wiki.openmod-initiative.org - User contributions [en]

SimSEE

2022-07-09T20:38:48Z

Ruben Chaer:

{{Model
|Full_Model_Name=Simulator of System of Electrical Energy.
|Acronym=SimSEE
|author_institution=Institute of Electrical Engineering
|authors=Ruben Chaer, Pablo Alfaro y Gonzalo Casaravilla
|contact_persons=Ruben Chaer
|contact_email=rchaer@simsee.org
|website=https://simsee.org/index_en.html
|source_download=https://sourceforge.net/projects/simsee/
|logo=Logosimseesinmarco 02.jpg
|text_description=SimSEE is a platform for the Simulation of Systems of Electrical Energy. As such, it allows creating simulators tailored to a generation system, simply by adding the different types of Actors (thermal, wind, solar and hydraulic generators, demand, interconnections, etc.) to a Play-Room (simulation environment). These Actors behave in the Room according to their type.

It is 100% programmed with Object Oriented technology which makes it easy to incorporate new models (types of Actors).

To simulate the optimal operation of an Electric Power System, SimSEE solves a Dynamic Stochastic Programming problem, obtaining as a result an Optimal Operation Policy. Using this Policy, different realizations of the stochastic processes (chronicles or possible histories of the future of the system) are simulated.

Since 2010, SimSEE has become the tool commonly used in Uruguay to simulate the operation of the energy system, mainly due to the good stochastic models developed for the modeling of wind and solar energy.
These models achieve an adequate representation, both in the long term (Investment Planning) and in the short term (System Operation).
|Primary outputs=Optimal energy dispatch of the energy resources.
|Support=https://simsee.org/contacto_en.php
|Framework=freepascal
|User documentation=https://simsee.org/simsee/verdoc/vol1_en.php
|Code documentation=https://sourceforge.net/p/simsee/src/HEAD/tree/
|Source of funding=academic projects
|Number of developers=more than 10
|Number of users=more than 100
|open_source_licensed=Yes
|license=GNU General Public License version 3.0 (GPL-3.0)
|model_source_public=Yes
|Link to source=https://sourceforge.net/p/simsee/src/HEAD/tree/
|data_availability=some
|open_future=No
|modelling_software=freepascal
|processing_software=freepascal
|GUI=Yes
|model_class=Optimal energy dispatch
|sectors=Electricity Market,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Industry
|Transfer (Electricity)=Transmission
|Storage (Electricity)=Battery, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|decisions=dispatch, investment
|Changes in efficiency=Can be affected by the temperature
|timeresolution=Hour
|network_coverage=net transfer capacities
|Observation period=Less than one month, Less than one year, More than one year
|Additional dimensions (Ecological)=compute greenhouse emissions
|Additional dimensions (Economical)=sopo prices, marginal costs, time-series
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=Optimal Stochastic Dynamic Programming solver for computation of the operational Policy and a Monte Carlo style simulator of the system using the computed Policy
|math_objective=minimization of the future operational cost.
|deterministic=stochastic, hydro inflows, wind velocity, solar radiation, temerature an Demand.
|is_suited_for_many_scenarios=Yes
|number_of_variables=1000
|montecarlo=Yes
|computation_time_minutes=15
|computation_time_hardware=desktop with 8 cpus
|computation_time_comments=the 15 min with 8 cpus is for a optimizaion/simulation of the uruguayan system over 10 years horizon with a daily time-step
|citation_references= Chaer, R. (2008.). Simulación de sistemas de energía eléctrica. Tesis de maestría. Universidad de la Republica (Uruguay). Facultad de Ingenieria.
|report_references=Chaer R. (2018) Handling the Intermittence of Wind and Solar Energy Resources, from Planning to Operation. Uruguay’s Success. September 2018 Conference: 36th USAEE/IAEE NORTH AMERICAN CONFERENCEAt: Washington DC USA
|Larger scale usage=long term investment planning (typically 20 years)
|Model validation=The model was tested using a set of configurations of the Uruguayan electrical system. For example, conditions of drought or abundance of the hydroelectric resource and starting the simulations with the lakes full or empty and comparing the results with the result of the expert operators.
|Comment on model validation=Once this series of tests was carried out, the model began to be used for the programming of the operation on all time scales by the ISO of Uruguay
|Specific properties=It uses its own stochastic modeling technique which facilitates the assimilation of forecasts
|Model input file format=No
|Model file format=No
|Model output file format=No
}}
#REDIRECT [[SimSES]]

SimSEE

2022-07-09T20:31:37Z

Ruben Chaer: Ruben Chaer moved page SimSEE to SimSES over redirect

#REDIRECT [[SimSES]]

