Wikipedia

User:RobbieIanMorrison/sandbox/work in progress 5

< User:RobbieIanMorrison‎ | sandbox
Open energy system models and databases / buffer

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

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


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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.[1]: S30–S31 

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

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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

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"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.[2][3]

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.[4] 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.[5]

An external 2015 study reviews the EMMA model and comments on the high assumed specific costs for renewable investments.[6]: 6 

References

  1. ^ Cite error: The named reference pye-and-bataille-2016 was invoked but never defined (see the help page).
  2. ^ Cite error: The named reference hirth-2016 was invoked but never defined (see the help page).
  3. ^ Hirth, Leon. The economics of wind and solar variability: how the variability of wind and solar power affects their marginal value, optimal deployment, and integration costs — PhD thesis (PDF). Berlin, Germany: Technical University of Berlin. doi:10.14279/depositonce-4291. Retrieved 2016-07-07.
  4. ^ Hirth, Lion (2013). "The market value of variable renewables: the effect of solar wind power variability on their relative price" (PDF). Energy Economics. 38: 218–236. doi:10.1016/j.eneco.2013.02.004. Retrieved 2016-07-09.
  5. ^ Hirth, Leon (2015). "The optimal share of variable renewables: how the variability of wind and solar power affects their welfare-optimal deployment" (PDF). The Energy Journal. 36 (1): 127–162. doi:10.5547/01956574.36.1.6. Retrieved 2016-07-07.
  6. ^ Cite error: The named reference zerrahn-and-schill-2015 was invoked but never defined (see the help page).

Added projects

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

Dispa-SET

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EnergyPATHWAYS

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GENESIS

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  • status: to research and adjust

GENESIS is described in an open access publication.[1]: sec 2 

Sources

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  • Bussar et al (2014)[2]
    • evince ~/synk/pdfs/2014-bussar-etal-optimal-allocation-capacity-energy-storage-systems-europe-100-pc-renewable-energy.pdf &
  • Bussar et al (2015)[1]
    • evince ~/synk/pdfs/2015-bussar-large-scale-integration-renewable-energies-storage-european-2050.pdf &
  • Bussar et al (2016a)[3]
    • evince ~/synk/pdfs/2016-bussar-etal-large-scale-integration-renewable-energies-storage-european-2050-sensitivity.pdf &
  • Bussar et al (2016b)[4]
    • evince ~/synk/pdfs/2016-bussar-etal-calculation-large-scale-long-term-power-system-evolution.pdf &
  • Bussar et al (2017) on first optimization results[5]
    • evince ~/synk/pdfs/2017-bussar-etal-long-term-power-system-evolution-first-optimisation-results-genesys.pdf &

Supporting references for Bussar et al (2015)

  • Hansen and Ostermeier (1996) on the CMA-ES (Covariance Matrix Adaptation Evolution Strategy)[6]
    • evince ~/synk/pdfs/1996-hansen-and-ostermeier-adapting-arbitrary-normal-mutation-distributions-evolutionary-strategies.pdf &
  • Thien et al (2015) on life cycle cost calculations[7]
    • evince ~/synk/pdfs/2015-thien-etal-ch21-life-cycle-cost-calculation.pdf &
  • Bussar et al (2017) conference paper[5]
    • try and download paper
    • evince ~/synk/genesys/literature/235-c-bussar-ires2017.pdf &

References

  1. ^ a b Bussar, Christian; Stöcker, Philipp; Cai, Zhuang; Moraes, Jr, Luiz; Alvarez, Ricardo; Chen, Hengsi; Breuer, Christopher; Moser, Albert; Leuthold, Matthias; Sauer, Dirk Uwe (1 June 2015). "Large-scale integration of renewable energies and impact on storage demand in a European renewable power system of 2050" (PDF). Energy Procedia. 73: 145–153. doi:10.1016/j.egypro.2015.07.662. ISSN 1876-6102. Retrieved 2016-12-07.  
  2. ^ Bussar, Christian; Moos, Melchior; Alvarez, Ricardo; Wolf, Philipp; Thien, Tjark; Chen, Hengsi; Cai, Zhuang; Leuthold, Matthias; Sauer, Dirk Uwe; Moser, Albert (2014). "Optimal allocation and capacity of energy storage systems in a future European power system with 100% renewable energy generation" (PDF). Energy Procedia. 46: 40–47. doi:10.1016/j.egypro.2014.01.156. Retrieved 2016-07-07.
  3. ^ Bussar, Christian; Stöcker, Philipp; Cai, Zhuang; Moraes Jr, Luiz; Magnor, Dirk; Wiernes, Pablo; van Bracht, Niklas; Moser, Albert; Sauer, Dirk Uwe (2016). "Large-scale integration of renewable energies and impact on storage demand in a European renewable power system of 2050 – Sensitivity study". Journal of Energy Storage. 6: 1–10. doi:10.1016/j.est.2016.02.004.
  4. ^ Bussar, Christian; Stöcker, Philipp; Cai, Zhuang; Moraes Jr, Luiz; Sauer, Dirk Uwe (2016). Calculation of large scale long-term power system evolution. 10th International Renewable Energy Storage Conference (IRES 2016).
  5. ^ a b Bussar, Christian; Stöcker, Philipp; Moraes Jr, Luiz; Jacqué, Kevin; Axelsen, Hendrik; Sauer, Dirk Uwe (2017). The long-term power system evolution: first optimisation results (PDF). 11th International Renewable Energy Storage Conference (IRES 2017). Retrieved 2017-05-23. Cite error: The named reference "bussar-etal-2017" was defined multiple times with different content (see the help page).
  6. ^ Hansen, Nikolaus; Ostermeier, Andreas (20–22 May 1996). "Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation" (PDF). Proceedings of IEEE International Conference on Evolutionary Computation (ICEC '96). pp. 312–317. doi:10.1109/ICEC.1996.542381. ISBN 0-7803-2902-3. Retrieved 2016-12-07. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
  7. ^ Thien, Tjark; Blank, Tobias; Lunz, Benedikt; Sauer, Dirk Uwe (2015). "Chapter 21: Life cycle cost calculation and comparison for different reference cases and market segments". In Moseley, Patrick T; Garche, Jüren (eds.). Electrochemical energy storage for renewable sources and grid balancing (PDF). Amsterdam, Netherlands: Elsevier. pp. 437–452. ISBN 978-0-444-62616-5. Retrieved 2016-12-08. URL is for a pre-press draft.

