NEMO |
|
|
University of New South Wales |
Ben Elliston |
|
|
|
b.elliston@unsw.edu.au |
Ben Elliston |
all |
dispatch |
|
|
|
National Electricity Market Optimiser |
|
Australia |
NEM regions |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
Optimisations are carried out using a single-objective evaluation function (with penalties). The search space (of generator capacities) is searched using the CMA-ES algorithm. |
minimise average cost of electricity |
|
true |
Python |
transmission |
100 |
true |
true |
|
|
|
https://git.ozlabs.org/?p=nemo.git |
Renewables Conventional Generation |
NEMO is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. |
Hour |
https://nemo.ozlabs.org/ |
NEMO (SEI) |
|
|
Stockholm Environment Institute |
Jason Veysey, Charlie Heaps, Eric Kemp-Benedict |
|
In preparation |
|
jason.veysey@sei.org |
Jason Veysey |
|
dispatch investment |
Deterministic but can readily be applied in Monte Carlo analyses |
|
Climate change mitigation, net-zero pathways, national energy strategies |
Next Energy Modeling system for Optimization |
|
All |
Flexible - user-defined regionalization |
true |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
Constrained cost optimization with perfect foresight |
Minimize total discounted costs |
Full energy system optimization flexible geographic and sectoral scope |
true |
Julia |
transmission distribution DC load flow net transfer capacities |
100 |
false |
true |
SQLite |
https://doi.org/10.1016/j.apenergy.2022.118580
https://doi.org/10.1016/j.est.2021.103474 |
All |
https://github.com/sei-international/NemoMod.jl |
Renewables Conventional Generation CHP |
NEMO is a high performance, open-source energy system optimization modeling tool developed in Julia. It is intended for users who seek substantial optimization capabilities without the financial burden of proprietary software or the performance bottlenecks of common open-source alternatives. It can be used in stand-alone mode or with the Low Emissions Analysis Platform (LEAP) as a front-end. |
Hour |
https://www.sei.org/projects-and-tools/tools/nemo-the-next-energy-modeling-system-for-optimization/ |
OMEGAlpes |
|
|
G2Elab |
B. DELINCHANT, S. HODENCQ, Y. MARECHAL, L. MORRIET, C. PAJOT, V. REINBOLD, F. WURTZ |
|
|
|
omegalpes-users@groupes.renater.fr |
|
some |
|
|
|
|
Optimization ModEls Generation As Linear Programming for Energy Systems |
|
|
|
false |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
|
|
Production consumption conversion storage |
true |
OMEGAlpes, PuLP |
|
|
false |
true |
|
|
Electricity Heat all |
https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes |
|
OMEGAlpes stands for Generation of Optimization Models As Linear Programming for Energy Systems. It aims to be an energy systems modelling tool for linear optimisation (LP, MILP). It is currently based on the LP modeler PuLP. |
|
https://omegalpes.readthedocs.io/en/latest/index.html |
OSeMOSYS |
|
|
KTH Royal Institute of Technology |
Mark Howells, Holger Rogner, Neil Strachan, Charles Heaps, Hillard Huntington, Socrates Kypreos, Alison Hughes, Semida Silveira, Joe DeCarolis, Morgan Bazillian, Alexander Roehrl |
doi:10.1016/j.enpol.2011.06.033 |
|
|
osemosys@gmail.com |
Mark Howells, Will Usher, Abhishek Shivakumar, Manuel Welsch, Vignesh Sridharan |
all |
investment |
|
|
|
open-source energy modelling system |
|
Africa (all countries), Sweden, Baltic States, Nicaragua, Bolivia, South America, EU-27+3 |
Country |
true |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
Linear optimisation (with an option of mixed-integer programming) |
Minimise total discounted cost of system |
|
true |
GNU MathProg |
transmission distribution |
|
true |
true |
Python |
|
all |
http://github.com/OSeMOSYS/OSeMOSYS |
Renewables Conventional Generation CHP |
OSeMOSYS has been created by a community of leading institutions and is capable of powerful energy systems analysis and prototyping new energy model formulations. It is typically used for the analysis of energy systems looking over the medium (10-15yrs) and long (50-100yrs) term. It is used by experienced modellers as an exploratory tool, by developing country modellers where data limitations are an issue, and as a teaching tool. |
Day |
http://www.osemosys.org |
Oemof |
|
|
Reiner Lemoine Institut / ZNES Flensburg |
Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al. |
|
|
|
|
Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al. |
some |
dispatch investment |
Deterministic |
|
|
Open Energy Modelling Framework |
|
Depends on user |
Depends on user |
false |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
https://oemof.org/libraries/ |
costs, emissions |
Energy Modelling Framework |
true |
Python, Pyomo, Coin-OR |
transmission distribution net transfer capacities DC load flow |
|
true |
true |
PostgreSQL, PostGIS |
|
Electricity Heat Mobility |
https://github.com/oemof/oemof/releases |
Renewables Conventional Generation CHP |
oemof is a framework for energy system model development and its application in energy system analysis. Currently, it bases on collaborative work of three institutions. You can clone/fork the code at github.
