Name Renewable Electricity Supply and Storage in Europe
Acronym RESTORE
Methodical Focus None
Institution(s) Wuppertal Institut;
Author(s) (institution, working field, active time period) Mathis Buddeke; Christine Krüger; Arjuna Nebel
Current contact person Christine Krüger
Contact (e-mail) christine.krueger@wupperinst.org
Website www.wupperinst.org
Logo
Primary Purpose Optimised dispatch of flexibility options (Storage, Demand Side Management, Transmission grid). Minimising and leveling backup eneregy (non renewable energy). Europe, temporal and spatial resolution are adaptable (min resolution: countrywise, hourly)
Primary Outputs Technical component dispatch. Residual load. Redispatch of fluctuating RES.
Support / Community / Forum
Framework
Link to User Documentation
Link to Developer/Code Documentation
Documentation quality expandable
Source of funding BMBF - RESTORE2050 Project
Number of developers less than 10
Number of users less than 10
Open Source
Planned to open up in the future
Costs -
Modelling software Matlab, R
Internal data processing software fmincon
External optimizer
Additional software
GUI
Citation reference Kruger, C.; Buddeke, M.; Merten, F.; Nebel, A. (2015) Modelling the interdependencies of storage, DSM and grid-extension for Europe In: 12th International Conference on the European Energy Market (EEM), Lisbon Portugal, 19-22 May 2015, Inst. of Electrical and Electronics Engineers, New York, pp. 1-5
Citation DOI http://dx.doi.org/10.1109/EEM.2015.7216669
Reference Studies/Models Vogt et al. (2016): „RESTORE 2050 - Regenerative Stromversorgung & Speicherbedarf im Jahr 2050, Projektabschlussbericht: Ergebnisse und Handlungsempfehlungen
Example research questions Interdependencies of flexibility options such as storage, DSM or grid extension in renewable based energy systems
Model usage
Model validation -
Example research questions Interdependencies of flexibility options such as storage, DSM or grid extension in renewable based energy systems
further properties
Model specific properties target function not cost-based, but minimization of residual loads
Modeled energy sectors (final energy) electricity
Modeled demand sectors Households, Industry, Commercial sector, Transport
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro, Solar thermal
Conventional gas
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage battery, kinetic, compressed air, pump hydro, chemical, gas
User behaviour and demand side management Demand Side Management is realized for different sectors as a "storage" with time-dependent boundaries
Changes in efficiency -
Market models -
Geographical coverage Europe and North africa (32 countries)
Geographic (spatial) resolution national states
Time resolution hour
Comment on geographic (spatial) resolution Fully flexible, adaptable to the object of investigation.
Observation period 1 year, adaptable observation period
Additional dimensions (sector) ecological, additional dimensions sector ecological text
Model class (optimisation) fmincon
Model class (simulation) -
Other
Short description of mathematical model class Quadratic objective function: minimization of the square of the residual load, resulting in - reduced overall residual load - reduced load peaks - reduced gradients
Mathematical objective RE share and load levelling
Approach to uncertainty Rolling horizon optimization
Suited for many scenarios / monte-carlo
typical computation time more than a day
Typical computation hardware 12 Core 64GB
Technical data anchored in the model -
Interfaces
Model file format mat
Input data file format .csv
Output data file format *.mat, *.pdf
Integration with other models
Integration of other models

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grid Europe MODEX Storage DSM