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

 

Scope

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..

Benefits

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.

Approach

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

01

Guidelines for the acquisition and analysis of renewable performance datasets

 

02

HW and SW requirements for plant digitalization and energy flow management

03

Accurate performance models for battery storage systems 

04

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

05

Reliability models for predicting equipment breakdowns 

 

06

Interoperable communication for integrated operation and data aggregation

07

Cloud-based solution with enhanced energy management interoperability

08

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.

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