Containing a linear optimisation problem formulation library, feedin-data generation library and other auxiliary libraries it is meant to be developed further according to interests of user/ developer community. |
Hour |
https://oemof.org/ |
OnSSET |
|
|
KTH Royal Institute of Technology |
Dimitrios Mentis, Mark Howells, Holger Rogner, Alexandros Korkovelos, Christopher Arderne, Oliver Broad, Manuel Welsch, Francesco Fuso Nerini |
10.1016/j.esd.2015.09.007 |
Mentis, Dimitrios; Welsch, Manuel; Fuso Nerini, Francesco; Broad, Oliver; Howells, Mark; Bazilian, Morgan; Rogner, Holger (December 2015). "A GIS-based approach for electrification planning: a case study on Nigeria". Energy for Sustainable Development. 29: 142–150. doi:10.1016/j.esd.2015.09.007. ISSN 0973-0826. |
|
mentis@kth.se |
Dimitrios Mentis |
|
|
|
|
|
Open Source Spatial Electrification Tool |
|
Sub-Saharan Africa, developing Asia, Latin America |
1 km to 10 km |
false |
MIT license (MIT) |
|
Optimization |
|
Cost minimization |
|
true |
Python |
|
|
false |
true |
Python |
IEA World Energy Outlook 2014, IEA World Energy Outlook 2015, IEA and World Bank Global Tracking Framework 2015 |
|
https://github.com/KTH-dESA/PyOnSSET |
Renewables Conventional Generation |
OnSSET has been designed for identifying least-cost technology options to electrify areas presently unserved by grid-based electricity and to estimate associated investment needs related to electrification. OnSSET uses energy-related data and information on a geographical basis such as settlement sizes and locations, distances from existing and planned transmission network, power plants, economic activity, local renewable energy flows,road network, nighttime light etc. |
Multi year |
|
OpenTUMFlex |
|
|
Technical University of Munich |
Michel Zade, Babu Kumaran Nalini, Zhengjie You, Peter Tzscheuschler |
doi:10.3390/en13215617 |
Zade, M.; You, Z.; Kumaran Nalini, B.; Tzscheutschler, P.; Wagner, U. Quantifying the Flexibility of Electric Vehicles in Germany and California—A Case Study. Energies 2020, 13, 5617. |
|
|
Michel Zade, Babu Kumaran Nalini, Zhengjie You, Peter Tzscheuschler |
some |
|
|
|
How can prosumer offer flexibility to the grid?
