Abstract
Emergent communication systems (i.e., 5G/B5G (6G)) seem appealing to the public owing to their desirable properties such as faster speeds and higher capacities. However, the associated dramatic increase in data traffic in today’s Internet of things (IoT) applications vastly surpass the capacity growth rates of new wireless communication technologies. There was an estimate of over 10 billion IoT connected devices in 2021, and this is projected to reach a high of 25 billion by 2030. As a result, the deployment and management of communication networks require further attention to achieve conflicting objectives of spectrum efficiency, energy efficiency, and quality of service (QoS). To address the problem of resource management in 5G networks, multi-access edge computing (MEC) is commonly used. MEC enables applications to benefit from localized communication, processing, and management, which dramatically decreases the service response latency, reduces the traffic load on the core network, and improves context-awareness. On the contrary, a major drawback to be addressed is the task assignment to the MEC, as well as the resulting uneven energy consumption of MEC. The reliability and stability of the MG energy supply is contingent on the energy generation of renewable and nonrenewable energy sources. Thus, strong coordination between the MEC network's energy consumption and the microgrid energy generation is required. This study presents a case study on the use of MEC in microgrids (MG) operations, where an energy management framework is developed for MEC enabled microgrids considering the physical constraints of both systems. This study utilizes a dynamic data driven approach to tailor the network according to the needs dictated by the actors and provides a holistic energy supply plan to meet the requirements of an MEC network to maintain optimal operation of the network.