DENSE

Wed, 01 Jan 2025 00:00:00 +0000

Dependable engineering of Smart Energy Systems (DENSE)

Funding Agency: Horizon Europe MSCA (Marie Skłodowska-Curie Actions Doctoral Network)
Start Date: January 2024
Duration: 4 years
Total budget: €2.6m
Website:
SPS Lab:

What is DENSE?

DENSE (Dependable Smart Energy Systems) is a 4-year Marie Skłodowska-Curie Actions Doctoral Network (HORIZON-TMA-MSCA-DN) funded under Horizon Europe. It brings together 10 partners from applied research, academia, and industry across 6 countries, with the aim of implementing 12 doctoral research projects.

The network trains early-stage researchers in the innovative dependable engineering of Smart Energy Systems (SESs), with a main focus on robustness as well as preventive and corrective actions under uncertainty.

Why it matters

The European Green Deal aims to transform the EU into a climate-neutral, fair, and prosperous society by 2050. DENSE supports this ambition by training and connecting the next generation of researchers and innovators needed to progress toward dependable Smart Energy Systems.

Dependability of complex networks such as SESs characterizes their ability to deliver service that can justifiably be trusted. It comprises attributes such as availability, reliability, safety, integrity, and maintainability. DENSE tackles this challenge with a focus on operational robustness and preventive/corrective actions in SESs.

Research objectives

Methods for designing scalable, hierarchical, and predictive coordination and operation algorithms for SESs at transnational and communal level
Tools for robust data-driven planning, control, and adaptation, including open-source solutions for joint planning and optimization across heat and power
Methods for robust estimation, fault detection, and preventive security management in SESs under uncertainties and disturbances with different time-scales
Software and hardware demonstrators of systems and components with control, management, and analysis tools from individual doctoral projects
Advanced validation methods for performance and functionality assessment of SESs, including heat pumps, green mobility, and cross-sectoral applications in smart communities and buildings

My role

As a Doctoral Candidate in DENSE, my research focuses on the optimal operation of electric vehicles and heat pumps within active distribution networks, in collaboration between the Cyprus University of Technology (SPS Lab) and the Electricity Authority of Cyprus (EAC).

Source

Project page adapted from the description.

TRAISIM

Wed, 01 Jan 2025 00:00:00 +0000

Training Simulator for Power System Operators (TRAISIM)

Funding Agency: CRESYM
Start Date: January 2025
Partners: Cyprus University of Technology (CUT), Réseau de Transport d’Électricité (RTE), Collaborative Research for Energy System Modelling (CRESYM)
Website:
Code:

Overview

TRAISIM is developing an open-source real-time training simulator that helps power system operators prepare for a grid that is becoming more dynamic, more digital, and more demanding to operate.

The project brings together high-fidelity power-system simulation, operator-focused interfaces, and modern digital-twin thinking in a single platform. Its purpose is to make advanced operator training more accessible, transparent, and adaptable for transmission system operators, researchers, and educators.

Built on top of Dynawo, TRAISIM offers an open alternative to closed and vendor-locked training environments. This creates space for collaboration, easier experimentation, and a clearer pathway from research to real operational use.

Why It Matters

Control rooms are evolving quickly. Operators must respond to renewable integration, changing network conditions, automation, and growing system complexity, all while maintaining security and reliability.

TRAISIM is designed to support that transition by offering a realistic environment where operators can train on credible scenarios, test responses to disturbances, and build confidence with tools that reflect the needs of future grids.

The project is also a strong demonstration that open-source technology can support large-scale, real-time operator training without sacrificing ambition or practical relevance.

What TRAISIM Delivers

TRAISIM combines several key elements into one training environment:

a realistic physical simulator for large transmission networks
a control-room style human-machine interface
orchestration tools that align simulation time and operator interaction
trainer and scenario-management functions for guided exercises
protection and automation behaviour for more credible event progression
AI-enabled model adaptation to improve responsiveness where it matters most

Together, these capabilities help create a platform that is realistic enough for serious training, flexible enough for research, and open enough to grow with new operational needs.

Progress So Far

In its first phase, TRAISIM has shown that open-source real-time training for large transmission systems is a realistic and promising direction. The project has already established a strong benchmarking foundation, identified the main performance bottlenecks, and defined a focused roadmap for the next stage of development.

The platform is being demonstrated on a very large transmission network model inspired by the French grid, highlighting TRAISIM’s relevance for realistic TSO-scale applications.

Current work is focused on:

improving simulation speed and robustness
using adaptive model selection to reduce computational burden
strengthening coordination between the simulator and the training environment
preparing reusable methods, software improvements, and research outputs for the wider community

Early results from the adaptive model selection work are especially encouraging, showing major reductions in model size while maintaining very high predictive accuracy.

Publications

TRAISIM’s first public outputs are already available and showcase both the project vision and its early technical progress:

, accepted at PSCC 2026
TRAISIM: Training Simulator for Power System Operators, poster presented at CRESROADS 2026

These publications present the first benchmark results, explain the platform concept, and outline the next steps in solver optimisation, AI-enabled model adaptation, and open-source dissemination.

Synergy with SPS-Lab Research

TRAISIM is closely connected to other SPS-Lab research directions:

IBM (Interpolation-Based Method): improved handling of controller-induced discontinuities in hybrid simulations
Modeling & Simulation: scalable numerical methods for large and complex power-system models
TwinEU (Pilot 8, Task 5.5): digital twin concepts and infrastructure that support the broader training-platform vision

Contact:

Source

Project page adapted from the description.

Current Projects | Savvas Panagi

DENSE

Dependable engineering of Smart Energy Systems (DENSE)

What is DENSE?

Why it matters

Research objectives

My role

Related publications

Source

TRAISIM

Training Simulator for Power System Operators (TRAISIM)

Overview

Why It Matters

What TRAISIM Delivers

Progress So Far

Publications

Synergy with SPS-Lab Research

Source