NEMO

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  • status: done


  • Elliston et al (2012) on simulations of scenarios with 100% renewable energy in the Australian NEM[1]
    • evince ~/synk/pdfs/2012-elliston-etal-simulations-of-scenarios-with-100pc-renewable-electricity-australian-nem-preprint.pdf &
  • Elliston et al (2013) on least cost 100% renewable electricity scenarios in the Australian NEM[2]
  • Riesz et al (2013) on 100% renewables study[3]
    • does not mention NEMO
    • evince ~/synk/pdfs/2013-riesz-etal-submission-100pc-renewables-study-modelling-outcomes.pdf &
  • Elliston et al (2014) on 100% renewables versus low emission fossil fuel scenarios[4]
    • evince ~/synk/pdfs/2014-elliston-etal-comparing-least-cost-renewables-with-low-emission-fossil-fuel-scenarios-australian-nem-preprint.pdf &
  • Riesz and Elliston (2014) on impact of technology availability on 100% renewables for Australia[5]
    • two page summary
    • evince ~/synk/pdfs/2014-riesz-and-elliston-impact-technology-availability-cost-100pc-renewables-australia-summary.pdf &
  • Wilkie et al (2015) on revenue sufficiency in the Australian NEM with high renewables shares[6]
    • useful
    • evince ~/synk/pdfs/2015-wilkie-etal-revenue-sufficiency-australian-nem-with-high-renewables.pdf &
  • Elliston et al (2016) on the incremental cost of renewable generation[7]
    • section 2 describes the model
    • evince ~/synk/pdfs/2016-elliston-incremental-cost-renewable-generation-australian-nem-case-study.pdf &
  • Riesz and Elliston (2016) on R+D priorities for renewable technologies[8]
    • evince ~/synk/pdfs/2016-riesz-and-elliston-r-and-d-priorities-for-renewable-technologies-preprint.pdf &
  • Riesz et al (2016) on a research summary of 100% renewables[9]
    • only mentions NEMO in passing, might be better on Renewable energy in Australia
    • evince ~/synk/pdfs/2016-riesz-etal-100pc-renewables-australia-ceem-unsw.pdf &

References

  1. ^ Elliston, Ben; Diesendorf, Mark; MacGill, Iain (June 2012). "Simulations of scenarios with 100% renewable electricity in the Australian National Electricity Market" (PDF). Energy Policy. 45: 606–613. doi:10.1016/j.enpol.2012.03.011. ISSN 0301-4215. Retrieved 2016-12-03. Preprint URL given.
  2. ^ Elliston, Ben; MacGill, Iain; Diesendorf, Mark (August 2013). "Least cost 100% renewable electricity scenarios in the Australian National Electricity Market". Energy Policy. 59: 270–282. doi:10.1016/j.enpol.2013.03.038. ISSN 0301-4215.
  3. ^ Riesz, Jenny; Elliston, Ben; MacGill, Iain; Diesendorf, Mark (May 2013). Submission on 100 percent renewables study — Draft modelling outcomes report (PDF). Sydney, Australia: Centre for Energy and Environmental Markets (CEEM), University of NSW (UNSW). Retrieved 2016-12-03.
  4. ^ Elliston, Ben; MacGill, Iain; Diesendorf, Mark (June 2014). "Comparing least cost scenarios for 100% renewable electricity with low emission fossil fuel scenarios in the Australian National Electricity Market" (PDF). Renewable Energy. 66: 196–204. doi:10.1016/j.renene.2013.12.010. ISSN 0960-1481. Lead-up URL given.
  5. ^ Riesz, Jenny; Elliston, Ben (2014). The impact of technology availability on the costs of 100% renewable electricity generation scenarios for Australia (PDF). Sydney, Australia: School of Electrical Engineering and Telecommunications and Centre for Energy and Environmental Markets (CEEM), University of New South Wales (UNSW). Retrieved 2016-12-03. Two page summary.
  6. ^ Wilkie, Oscar; MacGill, Iain; Bruce, Anna (8–10 December 2015). Revenue sufficiency in the Australian National Electricity Market with high penetrations of renewable energy (PDF). 2015 Asia-Pacific Solar Research Conference, Australian PV Institute. Brisbane, Australia. Retrieved 2016-12-03.
  7. ^ Elliston, Ben; Riesz, Jenny; MacGill, Iain (September 2016). "What cost for more renewables? The incremental cost of renewable generation — An Australian National Electricity Market case study" (PDF). Renewable Energy. 95: 127–139. doi:10.1016/j.renene.2016.03.080. ISSN 0960-1481. Retrieved 2016-12-03. Preprint URL given.
  8. ^ Riesz, Jenny; Elliston, Ben (2016). Research and deployment priorities for renewable technologies: quantifying the importance of various renewable technologies for low cost, high renewable electricity systems in an Australian case study (PDF). Sydney, Australia: Centre for Energy and Environmental Markets (CEEM) and School of Electrical Engineering and Telecommunications, University of New South Wales (UNSW). Retrieved 2016-12-03. Preprint.
  9. ^ Riesz, Jenny; Elliston, Ben; Vithayasrichareon, Peerapat; MacGill, Iain (March 2016). 100% renewables in Australia: a research summary — CEEM working paper (PDF). Sydney, Australia: Centre for Energy and Environmental Markets (CEEM), University of NSW (UNSW). Retrieved 2016-12-03.