Can prosumer flexibility be quantified? |
OpenTUMFlex |
|
User dependent |
User dependent |
false |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
|
Cost optimal optimization and flexibility calculation |
Energy System Model urban energy systems load shifting optimisation Local energy systems |
true |
Python (Pyomo) |
distribution |
|
false |
true |
|
|
Energy Electricity Market Households electricity plus sector coupling (EVs |
https://github.com/tum-ewk/OpenTUMFlex |
Renewables CHP |
An open-source flexibility estimation model that quantifies all possible flexibilities from the available prosumer devices and prices them. |
15 Minute |
https://www.ei.tum.de/en/ewk/forschung/projekte/c-sells/ |
PLEXOS Open EU |
|
|
University College Cork |
Paul Deane |
doi:10.1016/j.renene.2015.02.048 |
http://www.sciencedirect.com/science/article/pii/S0960148115001640 |
60 |
jp.deane@ucc.ie |
Paul Deane |
all |
dispatch |
None |
|
Cost of electricity in 2020
Congestion on Lines
Impact of carbon prices |
PLEXOS Open EU |
|
North West Europe |
Member State |
false |
|
|
Optimization |
Least Cost Optimization, Can be run in MIP or linear relaxed mode |
Minimize total Generation cost |
Market Model |
true |
PLEXOS |
net transfer capacities |
|
true |
false |
MS Excel |
|
Electricity Market |
http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios |
Renewables Conventional Generation |
Full Details available at
http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios |
Hour |
http://www.ucc.ie/en/energypolicy/ |
POMATO |
|
|
TU Berlin |
Richard Weinhold, Robert Mieth |
10.1016/j.softx.2021.100870 |
Weinhold, Richard, and Robert Mieth. 2021. “Power Market Tool (POMATO) for the Analysis of Zonal Electricity Markets.” SoftwareX 16 (December): 100870. |
|
riw@wip.tu-berlin.de |
Richard Weinhold |
some |
dispatch |
Chance Constrained |
|
|
Power Market Tool |
|
User-dependent |
Nodal resolution |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
|
Optimization |
Linear Economic Dispatch. Linear Optimal Power Flow. Linear Security Constrained Optimal Power Flow |
Cost minimization |
Network-constrained Unit Commitment and Economic Dispatch |
true |
Julia/JuMP |
transmission DC load flow net transfer capacities |
|
false |
true |
Python |
Schönheit, Weinhold, Dierstein (2020), The impact of different strategies for generation shift keys (GSKs) on the flow-based market coupling domain: A model-based analysis of Central Western Europe. https://doi.org/10.1016/j.apenergy.2019.114067.
Weinhold, Richard, and Robert Mieth. 2020. “Fast Security-Constrained Optimal Power Flow Through Low-Impact and Redundancy Screening.” IEEE Transactions on Power Systems 35 (6): 4574–84. https://doi.org/10.1109/TPWRS.2020.2994764.
Weinhold, Richard. 2021. “Evaluating Policy Implications on the Restrictiveness of Flow-Based Market Coupling with High Shares of Intermittent Generation: A Case Study for Central Western Europe.” ArXiv preprint 2109.04940v1. https://arxiv.org/abs/2109.04940.
Weinhold, Richard, and Robert Mieth. 2021. “Uncertainty-Aware Capacity Allocation in Flow-Based Market Coupling.” ArXiv preprint 2109.04968v2. https://arxiv.org/abs/2109.04968. |
Electricity Market Heat |
https://github.com/richard-weinhold/pomato |
Renewables Conventional Generation CHP |
POMATO stands for (POwer MArket TOol) and is an easy to use tool for the comprehensive analysis of the modern electricity market. It comprises the necessary power engineering framework to account for power flow physics, thermal transport constraints and security policies of the underlying transmission infrastructure, depending on the requirements defined by the user. POMATO was specifically designed to realistically model Flow-Based Market-Coupling (FBMC) and is therefore equipped with a fast security constrained optimal power flow algorithm and allows zonal market clearing with endogenously generated flow-based parameters, and redispatch. |
Hour |
https://github.com/richard-weinhold/pomato |
Pandapipes |
|
|
Fraunhofer IEE, Uni Kassel |
Dennis Cronbach, Daniel Lohmeier, Jolando Kisse, Simon Drauz,Tanja Kneiske |
|
https://www.pandapipes.org/references/ |
|
tanja.kneiske@ieg.fraunhofer.de |
Tanja Kneiske |
|
|
|
|
|
pandapipes |
|
|
|
false |
