The individual modules are implemented in Matlab and their individual functions are arranged into several directories according to the individual modules. A main Matlab function organizes the data input and the distribution of the data matrices between the executed modules and is located in the main directory of the Scenario Tree Tool. The input data files in ASCII format are stored in a separate directory. The required input data files are generated by a MS Access Scenario Tree Tool input database holding the input data. The process of reading out data into the individual ASCII is controlled using a form. Therewith it is possible to select flexible time periods that have to be covered by the resulting scenario trees, the consideration of different forecast accuracies and wind power capacities.
By running the main programme, the required data is read into the Scenario Tree Tool and the individual modules are executed. The sequence of actions is organized as showed in Figure 48. The resulting ASCII files for the Scheduling Model are written out to a separate directory.
Scenario Tree Tool Input Database
1. Read-in of input data
Time-series of wind power
2. Transformation of wind power time- series into wind speed time-series
Time-series of wind speed
3. Generation of n wind speed forecast error scenarios
Scenarios of wind speed forecast error
4. Combination of wind speed forecast error scenarios with wind speed time-
series
Wind power forecast scenarios
5. Transformation of wind speed forecast scenarios into wind power forecast
scenarios 6. Generation of n load forecast error
scenarios
Scenarios of load forecast errors
7. Combination of load forecast error scenarios with load time-series
Load forecast scenarios
Wind speed forecast scenarios
8. Generation of n Semi- Markov processes of outages
of individual units
Scenarios of Semi- Markov processes of
unit outages
9. Random allocation of scenarios of wind power and load forecasts and of Semi-Markov processes of unit outages
Allocated scenarios of wind power and load forecasts Allocated scenarios of wind
power and load forecasts and Semi-Markov processes of unit outages
11. Determination of non-spinning
positive reserves 10. Scenario reduction and generation of scenario trees
Input files for the Scheduling Model Reliability data of
units
12. Read-out of resulting data
Time-series of load
Scenario Tree Tool Input Database
1. Read-in of input data
Time-series of wind power
2. Transformation of wind power time- series into wind speed time-series
Time-series of wind speed
3. Generation of n wind speed forecast error scenarios
Scenarios of wind speed forecast error
4. Combination of wind speed forecast error scenarios with wind speed time-
series
Wind power forecast scenarios
5. Transformation of wind speed forecast scenarios into wind power forecast
scenarios 6. Generation of n load forecast error
scenarios
Scenarios of load forecast errors
7. Combination of load forecast error scenarios with load time-series
Load forecast scenarios
Wind speed forecast scenarios
8. Generation of n Semi- Markov processes of outages
of individual units
Scenarios of Semi- Markov processes of
unit outages
9. Random allocation of scenarios of wind power and load forecasts and of Semi-Markov processes of unit outages
Allocated scenarios of wind power and load forecasts Allocated scenarios of wind
power and load forecasts and Semi-Markov processes of unit outages
11. Determination of non-spinning
positive reserves 10. Scenario reduction and generation of scenario trees
Input files for the Scheduling Model Reliability data of
units
12. Read-out of resulting data
Time-series of load
Figure 48. Sequence of actions within the Scenario Tree Tool.
In the following the sequence of actions within the Scenario Tree Tool is described: 1. Read in the required data from the individual ASCII input data files into
relevant Matlab matrices from the Scenario Tree Tool input database.
2. In the case where a region or zone is represented by measured wind power time-series, these time-series are converted into wind speed time-series. Otherwise the measured wind speed time-series are used directly.
3. Monte-Carlo-simulation of n scenarios of wind speed forecast errors for a forecast horizon up to 36 hours based on ARMA-processes describing the wind speed forecast error. The correlations of the wind speed forecast errors between individual zones are considered, see section A.1.2.1.
4. Combination of the generated wind speed forecast errors with the corresponding values of the wind speed time-series to simulate the wind speed forecast scenarios.
5. Transformation of wind speed forecast scenarios into wind power forecast scenarios. For this purpose an aggregated power curve considering the spatial distribution of wind farms within a zone is used, see section A.1.2.1. Subsequently, the individual wind power scenarios of the individual zones are aggregated on a regional level.
6. Monte-Carlo-simulation of n scenarios of load forecast errors for a forecast horizon up to 36 hours based on ARMA-processes describing the load forecast error, see section A.1.2.1.
7. Combination of the generated load forecast errors with the corresponding values of the load time-series to simulate the load forecast scenarios.
8. Monte-Carlo-simulation of n scenarios of Semi-Markov processes describing the availability or unavailability of the generation units considered over a whole year, see section A.1.2.3.
9. Random allocation of individual scenarios of forecasts of wind power and load and of Semi-Markov processes describing availability or unavailability of units considered.
10. Reduction of combined scenarios of wind power and load forecasts and generation of scenario trees, see section A.1.2.2.
11. Determination of the requirements for replacement reserves due to wind power and load forecast errors and unit availabilities, see A.1.2.4.
12. The resulting ASCII files holding the scenarios of wind power and load forecasts, Semi-Markov processes describing the availability or unavailability of the units considered and the requirements for replacement reserves are saved into the result directory of the Scenario Tree Tool. These resulting ASCII files are read-in by scenario tree database directly, see Figure 40.