oemof

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  • status: done
  • openmod posting dated 1 December 2016

OnSSET

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Project Host License Access Coding Documentation Scope/type
OnSSET KTH Royal Institute of Technology MIT GitHub Python website, GitHub cost-effective electrification

References

  • local PDFs
    • evince ~/synk/pdfs/2015-mentis-etal-first-global-application-open-source-spatial-electrification-tool-onsset.pdf &
    • evince ~/synk/pdfs/2016-arderne-climate-land-use-energy-water-nexus-assessment-bolivia.pdf &
    • evince ~/synk/pdfs/2016-berndtsson-open-geospatial-data-for-energy-planning-msc.pdf &
    • evince ~/synk/pdfs/2016-nerini-etal-cost-comparison-technology-approaches-improving-access-electricity-services.pdf &

Open Data RTE

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  • status: transferred
Project Host License Access Data formats Scope/type
Open Data RTE French RTE CC BY 2.0 compatible website, API JSON, CSV, XLS, SHP French electricity system

OSyMOSIS

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  • status: update reddit information



  • Maggi (2016) Masters in Engineering thesis on OSeMOSYS[1]
  • Lavigne (2017)[2]
    • evince ~/synk/pdfs/2017-lavigne-osemosys-energy-modeling-utopia.pdf &
    • nice summary on generating a Pareto frontier
  • Lavigne (2016)[3]
    • evince ~/synk/pdfs/2016-lavigne-teaching-energy-modeling-graduate-students-osemosys.pdf &

References

  1. ^ Maggi, Cristina (28 September 2016). Accounting for the long term impact of high renewable shares through energy system models: a novel formulation and case study (ME). Milan, Italy: Polytechnic University of Milan. high renewable shares, long term strategies, energy system models, open source, OSeMOSYS, operating reserve, electric system, Italy
  2. ^ Lavigne, Denis (2017). "OSeMOSYS energy modeling using an extended UTOPIA model" (PDF). Universal Journal of Educational Research. 5 (1): 162–169. doi:10.13189/ujer.2017.050120. Retrieved 2017-01-12.
  3. ^ Lavigne, Denis (2016). "Initiatives for teaching energy modelling to graduate students" (PDF). Universal Journal of Management. 4 (8): 451–458. doi:10.13189/ujm.2016.040805. Retrieved 2017-01-12.

2017 update

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2017 update 2

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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

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Jonas Hörsch wins OSeMOSYS challenge.[1]

GLPK wikibook: very large MathProg models

References

  1. ^ "Frankfurt Institute doctoral student wins 'Sustainable energy for all challenge' by improving modelling tool meant to facilitate universal electricity access — EN/318-ENV/DEV/1773" (Press release). New York, USA: United Nations. 31 January 2017. Retrieved 2017-02-16.

pandapower

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  • status: project added

Sources

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pandapower combines the data analysis library pandas and the power flow solver PYPOWER to create an easy to use network calculation program aimed at automation of power system analysis and optimization in distribution and sub-transmission networks.

pandapower is a joint development of the research group Energy Management and Power System Operation, University of Kassel and the Department for Distribution System Operation at the Fraunhofer Institute for Wind Energy and Energy System Technology (IWES), Kassel.

  • Scheidler et al (2016) on a pandapower application[1]
    • evince ~/synk/pdfs/2016-scheidler-etal-automated-grid-planning-distribution-grids-increasing-pv-penetration.pdf &
  • Thurner et al (2016) technical report[2]
    • evince ~/synk/pdfs/2016-thurner-pandapower-version-102.pdf &
  • PYPOWER manual (old)[3]

References

  1. ^ Scheidler, Alexander; Thurner, Leon; Kraiczy, Markus; Braun, Martin (14–15 November 2016). Automated grid planning for distribution grids with increasing PV penetration (PDF). 6th Solar Integration Workshop: International Workshop on Integration of Solar Power into Power Systems. Vienna, Austria. Retrieved 2016-12-02.
  2. ^ Thurner, Leon; Scheidler, Alexander; Dollichon, Julian; Meier, Friederike (30 November 2016). pandapower: convenient power system modelling and analysis based on PYPOWER and pandas — Version 1.0.2. Kassel, Germany: Fraunhofer IWES and Universität Kassel.
  3. ^ Lincoln, Richard (15 July 2011). PYPOWER documentation — Release 4.0.1 (PDF). Retrieved 2016-12-02.

