User:RobbieIanMorrison/sandbox/work in progress 5

This page contains lead up material for transfer to live Wikipedia pages when appropriate. Namely the following pages:


 * Open energy system models — live page
 * Open energy system databases — live page

Material on the classification of energy system projects and models has been moved to another sandbox:


 * ../work in progress 10.

= Text =

Unused text buffer
Various programming languages have been used to write software, including: Python, R, GAMS, MathProg, C++, Java, Matlab, Octave, Mathematica, and Excel/VBA. A number of languages are used for the pre-processing and post-processing of data and for visualization, including: Excel, R, Matlab, Python, and Graphviz. Relational and object-relational databases are also used to manage datasets.

Deep Decarbonization Pathways Project researchers have analyzed model typologies and made recommendations for future developments.

Various national governments and the European Union are developing meta-data standards and putting key policy statistics and datasets online. This includes energy supply data and energy trading data. One key component is the SDMX Statistical Data and Metadata eXchange standard. Sponsors of SDMX include Eurostat and various UN agencies. The US Department of Energy publishes energy information for the United States. The availability of municipal energy data depends on data policies of the relevant city administrations and utility providers.

Modeling paradigm
The energy modeling projects listed all fall within the bottom-up (BU) paradigm. This means that a model is built by defining and assembling the key constituents from the underlying system at the appropriate level of detail and resolution. Depending on the modeling genre, these components will include technical elements (like power stations), institutional arrangements (typically spot markets), and sometimes decision agents (such as bidders, consumers, and householders). Unlike the top-down (TD) paradigm, bottom-up models exhibit low levels of abstraction.


 * They have very detailed, often economy-wide, linked maps of energy use from supply through to end-use demand, and their operating paradigm is the minimization of the lifecycle costs for specific intermediate and end-use energy demands through technology competition, often in response to capital, labor, energy and emissions price changes. Their strengths include an integrated full-system representation and an explicit recognition of the capital, operating and fuel costs that provides a basis for least-cost analysis, normally based on a financial discount rate. Because of their technical depth and capacity for modeling capital stock turnover, they can also model the effects of technology regulations, a common requirement of decision makers and typically a weakness of TD models (see later discussion). Their weaknesses are their data intensiveness, behavioral simplicity (cost minimization based on financial discount rates does not completely describe firm and household behavior), exogenous demands for energy services, lack of capacity to model the financial recycling effects of emissions charges and inability to model economic structural change. As a practical consideration, BU models (and all models that follow) typically have steep learning curves.

EMMA text

 * copyright violating text below — has been reworked
 * User talk:RobbieIanMorrison


 * "The model code as well as all input parameters and this documentation are freely available to the public under the Creative Commons BY-SA 3.0 license and can be downloaded from http://neon-energie.de/EMMA." (from EMMA website)

EMMA models both dispatch of and investment in power plants, minimizing the total costs with respect to investment, production, and trade decisions under a large set of technical constraints. In economic terms, it is a partial equilibrium model of the wholesale electricity market with a focus on the supply side. It calculates short-term or long-term optima (equilibria) and estimates the corresponding capacity mix as well as hourly prices, generation, and cross-border trade for each market area. Technically, EMMA is a pure linear program (no integer variables) with about two million non-zero variables. As of 2016 the model covers Belgium, France, Germany, the Netherlands, and Poland and supports renewable generation, conventional generation, and cogeneration.

EMMA has been used to study the economic effects of the increasing penetration of variable renewable energy (VRE), specifically solar power and wind power, in the Northwestern European power system. A 2013 study finds that increasing VRE shares will depress prices and, as a result, the competitive large-scale deployment of renewable generation will be more difficult to accomplish than many anticipate. A 2015 study estimates the welfare-optimal market share for wind and solar power. For wind, this is 20%, three times more than at present.

An external 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.

= Added projects =


 * some references may be duplicated here, relative to the main article

Dispa-SET

 * : content transferred
 * https://wiki.openmod-initiative.org/wiki/Dispa-SET

EnergyPATHWAYS

 * status: idle
 * useful blog: http://www.evolved.energy/#!EnergyPATHWAYS/mhqg1/56c7494b0cf25df9371fd02d

GENESIS

 * : to research and adjust

GENESIS is described in an open access publication.

