T OOLS

All technical, economic and area-related parameters are stored in a single spreadsheet file.

The format was chosen as to provide clear overview for easy adjustments to changing scenario assumptions. The resource data are stored in binary data files, providing quick access by data processing tools. Geographical data for spatial analyses such as land cover, population, elevation, are stored in a database of GIS files. The final results are provided in text files, spreadsheets and diagrams.

2.3.2 Geographic information systems

Geographic information systems are used for data processing and visualisation. Resource and other data with spatial reference often differ in the format and resolution, regional coverage, geographic projection and reference system used. These properties were harmonised for all input data using the geographical information systems ‘IDRISI’ and

‘ARC-View’. These software tools can cut out a window from a data set covering a bigger area than required or paste together two data sets covering a part of the required area each.

Geographical reference systems and projections can be changed. The number of raster cells can be increased or decreased by calculating averages of the original raster cells or choosing the ‘nearest original neighbours’ values for the new raster cells. Raster cell contents can be reclassified, e.g. country numbers can be replaced with parameter values to be displayed on a country level, such as biomass potentials or annual electric power demand. Mathematical functions can be performed involving one or several data sets.

Information can be aggregated and extracted from a data set, e.g. a raster containing country numbers can serve as a model for the extraction of country-level values from a wind electricity generation data set. On the other hand, spatial information can be disaggregated with a proxy parameter by generating a normalised version of the proxy parameter data set and multiplying it with a map containing the parameter to be disaggregated. National potentials of forest wood for energy use, for example, were distributed on the national forest area with land cover data of the category ‘forest’, normalised on a national level.

Two geographic information systems with different focus and different functions needed for this investigation were used. While IDRISI provides raster data processing tools that can easily be used in combination with C-based data processing programs, ARC-View is a standard in geo-data processing. Many data are provided in ARC formats. Their processing requires the use of Arc-View conversion tools.

2.3.3 C-code

Installable power generation capacities and electricity generation potentials in each grid cell were calculated in C-programs using resource data, technical parameters and GIS-data sets for deciding on area suitability. In principle, these calculations could have been directly executed in a GIS, but using C-programs can strongly reduce the processing times especially for calculations that must be repeated for many time steps.

C-programs were also used for calculating costs in each raster cell and regional cost-potentials curves. The curves were generated by regional sorting of the potential in each raster cell to cost-categories. The regional potential in each cost category was cumulated and plotted versus the levelised electricity costs. These curves are shown in the corresponding sections on the potentials of renewable energy sources (see chapter 4).

The C-programs were also used for transfer of the technical and economic parameters to the energy system model. The model environment needs input with specific formats. The formatting was automated with a C-code module that writes text-files which can be read by the modelling environment GAMS.

2.3.4 GAMS – general algebraic modelling system

The modelling environment GAMS (General Algebraic Modelling System) provides the possibility to build up optimisation models with a clear and dense structure. The terminology adopted in GAMS is as follows: indices are called sets, given data are called parameters, decision variables are called variables, and restrictions and the objective function are called equations. The user defines parameters, variables and equations and declares their domains before they are formulated. A domain is the set over which a parameter, variable, or equation

is defined. After defining parameters, their values are read from input files. Then, variables are defined that are varied by GAMS in order to find an optimised solution for the presented problem. The problem itself is formulated in an objective function and restrictions. The objective determined by the objective function is the minimisation or maximisation of the objective variable. While the objective function must be an equation, the restrictions to be regarded can be equations or inequations.

In the presented work, the main sets are regions, technologies and time steps. The main parameters are installable capacities, generation potentials and energy demand, defined for their respective domains. The variables are installed capacities of generation, storage and transmission technologies and generation, storage consumption and transmission in each time step. The objective is to minimise the total system costs. The potentials of and the economic competition between resources, storage capacities and balancing effects enabled by transmission lines is taken into account via the restrictions, leading to the most cost-efficient combination of these options.

A linear programming approach was chosen. As the solution space of a linear optimisation problem is convex, the solution is always a global optimum. However, the model requires large amounts of input data and the running times can be very long. In order to reduce the running times, different solvers offered by GAMS and different algorithms were applied. The CPLEX solver can apply a simplex algorithm, which finds the optimum by changing variables along the ‘outer surface’ of the solution space. The barrier algorithm on the contrary is an interior-point method. In many cases, it proved to be faster than the simplex algorithm, but in some cases the processes could not be finished because of running times of several weeks.

Other options for reducing the running times that were applied are the reduction of the number of regions and time steps and keeping the number of technologies low. These simplifications lead to less accurate results but could not be avoided because of the otherwise excessive running times.