Name Genetic Optimisation of a European Energy Supply System
Acronym GENESYS (2)
Methodical Focus None
Institution(s) RWTH Aachen University Institut ISEA, RWTH Aachen University Institut IAEW
Author(s) (institution, working field, active time period) Bussar; Christian ISEA (2013 - today); Stoecker; Philipp ISEA(2013 - today); Moraes Jr.; Luiz ISEA( 2014-today);Thien; Tjark ISEA (GENESYS 1; till 2013); v. Bracht; Niklas IAEW (2014 -today); Fehler; Alexander IAEW (2016-today); Alvarez; Ricardo IAEW (GENESYS 1; till 2014)
Current contact person Christian Bussar
Contact (e-mail) christian.bussar@isea.rwth-aachen.de
Website www.genesys.rwth-aachen.de
Logo /media/logos/logo4_final.png
Primary 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 Investigation of reserve requirement, additional cost from grid expansion Investigation of sector electrification, additional system extension in case of higher demand and changed profiles
Primary Outputs Optimal system transition / system configuration (and development) for the target time span based on parametrisation of today existing power plants - Averaged cost of electricity, cost of electricity per source per country per year based on input data (cost of investment & fuels) - Investment amount per year of all considered technologies in the focus years (e.g. 2015-2050) - Cost of investments of all components, cost of operation - operation of system components, utilisation of generation units for demand balancing
Support / Community / Forum
Framework
Link to User Documentation
Link to Developer/Code Documentation
Documentation quality expandable
Source of funding 2011 BMU (FKZ 0325366) & 2015 BMWi (FKZ 0325692)
Number of developers less than 10
Number of users less than 10
Open Source
License LGPL / entire code based on open source
Source code available
GitHub
Access to source code mailto:genesys@isea.rwth-aachen.de | git.rwth-aachen.de
Data provided all
Collaborative programming
Modelling software C++11
Internal data processing software
External optimizer
Additional software Result processing and analysis of xml files is executed in python
GUI
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
Citation DOI https://doi.org/10.1016/j.egypro.2017.09.526
Reference Studies/Models https://doi.org/10.1016/j.est.2016.02.004: 10.2314/GBV:837303370 : https://doi.org/10.1016/j.egypro.2014.01.156
Example 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 linke?; 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?
Model usage
Model validation -
Example 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 linke?; 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?
further properties Specific targets can be set for limitation of annual Co2 Emissions per country/region or on a global scale, alternatively the competitiveness for thermal power generation can be evaluated under influence of co2 emission certificate price development assumptions Model can easily be adapted given the case input data can be provided for the representation of regions (time series, demand and existing system components)
Model specific properties Model can calculate a fast system operation/unit dispatch in 1-2 minutes for 35 years on hourly basis (1 CPU) This can be implemented in an evolution strategy, genetic algorithm or monte carlo simulation/optimisation and it is suitable for multi-core operation (via openmp)
Modeled energy sectors (final energy) electricity
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro
Conventional gas, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage battery, pump hydro, gas
User behaviour and demand side management
Changes in efficiency Efficiency of a powerplant is set by investment year (technology based), efficiency(t) can be varied Application of serveral powerplant capacities together uses a weighted average mean trying to priorise higher efficiencies.
Market models -
Geographical coverage
Geographic (spatial) resolution national states
Time resolution hour
Comment on geographic (spatial) resolution GENESYS is typically applied on a set of countries with NTC connections and aggregated capacities, it was has also been applied with regional/NUT3 parametrisation as a test. Aggregation of geospatial area to a specific zone allows flexible representation of states, tso regions, federal states or regions based on the availability of input data (e.g. capacities per zone, time series for RE geneators and load)
Observation period >1 year, flexible 1y - 100y+ possible
Additional dimensions (sector) ecological, additional dimensions sector ecological text, economic, additional dimensions sector economic text
Model class (optimisation) -
Model class (simulation) Bottom up
Other Heuristic optimisation with a bottom up model for dispatch
Short description of mathematical model class Bottom up definition of system components from a engineering perspective Demand balancing based on energy flow calculation
Mathematical objective costs
Approach to uncertainty -
Suited for many scenarios / monte-carlo
typical computation time less than a minute
Typical computation hardware x64 20 core
Technical data anchored in the model Hierarchical dispatch groups / RE, storage, thermal generation
Interfaces
Model file format unix executable
Input data file format .csv
Output data file format xml
Integration with other models
Integration of other models

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