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, Dimitrios Tzelepis

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
52 Downloads (Pure)

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.
Original languageEnglish
Article number5908
Number of pages19
JournalEnergies
Volume16
Issue number16
DOIs
Publication statusPublished - 10 Aug 2023

Keywords

  • microgrid
  • digital architecture
  • monitoring
  • data acquisition
  • energy data analytics

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