Dump

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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:

PyPSA

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  • 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

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  • status: now live
Project Host License Access Data formats Scope/type
SMARD German BNetzA market data visuals CC BY 4.0 website CSV, XLS, XML, PDF DE, AT, and LU electricity systems

SWITCH

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  • status: 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[1]
    • evince ~/synk/pdfs/2012-fripp-switch-planning-tool-power-systems-large-shares-intermittent-renewable-energy.pdf &
  • Nelson et al (2012)[2]
    • evince ~/synk/pdfs/2012-nelson-etal-high-resolution-modeling-western-north-american-power-system.pdf &
  • Mileva et al (2016)[3]
    • evince ~/synk/pdfs/2016-mileva-etal-power-system-balancing-for-deep-decarbonization.pdf &

References

  1. ^ Fripp, Matthias (5 June 2012). "Switch: a planning tool for power systems with large shares of intermittent renewable energy" (PDF). Environmental Science and Technology. 46 (11): 6371–6378. doi:10.1021/es204645c. ISSN 0013-936X. Retrieved 2016-12-24.
  2. ^ Nelson, James; Johnston, Josiah; Mileva, Ana; Fripp, Matthias; Hoffman, Ian; Petros-Good, Autumn; Blanco, Christian; Kammen, Daniel M (April 2012). "High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures" (PDF). Energy Policy. 43: 436–447. doi:10.1016/j.enpol.2012.01.031. ISSN 0301-4215. Retrieved 2016-12-24.
  3. ^ Mileva, Ana; Johnston, Josiah; Nelson, James H; Kammen, Daniel M (15 January 2016). "Power system balancing for deep decarbonization of the electricity sector" (PDF). Applied Energy. 162: 1001–1009. doi:10.1016/j.apenergy.2015.10.180. ISSN 0306-2619. Retrieved 2016-12-24.

Awaiting projects

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

CREST ?

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  • status: work-in-progress
  • micro-energy system spreadsheet model


Project Host License Access Coding Documentation Scope/type
CREST TBA

Project

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Project CREST
Host
Status
Scope/type
Code license TBA
Data license TBA
Website
Wiki
Repository
Documentation

Text text.

  • Richardson et al (2010) on domestic electricity use[1]
    • add to PhD
  • Richardson and Thomson (2012) on one-minute model[2]
    • evince ~/synk/pdfs/2012-richardson-and-thomson-integrated-simulation-pv-micro-generation-domestic-electricity-demand.pdf &

References

  1. ^ Richardson, Ian; Thomson, Murray; Infield, David; Clifford, Conor (October 2010). "Domestic electricity use: a high-resolution energy demand model". Energy and Buildings. 42 (10): 1878–1887. doi:10.1016/j.enbuild.2010.05.023. ISSN 0378-7788.
  2. ^ Richardson, Ian; Thomson, Murray (6 August 2012). "Integrated simulation of photovoltaic micro-generation and domestic electricity demand: a one-minute resolution open-source model". Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. doi:10.1177/0957650912454989.

DDPP Decarbonization Calculator

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  • status: needs reliable secondary source / needs license details
Project Host License Access Coding Documentation Scope/type
Decarbonization Calculator Deep Decarbonization Pathways Project TBA download Excel/VBA manual spreadsheet

DDPP Decarbonization Calculator

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Project DDPP Decarbonization Calculator
Host Deep Decarbonization Pathways Project
Status active
Scope/type spreadsheet
Code license TBA
Website deepdecarbonization.org/research-methods/ddpp-collective-toolkit/
Documentation deepdecarbonization.org/wp-content/uploads/2015/09/DDPP-Decarbonization-Calculator-Users-Guide.pdf

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.[1]

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.

References

  1. ^ DDPP Decarbonization Calculator User's Guide (PDF). 2015. Retrieved 2016-07-21.

EINSTEIN

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  • status: work up material
    • EINSTEIN developers <info energyxperts.net>
Project Host License Access Coding Documentation Scope/type
EINSTEIN energyXperts.NET GPLv3 download Python website single-site analysis

EINSTEIN

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Project EINSTEIN
Host energyXperts.NET
Status
Scope/type single-site analysis
Code license GPLv3
Website www.einstein-energy.net
Repository
Documentation einstein.sourceforge.net
Notes open version is disabled

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

EINSTEIN supports English and 10 other European languages.

