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| | |Model validation=simulated consumption and generation were validated against real measured data | | |Model validation=simulated consumption and generation were validated against real measured data |
| | |Comment on model validation=real data of the respective city are required | | |Comment on model validation=real data of the respective city are required |
| | + | |Integrated models=oemof |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
Latest revision as of 12:24, 17 April 2024
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Flexibilisation in Geographic Information Systems
by DLR Institute of Networked Energy Systems
Authors:
Alaa Alhamwi
Contact:
Alaa Alhamwi
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FlexiGIS: an open source GIS-based platform for modelling energy systems and flexibility options in urban areas. It extracts, filters and categorises the geo-referenced urban energy infrastructure, simulates the local electricity consumption and power generation from on-site renewable energy resources, and allocates the required decentralised storage in urban settings using oemof-solph. FlexiGIS investigates systematically different scenarios of self-consumption, it analyses the characteristics and roles of flexibilisation technologies in promoting higher autarky levels in cities. The extracted urban energy infrustructure are based mainly on OpenStreetMap data.
Based on Python. Using Geopandas for data processing.
Website / Documentation
Download
|
 Open Source
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
Directly downloadable
Some input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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urban energy systems
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| Sectors
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Electricity Sector
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| Technologies
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Renewables
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| Decisions
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| Regions
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| Geographic Resolution
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building, street, district, city
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| Time resolution
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15 Minute
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| Network coverage
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distribution
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| Model type
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Optimization, Simulation
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|
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Modelling and optimisation mathematical model
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| Variables
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| Computation time
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minutes
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| Objective
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simualte local urban demand and supply, localise distributed storage, minimise total system costs
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| Uncertainty modeling
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|
| Suited for many scenarios / monte-carlo
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No
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References
Scientific references
GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas
https://dx.doi.org/https://doi.org/10.1016/j.apenergy.2017.01.048.
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