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:
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:
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).
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