Name Joint Market Model
Acronym WILMAR-JMM
Methodical Focus None
Institution(s) University of Duisburg-Essen, University of Stuttgart, VTT, DTU, University College Dublin
Author(s) (institution, working field, active time period) Christoph Weber; University of Stuttgart / University of Duisburg-Essen, Peter Meibom; DTU, Juha Kiviluoma; VTT, Björn Felten; University of Duisburg-Essen/ Flow Based Market Coupling; Detailed CHP Modeling, and others
Current contact person Christoph Weber, University of Duisburg-Essen
Contact (e-mail) christoph.weber@uni-due.de
Website http://www.wilmar.risoe.dk/
Logo
Primary Purpose Creating a detailed model representation of the European electricity market.
Primary Outputs System costs, market prices, power plant schedules, renewable production and shedding, storage operation, etc.
Support / Community / Forum
Framework
Link to User Documentation http://www.wilmar.risoe.dk/Deliverables/Wilmar%20d6_2_b_JMM_doc.pdf
Link to Developer/Code Documentation http://www.wilmar.risoe.dk/Deliverables/Wilmar%20d6_2_b_JMM_doc.pdf
Documentation quality good
Source of funding public (EU)
Number of developers less than 20
Number of users less than 100
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
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 cross-checked with other models, checked with measurements (measured data)
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
Modeled energy sectors (final energy) electricity, heat
Modeled demand sectors Households, Industry, Commercial sector, Transport
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro, Solar thermal
Conventional gas, oil, liquid fuels, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity distribution, transmission
Gas -
Heat distribution, transmission
Properties electrical grid DC load flow
Modeled technologies: components for storage battery, compressed air, pump hydro, heat
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 continents, national states, TSO regions, regions, NUTS 3, power stations
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) ecological, additional dimensions sector ecological text, economic
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

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