The players in the Nordic power market, i.e. producers, transmission system operators and regulators use computer models to plan for the best possible utilization of the system and energy resources. The computer models give results such as power prices, power production, dispatch etc. for different weather scenarios. This is important to avoid emptying reservoirs which may result in curtailment of electricity or to avoid too cautious operation which may result in unnecessary spillage and lost power production.
The future power system will have more non-controllable renewable power sources such as wind and solar and a stronger coupling to the continental Europe. Today's computational methods are not adapted for analysis of the future power system with intermittent energy sources. The Fansi model, developed in the SOVN-project, was developed with methods suitable for analysis of the future power system. The results from this model are promising but comes at the cost of a high computational burden. This project will investigate techniques to reduce the computational burden, and thus make the model usable for analyses or operational use.
Results:
-
SINTEF Open: Hydropower Aggregation by Spatial Decomposition – an SDDP Approach
- Hansen, Ole Martin. Solution of the Economic Dispatch Problem by Spatial decomposition. : SINTEF Energi AS 2022 (ISBN 978-82-14-07557-1) 43 s. SINTEF Rapport(2022:00246) Solution of the Economic Dispatch Problem by Spatial decomposition
- Helseth, Arild. Relaxation Techniques and Temporal Decomposition of Scenarios in Fundamental Power Market Models. SINTEF Energi AS 2022 (ISBN 978-82-14-07591-5) 37 s. SINTEF Rapport(2022:00960)
- Hansen, Ole Martin. Speeding up the hydrothermal power market model FanSi - Project results
- Helseth, Arild; Mo, Birger. Hydropower Aggregation by Spatial Decomposition – an SDDP Approach. IEEE Transactions on Sustainable Energy 2022 ;Volum 14.(1) s. 381-392
The FanSi model formulates mathematical optimisation problems of a large power system, for example the Nordic power system. In addition, the optimisation problem has a long time-horizon stretching over many timesteps, for example 8760 hours of a year. A know technique to reduce calculation time is to divide the large optimisation problem into many smaller optimisation problems, for example one can split the problem into smaller geographical areas and/or in time. This requires the coordination of the solutions from the smaller problems into a complete solution of the problem. This allows for reduced calculation time and large-scale parallel processing which in turn will reduce calculation time.
Project owner:
Statnett - Contact: Ivar Husevåg Døskeland
Project participants:
- Statnett
- SINTEF Energy Research
- Statkraft Energi
- The Norwegian Water Resources and Energy Directorate (NVE)
This is an IPN-project - Innovation Project for the Industrial Sector - partly financed by the Research Council of Norway