Sources

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  • documentation[2]
  • Schwiger et al (2011)[3]
    • evince ~/synk/pdfs/2011-schweiger-etal-assessment-potential-solar-thermal-energy-industrial-sector-einstein-spanish.pdf &
  • Brunner et al (2010a)[4]
    • evince ~/synk/pdfs/2010-brunner-etal-einstein-expert-system-audit-methodology-and-software-tool.pdf &
  • Brunner et al (2010b)[5]
    • evince ~/synk/pdfs/2010-brunner-etal-einstein-expertensystem.pdf &

References

  1. ^ "Einstein Energy — Home". Experts System for an Intelligent Supply of Thermal Energy in Industry and other Large-Scale Applications. Berlin, Germany. Retrieved 2016-12-22.
  2. ^ "Expert system for an INtelligent Supply of Thermal Energy in INdustry and other large scale applications — EINSTEIN 2.4.02 documentation". SourceForge. Retrieved 2016-12-22.
  3. ^ Schweiger, Hans; Vannoni, Claudia; Pinedo Pascua, Irene; Facci, Enrico; Baehrens, David; Koch, Marius; Pérez, David; Lucas Mozetic, Lucas (2011). Evaluación del potencial de la energía solar térmica en el sector industrial — Estudios Técnicos PER 2011-2020 [Assessment of the potential of solar thermal energy in the industrial sector — Technical studies PER 2011-2020] (PDF) (in Spanish). Madrid, Spain: IDAE. Retrieved 2016-12-22.
  4. ^ Brunner, Christoph; Muster, Bettina Muster; Heigl, Eva; Schweiger, Hans; Vannoni, Claudia (October 2010). "EINSTEIN — Expert System for an Intelligent Supply of Thermal Energy in Industry — Audit Methodology and Software Tool" (PDF). Proceedings Eurosun. Retrieved 2016-12-22.{{cite conference}}: CS1 maint: date and year (link)
  5. ^ Brunner, Christoph; Muster, Bettina Muster; Heigl, Eva; Schweiger, Hans; Vannoni, Claudia (2010). "EINSTEIN — Expertensystem spürt systematisch Energie-Einsparpotentiale auf" [Expert system systematically unlocks energy saving potentials] (PDF). Energy 2.0–Kompendium 2011 (in German). publish-industry Verlag: 270–272. Retrieved 2016-12-22.

EnergyNumbers–Balancing

edit
  • status: needs reliable secondary source
Project Host License Access Coding Documentation Scope/type
EnergyNumbers–Balancing University College London GPLv3 on application Fortran, PHP — web-based

EnergyNumbers–Balancing

edit
Project EnergyNumbers–Balancing
Host University College London
Status active
Scope/type web-based
Code license GPLv3
Website energynumbers.info/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. As of 2016, Britain, Germany, and Spain are supported.

energyRt

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Project Host License Access Coding Documentation Scope/type
energyRt — AGPLv3.0 GitHub R — linear optimization

energyRt

edit
Project energyRt
Host —
Status active
Scope/type linear optimization
Code license AGPLv3.0
Website energyrt.org
Repository github.com/olugovoy/energyRt
Documentation —

energyRt stands for energy systems modeling R-toolbox. As of 2016, 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.[1][2]

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.

References

  1. ^ Gandrud, Christopher (11 June 2015). Reproducible research with R and RStudio (2 ed.). Boca Raton, USA: Chapman and Hall/CRC. ISBN 978-1-4987-1537-9.
  2. ^ Xie, Yihui (22 June 2015). Dynamic documents with R and knitr (2 ed.). Boca Raton, USA: Chapman and Hall/CRC. ISBN 978-1-4987-1696-3.

German Green Growth Model

edit
  • status: watching brief / not yet released
Project Host License Access Coding Documentation Scope/type
German Green Growth Model Global Climate Forum TBA not yet public — — agent-based

German Green Growth Model

edit
Project German Green Growth Model
Host Global Climate Forum
Status active
Scope/type agent-based
Code license TBA
Data license TBA
Website
Wiki
Repository
Documentation

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.

Sources

edit
  • Gesine Steudle
  • model page
    • The German Green Growth Model project (Bewertungsmodul Klimapolitik, Förderkennzeichen 03KSE041, May 2012 – December 2014) develops a module for assessing costs and benefits of German energy and climate policy measures in a macro-economic context. Based on a dialogue with experts and potential users, the module is designed in such a way that it can complement existing detailed models of specific sectors. It will be available as open source software and via the representation of multiple equilibria it will allow to identify win-win strategies for climate policy.
  • blog by Sarah Wolf

References

GnuAE

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


MultiMod

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Project Host License Access Coding Documentation Scope/type
MultiMod DIW Berlin and NTNU Trondheim planned open GAMS game theory

MultiMod

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Project MultiMod
Host
Status active
Scope/type game theory
Code license TBA
Data license TBA
Website www.diw.de/multimod
Wiki
Repository
Documentation

MultiMod is the energy system and resource market model.

Sources

edit
  • Huppmann and Egging (2014a)[1]
    • evince ~/synk/pdfs/2014-huppmann-and-egging-market-power-fuel-substitution-infrastructure-diw-1370.pdf &
  • Huppmann and Egging (2014b)[2]
  • Yeh et al (2016)[3]
    • evince ~/synk/pdfs/2016-yeh-etal-north-american-natural-gas-and-energy-markets-transition.pdf &

References

  1. ^ Huppmann, Daniel; Egging, Ruud (21 July 2014). Market power, fuel substitution and infrastructure – A large-scale equilibrium model of global energy markets — Working paper 1370 (PDF). Berlin, Germany: DIW Berlin. ISSN 1619-4535. Retrieved 2016-12-02.
  2. ^ Huppmann, Daniel; Egging, Ruud (1 October 2014). "Market power, fuel substitution and infrastructure – A large-scale equilibrium model of global energy markets". Energy. 75: 483–500. doi:10.1016/j.energy.2014.08.004. ISSN 0360-5442.
  3. ^ Yeh, Sonia; Cai, Yiyong; Huppman, Daniel; Bernstein, Paul; Tuladhar, Sugandha; Huntington, Hillard G (November 2016). "North American natural gas and energy markets in transition: insights from global models" (PDF). Energy Economics. 60: 405–415. doi:10.1016/j.eneco.2016.08.021. ISSN 0140-9883. Retrieved 2016-12-02. Based on EMF 31 study.

