Challenge 3: Safe, Efficient and Clean Energy

The MSO-ED tools allow studying important aspects such as demand response, energy transmission, or distributed generation. Mathematical technologies allow the management of smart networks that integrate sustainable energy sources such as wind, photovoltaic or hydroelectric power with fossil fuels. They are also essential for improving the efficiency of power plants, wind turbines, batteries and solar efficiency.

No less important is the optimization of energy-intensive industrial processes. Optimally adjusting the operating parameters of each process helps adjust the energy required for the required production level. It is a field in which the numerical simulation of the process helps to make disruptive changes that entail significant changes in the need for energy resources.

Decision makers need advanced tools that enable long-term risk analysis, clean processes, and prototype improvements, along with an appropriate set of optimization and control methods. The key technologies of these processes are often very complex and require the support of highly qualified experts, which are often not available in small and medium-sized companies. In addition, the providers of the necessary skills are spread over different areas of specialization in universities, research and technological institutions. These characteristics require the collaboration and integration of different advanced providers and other innovation players in different sectors and regions. This integrative role is one of the proposed goals for the PET MSO-ED platform.

• Capacities of MSO-ED Technologies in the field of Challenge 3
  • Prediction and planning of energy production, and generation of scenario trees of the uncertainty of the demand for the various types of energy for domestic and industrial uses.
  • Optimization of the planning of the expansion capacity of energy generating elements and their transmission, considering the location options of renewable energy sources (wind, solar and photovoltaic, among others).
  • Optimization of electricity generation in industrial and residential buildings for self-consumption, and its connection to the distribution network.
  • Optimization of maintenance planning for electric power generators.
  • Optimization of power distribution networks.
  • Numerical simulation of heat transfer processes and combustion processes.
  • Numerical simulation of thermoelectric, thermomagnetic, and thermomechanical processes.
  • Help in making decisions in energy processes.

• Success stories already implemented in the field of Challenge 3
  • Power network simulation and optimization software. Math-in success case accessible in the database of the European network EU-Maths-IN.
  • Simulation and optimization of battery charging and discharging processes for electric vehicles. Math-in success case published in the database of the European network EU-Maths-IN.
  • Profitability of hydrogen storage systems in wind farms. math-in Case Study Book, pg. 8.
  • Predictive maintenance of air conditioning systems. Math-in success stories published in the database of the European network EU-Maths-IN. See in link1 and link2.
  • Temperature control in industrial ovens. math-in Case Study Book, pg. 5.
  • Metal casting processes and metallurgical treatment. Math-in success case published in the database of the European network EU-Maths-IN.
  • Predictive tool for pollution episodes and pollution events in a thermal plant. See math-in Success Case Book, pg. 6.
  • Mathematical modeling and numerical simulation in order to improve the efficiency and productivity of industrial furnaces for metal purification. See math-in Success Case Book, pg. 3. 4.
  • Assists in the design of energy supply systems for ships in ports. Math-in success case published in the database of the European network EU-Maths-IN.