RWTH Aachen University Institut ISEA, RWTH Aachen University Institut IAEW
Geographical Scope
Global, Regional, National
Sectoral Scope
Electricity
General Problem Scope
Forecasting, Exploring
Specific Problem Scope
Energy demand, Energy supply, Environmental, Modular build-up, Energy dispatch, Capacity expansion planning, Unit commitment, Rule based operation management
Inital purpose
Optimisation of the development corridor from today till 2050 under political boundaries like co2 emission targets, emission certificate prices (that are set in the framework);Standard parametrisation of several European countries Operation and unit dispatch on hourly basis of the whole optimisation time span;Time series for Renewable Energy Generation with support for lookup table to simulate location details of optimal generator locations (wind) or less optimal sites; Flexibility of model settings to investigate targeted time span based on existing components, new investments, green field; Operation on hourly basis, but flexible adaptions can be implemented Investigation of need for long term, seasonal storage, synergies of renewable generation with different storage types; Investigation of policy effects on system structure like co2 emission allowance cost development trajectories, phase out of technologies, retarded investment incentives on certain technologies
Inital purpose change
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Research questions
What does the optimal system development pathway look like to reach Co2 mitigation targets from today towards year x ?, What does the power system configuration in year x look like?, What are the generation prices for technology X in an optimal system look like and is it competitive with other carriers?, How is the demand for storage in a specific year; in what year is investment into long term storage inevitable?
Inital Release Date
2017-01-01
Parent Framework
-
Version
Version 1.6.0
Source of funding
Bundesministerium für Wirtschaft und Energy (BMWi), Federal Ministry for Economics Affairs and Energy (BMWi)
GUI
Programming Framework
C++, Python used for result analysis
External Solver
Heuristic search algorithms are used, cmaes could be replaced by Particle Swarm etc.
Input Data Format
CSV
Auto model generator
data preprocessing
Output Data Format
XML
data postprocessing
python scripts for generating input files and preprocessing the output-file to a readable excel-format
energy system modelling background, working with Linux
Skills advanced
C++ for internal development
Fixed units
Renewable Technology Inclusion
Hydro, Solar, Wind, Biomass
Storage Technology Inclusion
Pumped-hydro energy storage, Battery energy storage, Hydrogen production / storage / consumption
Transport Demand
-
Residential Demand
-
Commercial Demand
-
Agricultural demand
Grid model
Single-node model
Cost Inclusion
Fuel prices, Fixed Operation & Maintenance, CO2 cost
New components
Variable time step
Variable rolling
Commonly used time step
Hourly
Commonly modelled time horizon
long term (>=15)
Reference Studies/Models
-
Citation reference
Bussar, C. ; Stöcker, Philipp ; Moraes Jr., Luiz ; Jacqué, Kevin ; Axelsen, Hendrik ; Sauer, D.U.: The Long-Term Power System Evolution – First Optimisation Results. In: Energy Procedia 135 (2017), p. 347–357