PLEXOS Open EU

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  • status: watching brief / not yet open



Project Host License Access Coding Documentation Scope/type
PLEXOS Open EU TBA

PLEXOS Open EU

edit
Project PLEXOS Open EU
Host
Status active
Scope/type
Code license TBA
Data license TBA
Website
Wiki
Repository
Documentation

Text text.

Sources

edit

References

Rogeaulito

edit
  • status: to research and add / awaiting response to my email for license details
  • status: 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

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))

  • AR5 WG3 p238 for definitions <

Spreadsheet validity diversion

edit
  • Hermans and Murphy-Hill (2015) on Enron spreadsheets[1]
    • PDF currently under review, so it should have been published too
    • Enron's spreadsheets are more smelly than the usual corpus
    • evince ~/synk/pdfs/2015-hermans-and-murphy-hill-enrons-spreadsheets-and-related-emails.pdf &


  • Koc and Tansel (2011) survey of version control systems[2]
    • evince ~/synk/pdfs/2011-koc-and-tansel-survey-version-control-systems.pdf &
    • mentions some spreadsheets offer some version control features

References

  1. ^ Hermans, Felienne; Murphy-Hill, Emerson (2015). "Enron's spreadsheets and related emails: a dataset and analysis" (PDF). Proceedings of the 37th International Conference on Software Engineering. Vol. 2. Florence, Italy: IEEE Press. pp. 7–16. Retrieved 2016-12-14.
  2. ^ Koc, Ali; Tansel, Abdullah Uz (2011). A survey of version control systems (PDF). Retrieved 2016-12-15.

Open science diversion

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  • 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. doi:10.1126/science.1203354
  • Peng, R. D. (2011). "Reproducible research in computational science". Science 334, 1226–1227. doi:10.1126/science.1213847
  • Ince et al (2012) on open computer programs[1]
    • evince ~/synk/pdfs/2012-ince-etal-case-for-open-computer-programs.pdf &

References

  1. ^ Ince, Darrel C; Hatton, Leslie; Graham-Cumming, John (23 February 2012). "The case for open computer programs" (PDF). Nature. 482 (7386): 485–488. doi:10.1038/nature10836. ISSN 0028-0836. Retrieved 2016-12-14.

AIM diversion

edit

DICE diversion

edit
  • DICE model
  • Nordhaus and Boyer (2000)[1]
    • evince ~/synk/pdfs/2000-nordhaus-and-boyer-warning-the-world-economic-models-of-global-warming.pdf &
  • Newbold (2010) (EPA summary)[2]
    • evince ~/synk/pdfs/2010-newbold-summary-dice-model-epa.pdf &

References

  1. ^ Nordhaus, William D; Boyer, Joseph (2000). Warning the world: economic models of global warming (PDF). Cambridge, USA: The MIT Press. ISBN 9780262140713. Retrieved 2016-12-13.
  2. ^ Newbold, Stephen C (November 2010). "Summary of the DICE model" (PDF). United States: National Center for Environmental Economics, US EPA. Retrieved 2016-12-13.

StELMOD

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Project Host License Access Coding Documentation Scope/type
StELMOD DIW Berlin MIT GitHub GAMS website European electricity market

StELMOD

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Project StELMOD
Host DIW Berlin
Status active
Scope/type European electricity market
Code license MIT
Website www.diw.de/de/diw_01.c.528493.de/forschung_beratung/nachhaltigkeit/umwelt/verkehr/energie/modelle/elmod.html
Repository github.com/frkunz/stELMOD
Documentation see website

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

GAMS and CPLEX.

Sources

edit
  • Abrell and Kunz (2015)[1]
    • recommended citation
  • Kunz and Zerrahn (2016a)[2]
    • evince ~/synk/pdfs/2016-kunz-and-zerrahn-coordinating-cross-country-congestion-management-diw-1551.pdf &
  • Kunz and Zerrahn (2016b)[3]
    • evince ~/synk/pdfs/2016-kunz-and-zerrahn-coordinating-cross-country-congestion-management-presentation.pdf &

References

  1. ^ Abrell, Jan; Kunz, Friedrich (2015). "Integrating intermittent renewable wind generation — A stochastic multi-market electricity model for the European electricity market". Networks and Spatial Economics. 15 (1): 117–147. doi:10.1007/s11067-014-9272-4. ISSN 1572-9427.
  2. ^ Kunz, Friedrich; Zerrahn, Alexander (2016). Coordinating cross-country congestion management — DIW discussion paper 1551 (PDF). Berlin, Germany: DIW Berlin. ISSN 1619-4535. Retrieved 2016-12-17.
  3. ^ Kunz, Friedrich; Zerrahn, Alexander (8 April 2016). Coordinating cross-country congestion management — Presentation (PDF). Berlin, Germany: DIW Berlin. Retrieved 2016-12-17.

