Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods

Angelos Patsidis, Adam Dyśko, Campbell Booth, Anastasios Oulis Rousis, Polyxeni Kalliga and Dimitrios Tzelepis ()
Additional contact information
Angelos Patsidis: Smart Power Networks Ltd., 65 More Close, London W14 9BN, UK
Adam Dyśko: Department of Electronic and Electrical Engineering, Royal College, University of Strathclyde, 204 George St., Glasgow G1 1RX, UK
Campbell Booth: Department of Electronic and Electrical Engineering, Royal College, University of Strathclyde, 204 George St., Glasgow G1 1RX, UK
Anastasios Oulis Rousis: Smart Power Networks Ltd., 65 More Close, London W14 9BN, UK
Polyxeni Kalliga: Smart Power Networks Ltd., 65 More Close, London W14 9BN, UK
Dimitrios Tzelepis: Smart Power Networks Ltd., 65 More Close, London W14 9BN, UK

Energies, 2023, vol. 16, issue 16, 1-19

Abstract: Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been developed in AWS, considering market arrangements between the microgrid and the utility. The analysis involves time-series data processing, followed by the exploration of statistical relationships utilizing cloud-hosted tools. Insights from one year of operation highlight the potential for significant operational cost reduction through the real-time optimization and control of microgrid assets. By addressing the real-world applicability, end-to-end architectures, and extraction of case-specific insights, this work contributes to advancing microgrid design, operation, and adoption.

Keywords: microgrid; digital architecture; monitoring; data acquisition; energy data analytics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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