NEMO

 * status: done
 * Elliston et al (2012) on simulations of scenarios with 100% renewable energy in the Australian NEM


 * Elliston et al (2013) on least cost 100% renewable electricity scenarios in the Australian NEM


 * Riesz et al (2013) on 100% renewables study
 * does not mention NEMO


 * Elliston et al (2014) on 100% renewables versus low emission fossil fuel scenarios


 * Riesz and Elliston (2014) on impact of technology availability on 100% renewables for Australia
 * two page summary


 * Wilkie et al (2015) on revenue sufficiency in the Australian NEM with high renewables shares
 * useful


 * Elliston et al (2016) on the incremental cost of renewable generation
 * section 2 describes the model


 * Riesz and Elliston (2016) on R+D priorities for renewable technologies


 * Riesz et al (2016) on a research summary of 100% renewables
 * only mentions NEMO in passing, might be better on Renewable energy in Australia

oemof

 * status: done
 * openmod posting dated 1 December 2016

OnSSET

 * : transferred
 * check against openmod listing in due course: https://wiki.openmod-initiative.org/wiki/OnSSET


 * local PDFs

Open Data RTE

 * : transferred

OSyMOSIS

 * : update reddit information
 * matrix reduction challenge
 * reddit thread to collect FAQs: https://www.reddit.com/r/optimuscommunity/comments/5ar1q2/osemosys_website_is_live/


 * Maggi (2016) Masters in Engineering thesis on OSeMOSYS


 * Lavigne (2017)
 * nice summary on generating a Pareto frontier
 * nice summary on generating a Pareto frontier


 * Lavigne (2016)

2017 update

 * Africa
 * http://www.sciencedirect.com/science/article/pii/S0973082615300065
 * http://www.osemosys.org/temba.html


 * South America
 * http://www.osemosys.org/samba-south-american-model-base.html


 * It has also been extended to include the Climate, Land, Energy, Water (CLEW) nexus for the United Nations.
 * A global model can be found here: https://unite.un.org/sites/unite.un.org/files/app-globalclews-v-1-0/landingpage.html
 * A country model for Mauritius can be found here: http://un-desa-modelling.github.io/clews-mauritius-presentation/
 * Models of the Sava river basin countries are reported here: http://www.savacommission.org/dms/docs/dokumenti/sava_nexus/savadraftnexusreport_8april2015_clean-for-review_sent.docx
 * Models of the Syr Darya river basin countries are reported here: https://www.unece.org/fileadmin/DAM/env/water/publications/WAT_Nexus/ECE_MP.WAT_46_Chap.7_ENG_Syr-Daria-Web_TF.pdf


 * Wikipedia
 * https://en.wikipedia.org/wiki/Syr_Darya
 * https://en.wikipedia.org/wiki/International_Sava_River_Basin_Commission
 * https://en.wikipedia.org/wiki/Sava

2017 update 2
For those of you using OSeMOSYS, please see some peer reviewed resources that may be of use. Please do forgive any double posting.

http://link.springer.com/article/10.1007/s12351-016-0246-9

The authors have coupled OSeMOSYS with a share of choice model

http://www.sciencedirect.com/science/article/pii/S2211467X16300128

Using OSeMOSYS to analyse the decarbonising the Alberta power system with carbon pricing

http://www.sciencedirect.com/science/article/pii/S2214629616300160

Adapting OSeMOSYS to develop an open-source model for unconventional participation to energy planning

http://www.sciencedirect.com/science/article/pii/S0973082615300065

All countries in Africa represented in a multi-regional expansion and trade analysis.

http://link.springer.com/chapter/10.1007/978-981-10-0974-7_4

Uses the OSeMOSYS functionality added to LEAP for national analysis

https://www.routledge.com/The-Water-Food-Energy-and-Climate-Nexus-Challenges-and-an-agenda-for/Dodds-Bartram/p/book/9781138190955

Chapter 2 Discusses an OSeMOSYS model based engagement for the SDGs​

Matrix generation fix
Jonas Hörsch wins OSeMOSYS challenge.

GLPK wikibook: very large MathProg models

pandapower

 * status: project added

Dump
We have recently released the new python open source software pandapower for convenient modeling and analysis of power systems, and we think this could be interesting for those of you working with electric power system analysis.

