+ DESCRIPTION +
stELMOD is a stochastic optimization model to analyze the impact of uncertain wind generation
on the dayahead and intraday electricity markets as well as network congestion management.
The consecutive clearing of the electricity markets is incorporated by
a rolling planning procedure resembling the market process of most European markets.

The model is documented in:
Abrell, J. and Kunz, F. (2015):
Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market
Electricity Model for the European Electricity Market
Networks and Spatial Economics 15(1), pp. 117-147.
http://link.springer.com/article/10.1007/s11067-014-9272-4


+ VERSION +
Version 1.0.0, March 2016


+ LICENSE +
This work is licensed under the MIT License (MIT).

The MIT License (MIT)
Copyright (c) 2016 Friedrich Kunz (DIW Berlin) and Jan Abrell (ETH Zurich)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software
and associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute,
sublicense, and/or sell copies of the Software, and to permit persons to whom the Software
is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE
AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


+ CITATION +
Whenever you use this code, please refer to
Abrell, J. and Kunz, F. (2015):
Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market
Electricity Model for the European Electricity Market
Networks and Spatial Economics 15(1), pp. 117-147.
http://link.springer.com/article/10.1007/s11067-014-9272-4


+ CONTACT +
Friedrich Kunz, DIW Berlin, fkunz@diw.de, phone: +49(0)30 89789 495

###################################################################################################

+ MODEL STRUCTURE +
The model resembles three different market, Dayhead, Reserve capacity, and Intraday market.
Additionally, congestion management, performed outside the market by the TSO, is considered. The
different mmodules are connected through a rolling planning approach, which iterates through the
hours of the year and solves the daily dayahead and reserve, hourly intraday market and hourly
congestion management.

Generation is reflected by a unit commitment approach, which can be defined as a binary or
integer (clustered) unit commitment.

Load is considered as price-inealastic fixed hour load. Load shedding options are available as 
last-resort option with high unit costs.

Renewable generation is valued at zero marginal costs and can be considered as either stochastic
or deterministic infeed. The stochastics take effect in the intraday market as well as they could
be considered in the congestion management. The dayahead market is defined as a deterministic
model in any case.


+ MODEL FILES +
To structure the entire code, different GAMS files are specified for specific purposes.

call.gms 							Main model file, which calls other gms-files
dataload.gms  						Dataupload and preprocessing of input data
construct_tree.gms 					Tree construction in case of stochastic model version
model_dayahead.gms  				Dayahead and reserve model
model_intraday_det.gms 				Deterministic intraday model
model_intraday_sto.gms 				Stochastic intraday model
model_congestionmanagement_det.gms 	Deterministic congestion management model
model_congestionmanagement_sto.gms  Stochastic congestion management model
prepare_da_init.gms 				Prepare input data for initial dayahead model
prepare_da.gms 						Prepare input data for dayahead models in rolling planning
prepare_id.gms 						Prepare input data for initial intraday model
prepare_cm.gms 						Prepare input data for initial congestion management model
unfix.gms 							Unifx decision variables (used in prepare_*.gms)
import_gdx.gms  					Import gdx files with individual model results
report.gms 							Reporting of model results


+ MODEL INPUT +
This version of the model uses two separate input file formats: Excel and GDX

%data%.xls 		Definition of (nearly) all input data in corresponding sheets
%ptdf%.gdx 		Input for power-transfer-distribution-matrix (PTDF)


+ MODEL OUTPUT + 
The model results are reported in GAMS gdx-format in a single file named %result%.gdx

The naming of the raw result parameters, final report (fr), is as follows:
fr_da_*			Final report of dayhead model results
fr_id_*			Final report of intraday model results
fr_cm_*			Final report of congestion management model results

These final reporting parameters are written after each model solve to a gdx-file and
after successful rolling planning merged and upload to GAMS for further post-processing.
NOTE: These individual gdx-files report only the results of the relevant hours or hour, and
not the full solution of the entire model solve. E.g. Dayahead model is solved for 36 hour, but
only 24 hour are reported. For intraday and congestion management, only the first hour is reported.

Within the report.gms a post-processing of the final reports is performed, with a pre-specified
set of relevant output parameters. Once the model is solved, the reporting could be changed and
the model option "only_reporting" allows to rerun the reporting with the previous model results.