MIT license (MIT) |
|
Simulation |
|
|
|
false |
Python |
distribution |
|
true |
true |
|
|
|
https://github.com/e2nIEE/pandapipes |
Renewables |
An easy to use open source tool for fluid system modeling, analysis and optimization with a high degree of automation. |
|
http://www.pandapipes.org |
Pandapower |
|
|
|
Energy Management and Power System Operation (University of Kassel), Fraunhofer IEE |
10.1109/TPWRS.2018.2829021 |
L. Thurner, A. Scheidler, F. Schäfer et al, pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems, in IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 6510-6521, Nov. 2018 |
|
|
Leon Thurner, Alexander Scheidler |
some |
|
|
|
|
Pandapower |
|
|
|
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
|
|
Transmission Network Model |
true |
Python |
transmission distribution |
|
false |
true |
Pandas |
|
|
https://github.com/e2nIEE/pandapower/ |
Renewables Conventional Generation |
pandapower builds on the data analysis library pandas and the power system analysis toolbox PYPOWER to create an easy to use network calculation program aimed at automation of analysis and optimization in power systems. What started as a convenience wrapper around PYPOWER has evolved into a stand-alone power systems analysis toolbox with extensive power system model library, an improved power flow solver and many other power systems analysis functions. |
|
http://www.pandapower.org |
PowNet |
|
|
Singapore University of Technology and Design |
AFM Kamal Chowdhury, Jordan Kern, Thanh Duc Dang, Stefano Galelli |
http://doi.org/10.5334/jors.302 |
Chowdhury, A.F.M.K., Kern, J., Dang, T.D. and Galelli, S., 2020. PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5. |
|
k.chy0013@gmail.com |
AFM Kamal Chowdhury |
all |
dispatch |
Sensitivity analysis |
|
|
PowNet |
|
Laos, Cambodia, Thailand, any user-defined country or region |
High-voltage substation |
false |
MIT license (MIT) |
|
Optimization Simulation |
Mixed Integer Linear Program (MILP), DC Power Flow, Unit Commitment, Economic Dispatch |
Cost minimization |
Network-constrained Unit Commitment and Economic Dispatch |
true |
Python (Pyomo) |
transmission distribution DC load flow |
|
false |
true |
Python |
|
Electricity Electric power Energy |
https://zenodo.org/record/3462879#.XoL6T4gzZaQ |
Renewables Conventional Generation |
PowNet is a least-cost optimization model for simulating the Unit Commitment and Economic Dispatch (UC/ED) of large-scale (regional to country) power systems. In PowNet, a power system is represented by a set of nodes that include power plants, high-voltage substations, and import/export stations (for cross-border systems). The model schedules and dispatches the electricity supply from power plant units to meet hourly electricity demand in substations (at a minimum cost). It considers the techno-economic constraints of both generating units and high-voltage transmission network. The power flow calculation is based on a Direct Current (DC) network (with N-1 criterion), which provides a reasonable balance between modelling accuracy and data and computational requirements. |
Hour |
https://github.com/kamal0013/PowNet |
PowerMatcher |
|
|
Flexiblepower Alliance Network |
|
|
|
|
|
|
|
|
|
|
|
PowerMatcherSuite |
|
|
|
false |
Apache License 2.0 (Apache-2.0) |
|
|
|
|
|
true |
Java |
|
|
false |
true |
|
|
|
https://github.com/flexiblepower/powermatcher |
Renewables |
"The PowerMatcher is a smart grid coordination mechanism. It balances distributed energy resources (DER) and (flexible) loads ... The PowerMatcher core application provides the market mechanism for the determination of the market equilibrium, while the devices work as actors for demand and/or supply" |
|
http://flexiblepower.github.io/ |
PowerSimulations.jl |
|
|
NREL |
Clayton Barrows, Jose-Daniel Lara, Daniel Thom, Dheepak Krishnamurthy, Sourabh Dalvi |
|
|
1 |
clayton.barrows@nrel.gov |
Clayton Barrows |
|
dispatch |
scenario analysis |
|
|
PowerSimulations.jl |
|
Any |
nodal resolution (all nodes are included) |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Optimization |
Principal application is sequential quasi-static system optimization problems (production cost modeling). |
Least Cost |
