- Publication date
- 1 January 2017
- Joint Research Centre
Variability and uncertainty are familiar aspects of all power systems. However, the increase in the share of renewable sources leads to new needs in terms of flexible resources. These resources can be provided by different means, including:
- Dispatchable power plants (i.e. with ramp up and ramp down capabilities)
- Storage systems, mainly in the form of pumped hydro units
- Grid interconnections between countries
- Demand side management
- Power-to-X solutions (gas, fuel, heat, ...)
The aim of the Dispa-SET model is to represent with a high level of detail the short-term operation of large-scale power systems, solving the unit commitment problem. To that aim, it is considered that the system is managed by a central operator with full information on the technical and economic data of the generation units, the demands in each node, and the transmission network.
The unit commitment problem consists of two parts: i) scheduling the start-up, operation, and shut down of the available generation units, and ii) allocating (for each period of the simulation horizon of the model) the total power demand among the available generation units in such a way that the overall power system costs is minimized. The first part of the problem, the unit scheduling during several periods of time, requires the use of binary variables in order to represent the start-up and shut down decisions, as well as the consideration of constraints linking the commitment status of the units in different periods. The second part of the problem is the economic dispatch problem, which determines the continuous output of each and every generation unit in the system. The problem mentioned above can be formulated as a mixed-integer linear program (MILP).
The goal of the model being the simulation of a large (e.g. European) interconnected power system, a tight and compact formulation has been implemented, in order to simultaneously reduce the region where the solver searches for the solution and increase the speed at which the solver carries out that search. Tightness refers to the distance between the relaxed and integer solutions of the MILP and therefore defines the search space to be explored by the solver, while compactness is related to the amount of data to be processed by the solver and thus determines the speed at which the solver searches for the optimum.
The model is released as an open-source tool to increase its transparency, its reproducibility and its visibility. It is also cross-platform, and has been successfully tested in Windows, Linux and OSX. The model is structured in such a way that potential users can easily modify the input data to run their own simulations with limited knowledge in programming languages.