Reasons behind the project
In recent years, the power system has been undergoing profound transformations, largely driven by the integration of renewable energy sources and the widespread deployment of power electronic devices and interfaces. These changes necessitate new approaches to system operation and control.
To support system operators in managing the power system across a dynamic time window—from day-ahead planning to real-time operation—there is a need to define and develop advanced tools and methods. These will take the form of smart assistants, designed to assist operators in proactive and informed decision-making.
Despite increasing automation, a significant portion of decision-making will continue to rely on human operators in future control rooms. These operators will need to anticipate events, coordinate distributed control actions, take preventive measures, and address coordination challenges related to balancing and congestion management.
To effectively train these future operators, the renovation of existing decision-support tools and the creation of a realistic, high-fidelity environment for testing and training is essential.
This need is addressed through the development of a power system training Digital Twin, a virtual environment designed to simulate complex and critical scenarios. The core of this Digital Twin will be developed as part of the European project TWINEU (Pilot 8), specifically under Task 5.5: A power system training simulator for complex and critical situations. The system will be further enhanced by a complementary side project named TRAISIM.
Project objectives
The objectives of this project is:
1. To build a power system training Digital Twin that can simulate atleast as fast as the actual physical processes it models.
2. Address the challenges associated with meeting real-time constraints, particulary in solver design and execution efficiency.
3. Evaluate the simulator’s performance through structured procedures and analyses.
4. Focus on two key performance aspects: Computational efficiency & Numerical efficiency
5. Conduct a systematic assessment to understand the simulator’s behaviour under various modeling conditions.
6. Use the evaluation results to supprt and guide the simulator’s integration into real-time applications.
Project partners:
the project now gathers one PhD from Cyprus University of Technology and one industrial partner (RTE).