quasii-static sequential unit-commitment and economic dispatch problems |
true |
Julia |
transmission AC load flow DC load flow net transfer capacities |
1,000,000 |
false |
true |
Julia |
|
Power system |
https://github.com/nrel-siip/PowerSimulations.jl |
Renewables Conventional Generation |
Flexible, modular, and scalable package for power system quasi-static analysis with sequential problem specification capabilities. |
Second |
https://github.com/nrel-siip/PowerSimulations.jl |
PowerSimulationsDynamics.jl |
|
|
NREL |
Jose-Daniel Lara, Rodrigo Henríquez-Auba |
|
|
|
nrel-siip@nrel.gov |
Clayton Barrows |
|
|
scenario analysis |
|
|
PowerSimulationsDynamics.jl |
|
|
Nodal resolution |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
PowerSimulationsDynamics.jl enables transient stability analysis of power systems through differential-algebraic equations and with forward differentiation to enable small-signal stability analysis. |
N/A |
Dynamic system simulation model library |
true |
Julia |
transmission AC load flow |
|
false |
true |
Julia |
|
Electric power Electricity electricity |
https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl |
Renewables Conventional Generation CHP |
|
Less than second |
https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl |
PowerSystems.jl |
|
|
NREL |
Clayton Barrows, Jose-Daniel Lara, Daniel Thom, Dheepak Krishnamurthy, Sourabh Dalvi |
|
|
0.1 |
nrel-siip@nrel.gov |
Clayton Barrows |
|
dispatch |
scenario analysis |
|
|
PowerSystems.jl |
|
Any |
Nodal resolution |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
PowerSystems.jl includes basic power flow and network matrix calculation capabilities. |
|
Optimization Simulation |
true |
Julia |
transmission AC load flow DC load flow net transfer capacities |
100,000 |
false |
true |
Julia |
|
Electricity Electricity Sector Electric power |
https://github.com/NREL-SIIP/PowerSystems.jl |
Renewables Conventional Generation |
The PowerSystems.jl package provides a rigorous data model using Julia structures to enable power systems analysis and modeling. In addition to stand-alone system analysis tools and data model building, the PowerSystems.jl package is used as the foundational data container for the PowerSimulations.jl and PowerSimulationsDynamics.jl packages. PowerSystems.jl supports a limited number of data file formats for parsing. |
Less than second |
https://github.com/NREL-SIIP/PowerSystems.jl |
Pvlib python |
|
|
|
This is a community supported tool. Contributors to each release are listed here: https://pvlib-python.readthedocs.io/en/stable/whatsnew.html. |
https://doi.org/10.21105/joss.00884 |
William F. Holmgren, Clifford W. Hansen, and Mark A. Mikofski. “pvlib python: a python package for modeling solar energy systems.” Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884 |
|
|
See: https://github.com/pvlib/pvlib-python#getting-support |
some |
|
|
|
|
pvlib python |
|
|
|
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
|
|
|
true |
Python |
|
|
false |
true |
NumPy, Pandas |
|
Electricity |
https://github.com/pvlib/pvlib-python |
Renewables |
pvlib python is a community supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. |
|
https://pvlib-python.readthedocs.io/en/stable/ |
PyLESA |
|
|
University of Strathclyde |
Andrew Lyden |
|
|
|
andrew.lyden@strath.ac.uk |
Andrew Lyden |
some |
dispatch |
perfect foresight |
|
|
Python for Local Energy Systems Analysis |
|
|
Local/Community/District |
false |
MIT license (MIT) |
|
Simulation |
|
Minimization of operational costs |
Local energy systems |
true |
Python |
AC load flow DC load flow |
|
false |
true |
Python |
|
electricity heat |
https://github.com/andrewlyden/PyLESA |
Renewables Conventional Generation |
PyLESA is an open source tool capable of modelling local energy systems containing both electrical and thermal technologies. It was developed with the aim of aiding the design of local energy systems. The focus of the tool is on modelling systems with heat pumps and thermal storage alongside time-of-use electricity tariffs and predictive control strategies. It is anticipated that the tool provides a framework for future development including electrical battery studies and participation in grid balancing mechanisms.