A few highlights of pandapower are:


 * data structure based on pandas tables allows comfortable data handling
 * convenient modeling of electric networks through the pandapower API
 * element based datastructure with comprehensive electric models for lines, 2-Winding transformers, 3-Winding transformers, ward-equivalents and more
 * a switch model that allows modelling of ideal bus-bus switches as well as bus-line / bus-trafo switches
 * power flow and optimal power flow based on PYPOWER, accelerated with just-in-time compilation in numba
 * possibility for topological graph searches on electric networks with networkx
 * plotting of networks with and without geographical information with matplotlib

Links:


 * Download: http://www.uni-kassel.de/go/pandapower
 * Documentation: http://www.uni-kassel.de/go/pp_docs
 * A short Introduction: http://www.uni-kassel.de/go/pp_introduction
 * Interactive Tutorials for an in depths introduction: http://www.uni-kassel.de/go/pp_tutorials

PyPSA

 * status: watching brief
 * awaiting paper (13-Dec-2016)
 * submitted an abstract to the SciGRID conference for essentially a condensed version of the documentation with more background about why new software was needed, the thinking behind the architecture, etc. This paper has to be submitted by March/April and then the peer review process will last a few months more.

SMARD

 * : now live

SWITCH

 * : update entry, add Nelson et al (2012) and Mileva et al (2016)
 * email from Felix Cebulla: As far as I understood, SWITCH was initially developed by the University of Hawaii. However, the later versions include some major developments from the Renewable and Appropriate Energy Laboratory (RAEL) at the University of Berkeley.
 * Fripp (2012) on the software


 * Nelson et al (2012)


 * Mileva et al (2016)

= Awaiting projects =



Other open energy models includes energy accounting models and distribution network models. Accounting models are often implemented using spreadsheets or relational databases.

CREST ?

 * : work-in-progress
 * micro-energy system spreadsheet model


 * https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/7773
 * CC BY-NC 3.0 license
 * https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/7773/5/CREST_Integrated_PV_electricity_demand_model_1.0B(1).xlsm

Project
Text text.


 * Richardson et al (2010) on domestic electricity use
 * add to PhD


 * Richardson and Thomson (2012) on one-minute model

DDPP Decarbonization Calculator

 * status: needs reliable secondary source / needs license details

DDPP Decarbonization Calculator
The DDPP Decarbonization Calculator is a spreadsheet-based energy system model used to explore different pathways to deep decarbonization. It is being developed by the Deep Decarbonization Pathways Project (DDPP), headquartered in Paris, France. The calculator consists of a single spreadsheet written in Excel/VBA. The project has a small website, from where the software can be downloaded. The user is responsible for gathering the necessary data. A manual is available.

The Decarbonization Calculator is intended to represent a simple energy-economy system that can be characterized using a reasonable small set of readily-found input data.

EINSTEIN

 * : work up material
 * EINSTEIN developers <>

EINSTEIN
Text text. energyXperts.NET (E4-Experts GmbH), Berlin, Germany.

EINSTEIN supports English and 10other European languages.

EnergyNumbers–Balancing

 * status: needs reliable secondary source

EnergyNumbers–Balancing
EnergyNumbers–Balancing is an interactive electricity system model. It is being developed by the UCL Energy Institute, University College London, London, United Kingdom. The project maintains an interactive website. Users can request access to the codebase by twitter. EnergyNumbers-Balancing is programmed in Fortran, PHP, JavaScript, HTML, and CSS.

The model uses historic demand data and historic one (or half) hourly capacity factors for photovoltaics and wind generation to simulate the extent to which demand could be met by some combination of wind, photovoltaics, and storage. , Britain, Germany, and Spain are supported.

energyRt

 * status: needs reliable secondary source / research further though
 * http://wiki.openmod-initiative.org/wiki/EnergyRt
 * https://www.edf.org/people/oleg-lugovoy
 * based in US, lead developer works for Environmental Defense Fund
 * Oleg Lugovoy (energyRt)  (creator)
 * Vladimir Potashnikov (energyRt) 

energyRt
energyRt stands for energy systems modeling R-toolbox. , the project is in development. Basic reference energy system (RES) models are currently supported, but features like regions and storage technologies are in planning. The code is hosted on GitHub. The software is written in R and can use either GAMS or GLPK as its optimization solver. There is no documentation at present. Nor are demonstration models available. The project advocates and uses reproducible research techniques based on RStudio and knitr.

The energyRt software produces a pure linear (no integer variables) cost-minimization problem which is then passed to the selected solver. The design of energyRt shares similarities with bottom-up models like TIMES/MARKAL or OSeMOSYS.

