Open Source | |
Planned to open up in the future | |
Costs | licensing costs dependent on client |
Modelling software | GAMS |
Internal data processing software | Microsoft Access and/or SQL |
External optimizer | |
Additional software | |
GUI |
Modeled energy sectors (final energy) |
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Modeled demand sectors |
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Modeled technologies: components for power generation or conversion |
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Modeled technologies: components for transfer, infrastructure or grid |
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Properties electrical grid |
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Modeled technologies: components for storage |
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User behaviour and demand side management | four options: a) price-dependent demand b) DSM as load shifting c) Heat-pumps d) electrical vehicles (add-on) | ||||||
Changes in efficiency | yes, in operation: linear affine fuel consumption curve | ||||||
Market models | - | ||||||
Geographical coverage | |||||||
Geographic (spatial) resolution |
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Time resolution | annual, hour | ||||||
Comment on geographic (spatial) resolution | Included countries: EU28 (without CY,MT)+NO+CH+AL+ME+MK+RS+Baltics | ||||||
Observation period | <1 year, 1 year | ||||||
Additional dimensions (sector) |
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Model class (optimisation) | LP, MILP |
Model class (simulation) | - |
Other | |
Short description of mathematical model class | both LP and MIP formulations exist, use same code for roughly 95%, can be switched using software button |
Mathematical objective | costs |
Approach to uncertainty | Deterministic, currently no stochastic version operational, but limited foresight deterministic optimization & inclusion of forecast errors can induce recourse action |
Suited for many scenarios / monte-carlo | |
typical computation time | less than a day |
Typical computation hardware | standard desktop PC (4-core, 8 GB RAM) or server infrastructure |
Technical data anchored in the model | - |
Interfaces | Data exchange to input and output databases, communication through text files and csv. |
Model file format | .gms |
Input data file format | .inc (text files) |
Output data file format | .csv |
Integration with other models | separate CHP tool via .inc files, grid model via .inc files, vertical load model via .inc files, heat demand model via .inc files |
Integration of other models | grid model via .inc files |
Citation reference | LP: Wilmar Deliverable D6.2 (b), Wilmar Joint Market Model Documentation http://www.wilmar.risoe.dk/Deliverables/Wilmar%20d6_2_b_JMM_doc.pdf; Weber, C.; Meibom, P.; Barth, R.; Brand, H.: WILMAR - a stochastic programming tool to analyse the large scale integration of Wind Energy. In: Kallrath, J.; Pandalos, P.; Rebennack, S.; Scheidt, M. (Hrsg.): Optimization in the Energy Industry. Springer, New York 2009, S. 437 - 460; Trepper, K.; Bucksteeg, M.; Weber, C. (2015): Market splitting in Germany – New evidence from a three-stage numerical model of Europe, Energy Policy; Meibom, P.; Barth, R.; Hasche, B.; Brand, H.; Weber, C.; O'Malley, M. (2011): Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland. IEEE Transactions on Power Systems, 26 (3), S. 1367-1379; Meibom, P.; Barth, R.; Brand, H.; Hasche, B.; Swider, D.; Ravn, H.; Weber, C.; (2007): Final Report for All Island Grid Study. Work-stream 2(b): Wind Variability Management Studies. |
Citation DOI | 10.1109/TSTE. 2016.2555483; https://doi.org/10.1016 /j.enpol.2015.08.016; 10.1049/iet-gtd.2014.1063; 10.1002/we.224; |
Reference Studies/Models | Bucksteeg, M.; Niesen, L.; Weber, C.; Impacts of Dynamic Probabilistic Reserve Sizing Techniques on Reserve Requirements and System Costs (LP); IEEE Transactions on Sustainable Energy; 2016; Trepper, K.; Bucksteeg, M.; Weber, C.; Market splitting in Germany – New evidence from a three-stage numerical model of Europe (LP und MIP); Energy Policy; 2015; Bucksteeg, M.; Trepper, K.; Weber, C.; Impacts of renewables generation and demand patterns on net transfer capacity: implications for effectiveness of market splitting in Germany (LP und MIP); IET Generation, Transmission & Distribution; 2014; Barth, R., Meibom, P., Weber, C.; Simulation of short-term forecasts of wind and load for a stochastic scheduling model (LP und MIP); IEEE Power and Energy Society General Meeting Detroit 2011; 2011; Meibom, P.; Barth, R.; Hasche, B.; Brand, H.; Weber, C.; O'Malley, M.; Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland (MIP); IEEE Transactions on Power Systems, 26 (3), S. 1367-1379; 2011; Schröder, S.T., Meibom, P., Spiecker, S., Weber, C.; Market impact of an offshore grid - A case study (LP); Proceedings of the IEEE PES General Meeting. Minneapolis 2010; 2010; Meibom, P.; Weber, C.; Barth, R.; Brand, H.; Operational Costs Induced by Fluctuating Wind Power Production in Germany and Scandinavia (LP); IET Renewable Power Generation 3 (1), S. 75-83. ; 2009; Meibom , P., Kiviluoma, J., Barth, R., Brand, H., Weber, C., Larsen, H. V.; Value of electric heat boilers and heat pumps for wind power integration (LP); Wind Energy 10, S. 321-337; 2007; Meibom, P. Barth, R. Brand, H., Weber, C.; Wind power integration studies using a multi-stage stochastic electricity system model (LP); Proceedings of the 2007 IEEE Power Engineering Society General Meeting, Tampa 2007; 2007; Meibom, P.; Kiviluoma, J.; Barth, R.; ; Value of electric heat boilers and heat pumps for wind power integration (LP); Wind Energy; 2007; Barth, R. Brand, H., Swider, D., Weber, C., Meibom, P.; Regional electricity price differences due to intermittent wind power – Impact of extended transmission and storage capacities (LP); International Journal of Global Energy Issues 25, S. 276 - 298; 2006 |
Example research questions | Evaluating the welfare impact of a DC cable, evaluating efficiency of different bidding zone configuations, calculating market results and power plant dispatch as input for grid models |
Model usage | In addition to partners from the original WILMAR consortium, the model is in use at multiple utilities and transmission system operators |
Model validation |
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Example research questions | Evaluating the welfare impact of a DC cable, evaluating efficiency of different bidding zone configuations, calculating market results and power plant dispatch as input for grid models |
further properties | rolling planning approach without perfect foresight |
Model specific properties | a lot of details modeled (see above characteristics), numerous represented areas/markets, combined grid and market simulation possible, time effort for data research is correspondingly high |