This tool was developed as part of a PhD, "Modelling and Design of Local Energy Systems Incorporating Heat Pumps, Thermal Storage, Future Tariffs, and Model Predictive Control " by Andrew Lyden. |
Hour |
|
PyPSA |
|
|
FIAS |
Tom Brown, Jonas Hörsch, David Schlachtberger |
https://doi.org/10.5334/jors.188 |
Journal of Open Research Software, 2018, 6 (1) |
|
brown@fias.uni-frankfurt.de |
Tom Brown |
all |
dispatch investment |
Not explicitly covered, but stochastic optimisation possible |
|
Power flow analysis, market analysis, total system investment optimisation, contingency analysis, sector coupling |
Python for Power System Analysis |
|
Europe, China, South Africa |
User dependent |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
Non-linear power flow; linear optimal power flow / investment optimisation |
Cost minimization |
Energy System Model |
true |
Python, Pyomo |
transmission distribution AC load flow DC load flow net transfer capacities |
|
false |
true |
Pandas |
https://pypsa.org/publications/ |
Electricity Heat Transport User-defined |
https://github.com/PyPSA/PyPSA |
Renewables Conventional Generation CHP |
PyPSA is a free software toolbox for simulating and optimising modern energy systems that include features such as variable wind and solar generation, storage units, sector coupling and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series. |
Hour |
https://www.pypsa.org/ |
QuaSi - GenSim |
|
|
Siz energieplus |
Tobias Maile, Simon Marx, Etienne Ott, Moira Peter, Heiner Steinacker, Matthias Stickel |
10.3390/en16176115 |
Maile, T.; Steinacker, H.; Stickel, M.W.; Ott, E.; Kley, C. Automated Generation of Energy Profiles for Urban Simulations. Energies 2023, 16, 6115. |
3 |
info@siz-energieplus.de |
Etienne Ott, Matthias Stickel |
all |
|
|
|
|
Generic Model for Thermal Building Simulation |
|
All |
|
false |
MIT license (MIT) |
|
Simulation |
EnergyPlus is used to perform a thermal building simulation |
|
building energy demand |
true |
EnergyPlus, OpenStudio, MS Excel, Ruby |
|
|
false |
true |
MS Excel |
To cite a specific version of GenSim, use:
Maile, T., Marx, S., Ott, E., Peter, M., Steinacker, H., & Stickel, M. (2023). GenSim v2.15 - Generic Building Simulation (part of QuaSi) (release). Zenodo. https://doi.org/10.5281/zenodo.10200807 |
electricity heat cold |
https://github.com/QuaSi-Software/GenSim |
|
GenSim - for "generic building simulation" - is a building simulation software using the EnergyPlus® simulation engine to generate high-resolution heating and cooling demand profiles as well as electricity demand profiles for buildings with various types of use. "Generic" in this context refers to a "generally valid" building model. This means that the software is versatile enough to simulate any type of building in a very flexible and simplified way, enabling users to efficiently adapt the software for any building design.
GenSim was specifically devloped for the use during project pre-planning where detailed simulations of buildings are challenging due to typically constrained time budgets and limited availability of information. Traditional simulation tools require extensive input data, making the process time-consuming. GenSim addresses this by providing presets for multiple building typologies and a streamlined approach for quick, simple, yet accurate building simulations. This is particularly valuable in early planning stages when only rough data about the planned buildings is available. If more detailed information (wall structure, detailed geometry, specific use, ...) is available about the building to be examined, this can be used for more precise results.
More information is available in the documentation: https://quasi-software.readthedocs.io/en/latest/gensim_user_manual/ |
15 Minute |
http://www.quasi-software.org/ |