German Green Growth Model

 * status: watching brief / not yet released

German Green Growth Model
The German Green Growth Model (GGGM) is an agent-based model designed to improve the understanding of the costs and benefits of climate and energy policy for Germany. It is being developed by the Global Climate Forum, based in Berlin, Germany.

GnuAE

 * status: work-in-progress
 * license: GPLv2


 * https://www.gnu.org/software/gnuae/
 * https://directory.fsf.org/wiki/Gnuae
 * https://www.gnu.org/software/gnuae/manual/

MultiMod

 * status: watching brief / not yet released
 * Daniel Huppmann
 * first top-down model?
 * http://wiki.openmod-initiative.org/wiki/MultiMod

MultiMod
MultiMod is the energy system and resource market model.

PLEXOS Open EU

 * status: watching brief / not yet open
 * http://wiki.openmod-initiative.org/wiki/PLEXOS_Open_EU
 * http://www.sciencedirect.com/science/article/pii/S0960148115001640

PLEXOS Open EU
Text text.

Rogeaulito
the former are typically developed by economists based on economic indices of prices and elasticities exploring macro-economic effects of a certain type of policy often using econometric methods, the latter are typically developed by engineers based on detailed descriptions of end-use and production technologies and cost structures (physical accounting). (text from B+M (2014))
 * : to research and add / awaiting response to my email for license details
 * : to update openmod wiki
 * location: User:RobbieIanMorrison/sandbox/work_in_progress_8
 * Markandya, A., and Halsnaes, K. (2001). “Costing methodologies,” in Climate Change 2001: Mitigation. Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change IPCC, eds B. Metz, O. Davidson, R. Swart, and J. Pan (Cambridge: Cambridge University Press), 451–498.
 * Markandya, A., and Halsnaes, K. (2001). “Costing methodologies,” — look up
 * The terms "top-down" and "bottom-up" are analytical approaches and shorthand for aggregated and disaggregated models of demand and supply. While
 * AR5 WG3 p238 for definitions

Spreadsheet validity diversion

 * Hermans and Murphy-Hill (2015) on Enron spreadsheets
 * PDF currently under review, so it should have been published too
 * Enron's spreadsheets are more smelly than the usual corpus


 * Koc and Tansel (2011) survey of version control systems
 * mentions some spreadsheets offer some version control features
 * mentions some spreadsheets offer some version control features

Open science diversion

 * Schwab, M., Karrenbach, N., and Claerbout, J. (2000). "Making scientific computations reproducible". Comput. Sci. Eng. 2, 61–67.
 * Stodden, V. C. (2010). "Reproducible research: addressing the need for data and code sharing in computational science". Comput. Sci. Eng. 12, 8–12.
 * Hanson, B., Sugden, A., and Alberts, B. (2011). "Making data maximally available". Science 331, 649–649.
 * Peng, R. D. (2011). "Reproducible research in computational science". Science 334, 1226–1227.


 * Ince et al (2012) on open computer programs

AIM diversion

 * Asia-Pacific Integrated Model (AIM)
 * http://www-iam.nies.go.jp/aim/
 * no evidence that it is open-source
 * local papers in the root directory
 * http://www.decisioncraft.com/energy/papers/ecc/eghgma/aimj.pdf
 * http://www-iam.nies.go.jp/aim/publications/report/1998/oecd.pdf

DICE diversion

 * DICE model
 * Nordhaus and Boyer (2000)


 * Newbold (2010) (EPA summary)

StELMOD

 * : research and add / priority
 * http://wiki.openmod-initiative.org/wiki/StELMOD

StELMOD
The short-run stochastic unit commitment model stELMOD creates a market dispatch and calculates the associated physical electricity flows.

GAMS and CPLEX.

UKTM

 * status: watching brief / not yet released
 * energy system model

UKTM
The UKTM or UK TIMES model is an open source implementation of the TIMES model for the United Kingdom.

Follow up

 * UCL annual review 2015-2016 and look for 'Research Theme: Energy Systems': https://www.bartlett.ucl.ac.uk/energy/docs/ucl-energy-annual-review-online-2015-2016 (done)
 * EPSRC research grant for wholeSEM: http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K039326/1


 * Fais et al (2014) presentation on technology pathways
 * not found


 * Strachan et al (2016) on reinventing the modelling-policy interface
 * paywalled, requested on Wikipedia on 13 November 2016
 * Blurb: stresses that energy modelling has a crucial underpinning role for policy making, proposing four key improvements to ensure that the modelling–policy interface delivers the insights that decision makers need
 * Abstract: Energy modelling has a crucial underpinning role for policy making, but the modelling–policy interface faces several limitations. A reinvention of this interface would better provide timely, targeted, tested, transparent and iterated insights from such complex multidisciplinary tools.