SimSES

2022-07-09T20:31:37Z

Ruben Chaer: Ruben Chaer moved page SimSEE to SimSES over redirect

{{Model
|Full_Model_Name=Simulation of stationary energy storage systems
|Acronym=SimSES
|author_institution=Technical University of Munich
|authors=Marc Möller, Daniel Kucevic, Nils Collath, Anupam Parlikar, Petra Dotzauer, Benedikt Tepe, Stefan Englberger, Martin Cornejo, Andreas Jossen, Holger Hesse, Maik Naumann, Nam Truong
|contact_persons=Martin Cornejo
|contact_email=simses.ees@ed.tum.de
|website=https://www.ei.tum.de/ees/simses/
|source_download=https://gitlab.lrz.de/open-ees-ses/simses
|text_description=SimSES provides a library of state-the-art energy storage models by combining modularity of multiple topologies as well as the periphery of an ESS. This paper summarizes the structure as well as the capabilites of SimSES. Storage technology models based on current research for lithium-ion batteries, redox flow batteries, as well as hydrogen storage-based electrolysis and fuel cell are presented in detail. In addition, thermal models and their corresponding HVAC systems, housing, and ambient models are depicted. Power electronics are represented with AC/DC and DC/DC converters mapping the main losses of power electronics within a storage system. Additionally, auxiliary components like pumps, compressors, and HVAC are considered. Standard use cases like peak shaving, residential storage, and control reserve power provisions through dispatch of storage are discussed in this work, with the possibility to stack these applications in a multi-use scenario. The analysis is provided by technical and economic evaluations illustrated by KPIs.
|User documentation=https://gitlab.lrz.de/open-ees-ses/simses
|Code documentation=https://gitlab.lrz.de/open-ees-ses/simses
|Number of developers=6
|open_source_licensed=Yes
|license=BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
|model_source_public=Yes
|Link to source=https://gitlab.lrz.de/open-ees-ses/simses
|data_availability=all
|open_future=No
|modelling_software=Python
|processing_software=Python
|GUI=No
|model_class=Electrical energy storage system
|sectors=Electricity,
|technologies=Renewables
|Demand sectors=Households, Industry, Commercial sector, Other
|Energy carriers (Renewable)=Sun, Wind
|Storage (Electricity)=Battery, Chemical
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Load profiles
|Market models=Profiles
|decisions=dispatch
|Changes in efficiency=Temperature, power
|georegions=World
|timeresolution=Minute
|Observation period=Less than one month, Less than one year, More than one year
|Additional dimensions (Economical)=NPV, ROI, IRR, LCOE
|Additional dimensions (Other)=Battery aging, battery energy efficiency
|math_modeltype=Simulation
|math_modeltype_shortdesc=Power flow and state of charge calculation based on time series profiles
|is_suited_for_many_scenarios=Yes
|number_of_variables=>50
|montecarlo=Yes
|computation_time_minutes=27
|computation_time_hardware=Workstation
|computation_time_comments=20 years with 5 minute time step resolution
|citation_references=Naumann, Maik; Truong, Cong Nam (2017): SimSES - Software for techno-economic simulation of stationary energy storage systems.
|citation_doi=10.14459/2017mp1401541
|report_references=Naumann, M; Truong, C.N.; Schimpe, M.; Kucevic, D.; Jossen, A.; Hesse, H.C. (2017): SimSES: Software for techno-economic Simulation of Stationary Energy Storage Systems. In: VDE-ETG-Kongress 2017. Bonn. Preprint accepted for publication in IEEE Conference Proceedings. http://ieeexplore.ieee.org/document/8278770/

Naumann, M.; Karl, R.Ch.; Truong, C.N.; Jossen, A.; Hesse, H.C. (2015): Lithium-ion Battery Cost Analysis in PV-household Application. In: Energy Procedia 73, S. 37–47. DOI: 10.1016/j.egypro.2015.07.555

Truong, C.; Naumann, M.; Karl, R.; Müller, M.; Jossen, A.; Hesse, H. (2016): Economics of Residential Photovoltaic Battery Systems in Germany. The Case of Tesla’s Powerwall. In: Batteries 2 (2), S. 14–30. DOI: 10.3390/batteries2020014
|example_research_questions=Optimal system sizing and operation due to battery aging or economic results
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

SimSES

2022-07-09T20:29:42Z

Ruben Chaer: Ruben Chaer moved page SimSES to SimSEE

File:Logosimseesinmarco 02.jpg

2022-07-09T19:15:55Z

Ruben Chaer:

== Summary ==

== Licensing ==
{{license_ownwork_default}}