Sci. Tech. Energ. Transition
Volume 78, 2023
|Number of page(s)
|13 October 2023
Resource allocation model for cloud-fog-based smart grid
Departemnt of Electrical and Electronics Engineering, International Burch University, Francuske revolucije bb, Ilidža, 71210 Sarajevo, Bosnia and Herzegovina
2 Department of Telecommunications, Faculty of Electrical Engineering, University of Tuzla, Franjevacka br. 2, 75000 Tuzla, Bosnia and Herzegovina
* Corresponding author: firstname.lastname@example.org
Accepted: 6 September 2023
This paper investigates the allocation model, the flexibility, and the scalability of fully distributed communication architectures for metering systems in smart grids. Smart metering infrastructure aggregates data from Smart Meters (SMs) and sends the collected data to the fog or the cloud data centres to be stored and analysed. The system needs to be scalable and reliable and to respond to increased demand with minimal cost. The problem is to find the optimal distribution of application data among devices, data centres or clouds. The need for support computing at marginal resources, which can be hosted within the building itself or shared within the construction of the complex, has become important over recent years. The resource allocation model is presented to optimize the cost of the resources in the communications and relevance parts of computing (the data processing cost). The fog helps cloud computing connectivity on the edge network. This paper explains how calculation/analysis can be performed closer to the data collection site to complement the analysis that would be undertaken at the data centre. Results for a range of typical scenarios are presented to show the effectiveness of the proposed method.
Key words: Smart grid / Optimization / Fog – Cloud computing / Advanced Metering Infrastructure / Distributed Architecture
© The Author(s), published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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