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Discover Real time edge control and AI for tomorrows smart grid services



Advanced system monitoring and analytics solution enhanced with intelligent interoperable data-driven features for efficient big data real-time analysis, failure diagnosis, automated management, and  integrated micro-grid control

Future grids will include a large share of distributed and fluctuating renewable energy sources. They will be digitally-enhanced to enable the necessary observability and control of underlying distributed energy resource (DER) assets. A significant challenge in the scope of decarbonizing the power sector and aligning with future energy needs is ensuring seamless DER integration such as solar PV and battery storage systems in electrical networks through advanced management and control systems. 
Therefore, we are entering an era whereby the energy focus is to improve PV’s performance and accelerate its advancement with new developments that facilitate fully dispatchable generation with storage. An industrial requirement is created for advanced, robust, and cost-effective system monitoring and control, as highlighted by the Solar Europe Industry Initiative (SEII) for enabling the transition towards a renewable grid..


The objective is to increase the value and competence of wind, solar, and energy storage by developing a next-generation multi-service monitoring and control system that:

  • Increases system efficiency,
  • Decreases investment cost through optimal performance,
  • Provides communicative smart grid control services.

The smart monitoring and cloud-based control system will be developed by integrating advanced data analysis algorithms in an edge computing solution with cloud-connectivity. Implementing intelligent, automated, and interoperable data-driven features allow for efficient real-time analysis of big data, predictive failure diagnosis, operational management, and integrated smart grid control. Such features will reduce the Levelized Cost of Electricity (LCoE) by increasing the lifetime output, improving operational efficiency, and optimizing system operations. Therefore, the system will significantly impact the technology’s value chain and serve as a transitional step towards fully dispatchable renewable energy generation.


The approach of the research project is to:

  • Assess data aggregation and interoperability protocols
  • Formulate protocols and guidelines for interoperable smart-grid controls
  • Develop predictive O&M diagnosis failure algorithms and asset reliability models incorporated as software modules to the advanced system
  • Optimize PV-plus-storage performance by developing peak performing replicas (digital twins)
  • Demonstrate the optimal performance of digital twins and software-based flexible power plant controllers at smart grid frequencies

The smart monitoring and cloud-based control system will be integrated with next-generation O&M, breakthrough supervision services (e.g., cost-effective predictive O&M, performance loss & failure predictions, and reliability routines), and advanced grid-to-storage applications that operate on data acquired from a vast integral of equipment (i.e., storage & grid controls) and tools (i.e., weather forecasts, workflows, and asset alerts).

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The Main Project Activities


Guidelines for the acquisition and analysis of renewable performance datasets



HW and SW requirements for plant digitalization and energy flow management


Accurate performance models for battery storage systems 


Software modules to forecast/diagnose failures and trend-based losses


Reliability models for predicting equipment breakdowns 



Interoperable communication for integrated operation and data aggregation


Cloud-based solution with enhanced energy management interoperability


Digital twin and software-based controller with real-time frequency response on the edge

Technology Enablers

The activities of the research project are divided into eight work-packages (WPs)

Real-time edge computing

Scalable time-series data backend

Advanced Analytics

Machine learning

API, Open protocols and interfaces

Low-cost IoT Solutions

Real-world use cases

Financial performance & Risk classification

Gain new insights on real time edge control and AI for tomorrows smart grid and asset services.

Latests Results

Result 1:

Microgrid setup at University Campus in Cyprus

  • Commercial-scale microgrid real-time supervisory and control management dashboard
  • Interoperable interfaces for real-time information flows for all underlying microgrid assets (building management systems, smart meters, storage and solar photovoltaic systems)
  • Energy management system presenting in real-time (at 1Hz resolution) all main consumption/production and power quality parameters

Result 2:

Hardware and cloud backend setup at University of Cyprus Campus

  • Full interoperability demonstration using Modbus TCP communication protocol
  • Integrated assets include building management systems, smart meters, photovoltaic inverters, battery storage systems and weather stations

Result 3:

Digital twin approach for PV fault diagnostics and health-state monitoring.

Result 4:

Mechanistic Performance Model (MPM) for PV Arrays

  • MPM assigns a meaningful normalized coefficient to expected performance behaviorto fit observed measurements with understandable loss coefficients

Result 5:

Mechanistic Performance Model part of Gantner’s Monitoring and control platform

Result 6:

Predictive performance of digital twin with errors < 3 % on MW-scale

  • Digital twin predictive models leveraging both mechanistic and machine learning principles (Artificial Neural Networks and Extreme Gradient Boosting)
  • Site-specific modelling with fully optimized hyper-parameter definition implementation stages

Result 7:

Unsupervised-Learning Methods for outlier classification at MW-scale & Microgrids

  • Unsupervised machine learning regimes (e.g., k-Nearest Neighbors, Angle-based Outlier Detector ) applied to data in order to detect and classify outliers (faults)

Result 8:

Digital Twin modelling and replica

  • Fully validated, robust and replicable models from small-scale to utility-scale PV power plant

Result 9:

PV system deviation modelling

  • PV system degradation rate calculation, short and long-term including (RdTools)
  • Performance loss rate modeling and detection of performance inflection points (long-term performance deviations)
  • Data quality, processing and sanity verification modeling for solar photovoltaic analytics

Workshop / Publications

“Data processing and quality verification for improved photovoltaic performance and reliability analytics,” Progress in Photovoltaics, DOI:10.1002/pip.3349, 2020

Intelligent cloud-based monitoring and control digital twin for photovoltaic power plants, 49th IEEE Photovoltaic Specialists Conference (IEEE PVSC 49), Philadelphia, USA, 2022 (Paper)

Intelligent cloud-based monitoring and control digital twin for photovoltaic power plants, 49th IEEE Photovoltaic Specialists Conference (IEEE PVSC 49), Philadelphia, USA, 2022 (Presentation)

Advanced system monitoring and artificial intelligent data-driven analytics to serve GW-scale photovoltaic power plant requirements, PV Performance Modelling Collaborative, Salt Lake City, USA, 2022

Unified methodology for PV data processing, quality verification, Web announcement of novel project results related to data quality and advance monitoring of PV power plants, PV magazine International, 2020

PV Module Monitoring and Characterization, From data to insights across the PV technology development cycle, SUPSI PVLab Industry Day, 2021

Improving Analysis Methods for IEC 61883 Matrix Measurements, PV Performance Modelling Collaborative, Salt Lake City, USA, 2022

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