| 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 | https://www.isea.rwth-aachen.de/cms/ISEA/Forschung/Projekte/Oeffentliche-Projekte/Abgeschlossene-Projekte/~pkja/GENESYS/?lidx=1 |
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| 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 | none |
| 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 |
| Modeled energy sectors (final energy) |
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| Modeled demand sectors |
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| Modeled technologies: components for power generation or conversion |
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| Modeled technologies: components for transfer, infrastructure or grid |
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| Properties electrical grid |
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| Modeled technologies: components for storage |
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| 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 |
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| 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) |
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| 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 |
| 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 |
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| 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) |