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| | |deterministic=24 h foresight for storage operation | | |deterministic=24 h foresight for storage operation |
| | |is_suited_for_many_scenarios=No | | |is_suited_for_many_scenarios=No |
| − | |computation_time_minutes=1-3 for operation simulation (5y data) <br> 80h optimisation run | + | |computation_time_minutes=1-3 min for operation simulation (5y data) <br> 80h optimisation run |
| | |citation_references=Bussar et. al, 2014, Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation | | |citation_references=Bussar et. al, 2014, Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation |
| | |citation_doi=10.1016/j.egypro.2014.01.156 | | |citation_doi=10.1016/j.egypro.2014.01.156 |
Revision as of 12:49, 9 December 2014
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Genetic Optimisation of a European Energy Supply System
by RWTH-Aachen University
Authors:
Alvarez, Bussar, Cai, Chen, Moraes Jr., Stöcker, Thien
Contact:
Christian Bussar
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The GENESYS Simulation tool has the central target so optimise the future European power system (electricity) with a high share of renewable generation. It can find an economic optimal distribution of generators, storage and grid in a 21 region Europe.
The optimisation is based on a covariance matrix adaption evolution strategy (CMA-ES) while the operation is simulated as a hierarchical setup of system elements aiming to balance the load at minimal cost.
GENESYS comes with a set of input time-series and a parameter set for 2050 which can be adjusted by the user.
It was developed as open source within a publicly funded project and its development is currently continued at RWTH Aachen University.
Based on C++, boost library, MySQL and QT4, (optional CPLEX solver implementation). Using Excel/Matlab and a Visualisation tool programmed in QT4 (c++) for data processing.
[www.genesys.rwth-aachen.de Website / Documentation]
[Form on website Download]
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 Open Source
GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1)
 Not directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Electricity System Model
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| Sectors
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Electricity
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| Technologies
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Renewables, Conventional Generation
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| Decisions
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dispatch, investment
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| Regions
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Europe, North Africa, Middle East
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| Geographic Resolution
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EUMENA, 21 regions
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| Time resolution
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Hour
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| Network coverage
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transmission, net transfer capacities
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| Model type
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Optimization, Simulation
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optimisation of system combination with evolutionary strategy
simulation of operation with hierarchical management strategy and linear load balancing between regions (network simplex)
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| Variables
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| Computation time
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1-3 min for operation simulation (5y data)
80h optimisation run"-3minforoperationsimulation(5ydata)<br/>80hoptimisationrun" is not declared as a valid unit of measurement for this property. minutes
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| Objective
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minimise levelised cost of electricity
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| Uncertainty modeling
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24 h foresight for storage operation
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| Suited for many scenarios / monte-carlo
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No
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References
Scientific references
Bussar et. al, 2014, Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation
https://dx.doi.org/10.1016/j.egypro.2014.01.156
Example research questions
How much storage systems of which technology needs to be implemented in the future energy system.
How big are the transfer capacities between regions.
How much renewable generator power of which technology are necessary?
How much conventional generators are allowed within assumed CO2 emission limits?
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