 * Dodds et al (2015) on model archaeology
 * Abstract: In common with other types of complex models, energy system models have opaque structures, making it difficult to understand both changes between model versions and the extent of changes described in research papers. In this paper, we develop the principle of model archaeology as a formal method to quantitatively examine the balance and evolution of energy system models, through the ex post analysis of both model inputs and outputs using a series of metrics. These metrics help us to understand how models are developed and used and are a powerful tool for effectively targeting future model improvements. The usefulness of model archaeology is demonstrated in a case study examining the UK MARKAL model. We show how model development has been influenced by the interests of the UK government and the research projects funding model development. Despite these influences, there is clear evidence of a strategy to balance model complexity and accuracy when changes are made. We identify some important long-term trends including higher technology capital costs in subsequent model versions. Finally, we discuss how model archaeology can improve the transparency of research model studies.
 * Abstract: In common with other types of complex models, energy system models have opaque structures, making it difficult to understand both changes between model versions and the extent of changes described in research papers. In this paper, we develop the principle of model archaeology as a formal method to quantitatively examine the balance and evolution of energy system models, through the ex post analysis of both model inputs and outputs using a series of metrics. These metrics help us to understand how models are developed and used and are a powerful tool for effectively targeting future model improvements. The usefulness of model archaeology is demonstrated in a case study examining the UK MARKAL model. We show how model development has been influenced by the interests of the UK government and the research projects funding model development. Despite these influences, there is clear evidence of a strategy to balance model complexity and accuracy when changes are made. We identify some important long-term trends including higher technology capital costs in subsequent model versions. Finally, we discuss how model archaeology can improve the transparency of research model studies.


 * Fais et al (2016) on the critical role of the industrial sector
 * application of UKTM ?
 * ResearchGate
 * download after 16 January 2017: http://discovery.ucl.ac.uk/1490701/


 * Pye et al (2016) on exploring national decarbonization pathways and global energy trade flows
 * part of DDPP


 * Trutnevyte et al (2016)
 * ResearchGate

WWS project

 * : to complete and add
 * email addresses
 * Mark Jacobson <>
 * Mark Delucchi <>
 * energy system model
 * email from Felix Cebulla: I worked with Mark Jacobson and during my time at Stanford. Just wanted to point out that there is a paper on 100% renewable (WWS) scenarios for 50 states of the U.S. which one could at as a reference: http://dx.doi.org/10.1039/C5EE01283J. Moreover, a similar paper, but for 139 countries of the world, is currently in review.

WWS project
The WWS (wind, water, and sunlight) project produces roadmaps for 139countries through which they can achieve fully renewable energy systems by 2050. The project is coordinated by the Atmosphere/Energy Program at Stanford University, California, USA.

The methods used have been the subject of academic controversy.

Links

 * Draft:Open energy system models
 * Energiewende in Germany
 * Mark Z. Jacobson
 * spreadsheets
 * http://web.stanford.edu/group/efmh/jacobson/Articles/I/WWS-50-USState-plans.html
 * Mark Delucchi , University of California, Davis or University of California, Berkeley

Text

 * from

The Atmosphere/Energy Program at Stanford University has developed roadmaps for 139 countries to achieve energy systems powered only by wind, water, and sunlight (WWS) by 2050. In the case of Germany, total end-use energy drops from 375.8 GW for business-as-usual to 260.9 GW under a fully renewable transition. Load shares in 2050 would be: on-shore wind 35%, off-shore wind 17%, wave 0.08%, geothermal 0.01%, hydro-electric 0.87%, tidal 0%, residential PV 6.75%, commercial PV 6.48%, utility PV 33.8%, and concentrating solar power 0%. The study also assess avoided air pollution, eliminated global climate change costs, and net job creation. These co-benefits are substantial.

= Open energy system databases =

OpenGridMap

 * : transferred

OpenGridMap
Not used.

Power Match

 * : add when goes live later in 2017

Power Match
Text text.

UK model quality assurance report

 * UK model quality assurance report (does not mention energy models though)

Open energy-economy models
Energy-economy models (also called energy-economy-environment models) combine a simplified energy system and a regional global economy.

Template / model

 * : add status

Project
Text text.

Template / database

 * : add status

Project
Text text.