UKTM

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  • status: watching brief / not yet released
  • energy system model
Project Host License Membership Coding Documentation Scope/type
UKTM (UK TIMES model) wholeSEM TBA GAMS 1 United Kingdom
  • 1. The GAMS code is automatically generated by the VEDA-FE front-end application.

UKTM

edit
Project UKTM
Host
Status active
Scope/type
Code license
Data license
Website
Wiki
Repository
Documentation

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

References

Sources

edit


  • presentation 2014[2]
  • presentation 2015[3]
  • documentation[4]

References

  1. ^ "wholeSEM's UCL team to develop open source version of UK TIMES with the Department of Energy and Climate Change". wholeSEM Whole Systems Energy Modelling Consortium. London, United Kingdom. 16 December 2014. Retrieved 2016-11-08. This open-source model will be available in early 2015 and will be managed by an expert user group from leading UK institutions.
  2. ^ Strachan, Neil; Fais, Birgit; Daly, Hannah (18 November 2014). Redefining the energy modelling-policy interface: developing a fully open source UK TIMES model — Presentation (PDF). Energy Technology Systems Analysis Programme (ETSAP) Workshop, Technical University of Denmark (DTU). Copenhagen, Denmark. Retrieved 2016-11-08.
  3. ^ Fais, Birgit; Strachan, Neil; Daly, Hannah (13–14 April 2015). Redefining the energy modelling-policy interface: developing a fully open source UK TIMES model — Presentation (PDF). 2nd Open Energy Modelling Workshop. Berlin, Germany. Retrieved 2016-11-08.
  4. ^ Daly, Hannah E; Fais, Birgit (November 2014). UK TIMES model: overview. London, United Kingdom: UCL Energy Institute. Retrieved 2016-11-12.

Follow up

edit


  • Fais et al (2014) presentation on technology pathways[1]
    • not found


  • Strachan et al (2016) on reinventing the modelling-policy interface[2]
    • 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[3]
    • evince ~/synk/pdfs/2015-dodds-etal-characterising-evolution-energy-system-models-model-archaeology.pdf &
    • 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.



  • Pye et al (2016) on exploring national decarbonization pathways and global energy trade flows[5]
    • part of DDPP


  • Trutnevyte et al (2016)[6]
    • ResearchGate

References

  1. ^ Fais, Birgit; Daly, Hannah; Keppo, Ilkka (8–9 July 2014). Technology pathways for a low-carbon energy transition — critical insights from the energy system model UKTM — Presentation. 1st Annual Conference of the wholeSEM project. London, United Kingdom.
  2. ^ Strachan, Neil; Fais, Birgit; Daly, Hannah (29 February 2016). "Reinventing the energy modelling–policy interface". Nature Energy. 1: 16012. doi:10.1038/nenergy.2016.12. ISSN 2058-7546.
  3. ^ Dodds, Paul E; Keppo, Ilkka; Strachan, Neil (2015). "Characterising the evolution of energy system models using model archaeology" (PDF). Environmental Modeling and Assessment. 20 (2): 83–102. doi:10.1007/s10666-014-9417-3. ISSN 1573-2967. Retrieved 2016-11-13.  
  4. ^ Fais, Birgit; Sabio, Nagore; Strachan, Neil (15 January 2016). "The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets". Applied Energy. 162: 699–712. doi:10.1016/j.apenergy.2015.10.112. ISSN 0306-2619.
  5. ^ Pye, Steve; McGlade, Christophe; Bataille, Chris; Anandarajah, Gabrial; Denis-Ryan, Amandine; Potashnikov, Vladimir (20 June 2016). "Exploring national decarbonization pathways and global energy trade flows: a multi-scale analysis". Climate Policy. 16 (sup1): S92–S109. doi:10.1080/14693062.2016.1179619. ISSN 1469-3062.
  6. ^ Trutnevyte, Evelina; McDowall, Will; Tomei, Julia; Keppo, Ilkka (March 2016). "Energy scenario choices: insights from a retrospective review of UK energy futures". Renewable and Sustainable Energy Reviews. 55: 326–337. doi:10.1016/j.rser.2015.10.067. ISSN 1364-0321.

WWS project

edit
  • status: to complete and add
  • email addresses
    • Mark Jacobson <jacobson stanford.edu>
    • Mark Delucchi <madelucchi berkeley.edu>
  • 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.[1] Moreover, a similar paper, but for 139 countries of the world, is currently in review.
Project Host License Membership Coding Documentation Scope/type
WWS project Stanford University MIT download Excel/VBA — 139 countries survey

WWS project

edit
Project WWS project
Host Stanford University
Status active
Scope/type 139 countries survey
Code license MIT 1
Website
Repository web.stanford.edu/group/efmh/jacobson/Articles/I/AllCountries.xlsx
Documentation
1. As of December 2016, the spreadsheet does not contain a text of the license.

