Numerical approaches for the clean energy transition

Important Dates:

  • 30 June 2024 (AoE): Paper submission (Extended Deadline)
  • 15 July 2024: Notification of acceptance
  • 5 September 2024: Registration     
  • 5 September 2024: Revised papers according to reviews
  • 16-19 September 2024: Symposium


The increasing energy crisis of our time and the climate change pressure are forcing us as a society to pursue greater efficiency and shift away from fossil fuels, and navigate towards renewable sources. However, the integration of renewable energy into the power grid increases the complexity of its operation and control, given the decentralized feed-in from regenerative energies and dynamic demand responses. Traditionally, power supply by power plants varies on a long-time scale in the range of hours. Now, fluctuations take place in the order of minutes. To prevent power systems from becoming increasingly vulnerable to blackouts, the flexibility of power grids urgently needs to be enhanced by improved status control and optimization. Another major challenge connected to this integration is to control the energy distribution on a local system level taking into consideration individual power sources. The identification of anomalies and the implementation of countermeasures to increase control and flexibility requires a precise current measurement within the connected devices and the local power system. The success of the future interlocking of regenerative energy sources is guaranteed by the communication between sub-components of generation and storage, consumers, sensors, machines and people, with an effect on all involved economic sectors, e.g. municipal infrastructure, industry, agriculture, transport.

Sustainability and resilience are thus key for our future green energy supply and system. Computational approaches, from optimization to machine learning, can foster enhanced capabilities for sensor and storage devices, power system stability and reliability, effective allocation of resources, realistic estimation of demand patterns, adaptive control of energy distribution networks. 

In the framework of SYNASC, this special session aims to serve as a catalyst for innovation and collaboration within academia, research and industry. By bringing together computer scientists, engineers and industry stakeholders, the special session targets the creation of opportunities for knowledge exchange, idea generation, and cooperative problem-solving. Through interdisciplinary collaboration, participants can develop novel solutions to complex challenges and accelerate the transition towards a sustainable energy future.


Suggested topics for papers include, but are not limited to, the following topics:

  • Prognosis of renewable energy production and consumption
  • Mathematical optimization of energy demand, production and storage
  • Machine learning for pattern identification in smart electricity grids
  • System state identification and fault detection in power grids and integrated power system
  • Prediction of photovoltaic generation from sky camera images
  • Prediction of SoH and SoC in battery management
  • Estimation of sensor magnetic field and current
  • Sensor optimization

Session chairs:

  • Peter Glösekötter, FH Münster, Germany
  • Joseph Moerschell, Haute École Spécialisée de Suisse Occidentale, Switzerland
  • Ignacio Rojas, Universidad de Granada, Spain
  • Tilman Sanders, FH Münster, Germany
  • Markus Gregor, FH Münster, Germany
  • Sarah Trinschek, FH Münster, Germany
  • Ruxandra Stoean, University of Craiova, Romania

Preliminary programme committee

  • Felix Albu, Valahia University of Targoviste, Romania
  • José A. Aguado, Universidad de Málaga, Spain
  • Nebojša Bačanin, Singidunum University, Serbia
  • Carlos Cano Domingo, Barcelona Supercomputing Center, Spain
  • Frederik Hoffmann, FH Münster, Germany
  • Ludwig Horsthemke, FH Münster, Germany
  • Laszlo Barna Iantovics, University of Medicine, Pharmacy, Sciences and Technology „George Emil Palade” of Târgu Mureș, Romania
  • Gonzalo Joya, Universidad de Málaga, Spain
  • Wieslaw Paja, University of Rzeszow, Poland