minimize AC power usage from the hybrid energy system and minimize solar energy waste, a Markov decision
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