Ecological Assessment Tool (LEcA Tool)

Name Ecological Assessment Tool
Acronym LEcA Tool
Methodical Focus -
Institution(s) KTH-LWR
Author(s) (institution, working field, active time period) Ulla Mörtberg, Xi Pang
Current contact person Ulla Mörtberg
Contact (e-mail)
Website -
Primary Purpose Integrated Impact Assessment including ecosystem services
Primary Outputs Joint assessment of five ecosystem service indices: bioenergy potential, industrial wood production, carbon storge, recreation value, and habitat value
Support / Community / Forum
Link to User Documentation -
Link to Developer/Code Documentation -
Documentation quality expandable
Source of funding -
Number of devolopers less than 10
Number of users -
Open Source
Planned to open up in the future
Costs -
Modelling software Python, Matlab, R
Internal data processing software Matlab, R
External optimizer
Additional software ArcGIS, QGIS
Citation reference "• Augutis J., Martišauskas L., Krikštolaitis R. Energy mix optimization from an energy security perspective ( // Energy Conversion and Management. ISSN 0196-8904. 2015. Vol. 90, p. 300–314 • Martišauskas L. OSeMOSYS model application for disturbed energy system modelling // 9th annual conference of young scientists on energy issues CYSENI 2012: international conference, Kaunas, Lithuania, 24-25 May, 2012. Kaunas : LEI, 2012. ISSN 1822-7554, p. 323-335."
Citation DOI 10.1016/j.enconman.2014.11.033
Please list references to reports and studies which were produced using the model "National research programme “Future Energy” projects: • Investigation of Lithuanian Energy Security and Assessment of Energy Security Level (2012-2014). • Development of Methodology for Energy Security Analysis and Integrated Security Level Assessment” (2010-2011). "
Example research questions -
Larger scale usage -
Model validation -
Example research questions -
further properties
Model specific properties -
Modeled energy sectors (final energy) -
Modeled demand sectors -
Modelled energy carriers (primary energy carrier)
Gas -
Liquids -
Solid Biomass
Renewables -
Modeled technologies: components for generation or conversion
Renewables -
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity -
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage -
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage
Geographic (spatial) resolution regions, municipalities
Time resolution 10 years
Comment on geographic (spatial) resolution Spatial resolution from 25 m - 1 km or user defined
Observation period >1 year, user-defined
Additional dimensions (sector) -
Model class (optimisation) -
Model class (simulation) -
Short description of mathematical model class
Mathematical objective -
Approach to uncertainty -
Suited for many scenarios / monte-carlo
typical computation time less than a second
Typical computation hardware RAM
Technical data anchored in the model -
Model file format .txt, .py, .r
Input data file format .m
Output data file format .m
Integration with other models
Integration of other models OSeMOSYS is used as a modelling tool in the model and is one of the part. Model has ability to run OSeMOSYS n times with different scenarios

If you find bugs or if you have ideas to improve the Open Energy Platform, you are welcome to add your comments to the existing issues on GitHub.
You can also fork the project and get involved.

Please note that the platform is still under construction and therefore the design of this page is still highly volatile!

Fact Sheet objectives: The Fact Sheets are made to...
  • Find models/frameworks for your needs or just get an overview about the existing ones
  • Compare a selection of models for different purposes - e.g. to develop a strategy to link them
  • Store your model/framework information to provide transparency
More info here
Use case example model Fact Sheet: For a European pathway simulation (EPS) we want to choose the models that best meet our requirements. All partners include their models in the Model Fact Sheets and tag them with “EPS”. To compare the models we filter all “EPS” models and choose different characteristics that we want to compare. OEP will give us tables (views) that facilitate the comparison.