TY - JOUR
T1 - Digital architecture for monitoring and operational analytics of multi-vector microgrids utilizing cloud computing, advanced virtualization techniques, and data analytics methods
AU - Patsidis, Angelos
AU - Dyśko, Adam
AU - Booth, Campbell
AU - Rousis, Anastasios Oulis
AU - Kalliga, Polyxeni
AU - Tzelepis, Dimitrios
PY - 2023/8/10
Y1 - 2023/8/10
N2 - 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.
AB - 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.
KW - microgrid
KW - digital architecture
KW - monitoring
KW - data acquisition
KW - energy data analytics
U2 - 10.3390/en16165908
DO - 10.3390/en16165908
M3 - Article
SN - 1996-1073
VL - 16
JO - Energies
JF - Energies
IS - 16
M1 - 5908
ER -