The WWS (wind, water, and sunlight) project produces roadmaps for 139 countries 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.[2][3][4]

edit

Sources

edit
  • 2009 presentation[5]
    • evince ~/synk/pdfs/2009-jacobson-plan-for-sustainable-future-presentation.pdf &
  • Jacobson and Delucchi (2009) Scientific American article[6]
  • Jacobson and Delucchi (2011a)[7]
    • evince ~/synk/pdfs/2011-jacobson-and-delucchi-wws-part-1-technologies-resources-quantities-infrastructure.pdf &
  • Jacobson and Delucchi (2011b)[8]
  • Trainer (2012) critique[2]
    • parts I and II
  • Trainer (2013) critique[3]
    • similar to [3] ?
    • parts I and II
    • evince ~/synk/pdfs/2012-trainer-critique-wws-concept.pdf &
  • Delucchi and Jacobson (2012) rebuttal[4]
    • evince ~/synk/pdfs/2012-delucchi-and-jacobson-rebuttal-to-ted-trainer.pdf &
  • Fischetti (2015) Scientific American article[9]
  • Jacobson et al (2015) 50 US states study[1]
  • Jacobson et al (2015)[1]
    • evince ~/synk/pdfs/2015-jacobson-etal-low-cost-solution-to-grid-reliability-problem-100pc-intermittent-wws.pdf &
  • Delucchi et al (2016) spreadsheets[10]
  • Jacobson et al (2016) report[11]
    • evince ~/synk/pdfs/2016-jacobson-etal-100-percent-wws-139-countries-final.pdf &

References

  1. ^ a b c Jacobson, Mark Z; Delucchi, Mark A; Bazouin, Guillaume; Bauer, Zack AF; Heavey, Christa C; Fisher, Emma; Morris, Sean B; Piekutowski, Diniana JY; Vencill, Taylor A; Yeskoo, Tim W (2015). "100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States". Energy and Environmental Science. 8 (7): 2093–2117. doi:10.1039/C5EE01283J. Cite error: The named reference "jacobson-etal-2015" was defined multiple times with different content (see the help page).
  2. ^ a b Trainer, Ted (May 2012). "A critique of Jacobson and Delucchi's proposals for a world renewable energy supply". Energy Policy. 44: 476–481. doi:10.1016/j.enpol.2011.09.037. ISSN 0301-4215.
  3. ^ a b c Trainer, Ted (Winter 2013). "A critique of Jacobson and Delucchi's proposals for a world renewable energy supply" (PDF). Synthesis/Regeneration 60: 23–28. Retrieved 2016-12-05.
  4. ^ a b Delucchi, Mark A; Jacobson, Mark Z (May 2012). "Response to "A critique of Jacobson and Delucchi's proposals for a world renewable energy supply" by Ted Trainer" (PDF). Energy Policy. 44: 482–484. doi:10.1016/j.enpol.2011.10.058. ISSN 0301-4215. Retrieved 2016-12-05.
  5. ^ Jacobson, Mark Z (30 October 2009). A plan for a sustainable future — Presentation (PDF). Retrieved 2016-12-05. Presented to Using Economics to Confront Climate Change, SIEPR Policy Forum, Stanford University, USA.
  6. ^ Jacobson, Mark Z; Delucchi, Mark A (2009). "A path to sustainable energy by 2030". Scientific American. 301: 58–65. doi:10.1038/scientificamerican1109-58.
  7. ^ Jacobson, Mark Z; Delucchi, Mark A (March 2011). "Providing all global energy with wind, water, and solar power, Part I: technologies, energy resources, quantities and areas of infrastructure, and materials". Energy Policy. 39 (3): 1154–1169. doi:10.1016/j.enpol.2010.11.040. ISSN 0301-4215.
  8. ^ Delucchi, Mark A; Jacobson, Mark Z (March 2011). "Providing all global energy with wind, water, and solar power, Part II: reliability, system and transmission costs, and policies". Energy Policy. 39 (3): 1170–1190. doi:10.1016/j.enpol.2010.11.045. ISSN 0301-4215.
  9. ^ Fischetti, Mark (19 November 2015). "139 Countries could get all of their power from renewable sources: energy from wind, water and sun would eliminate nuclear and fossil fuels". Scientific American. Retrieved 2016-12-05.
  10. ^ Delucchi, Mark A; Jacobson, Mark Z; Bauer, Zack AF; Goodman, Savannah C; Chapman, William E (2016). Spreadsheets for 139-country 100% wind, water, and solar roadmaps. Retrieved 2016-07-26. Direct URL: xlsx-spreadsheets.
  11. ^ Jacobson, Mark Z; Delucchi, Mark A; Bauer, Zack AF; Goodman, Savannah C; Chapman, William E; Cameron, Mary A; Bozonnat, Cedric; Chobadi, Liat; Clonts, Hailey A; Enevoldsen, P; Erwin, Jenny R; Fobi, Simone N; Goldstrom, Owen K; Hennessy, Eleanor M; Liu, Jingyi; Lo, Jonathan; Meyer, Clayton B; Morris, Sean B; Moy, Kevin R; O'Neill, Patrick L; Petkov, Ivalin; Redfern, Stephanie; Schucker, Robin; Sontag, Michael A; Wang, Jingfan; Weiner, Eric; Yachanin, Alexander S (24 October 2016). 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world (PDF). Retrieved 2016-11-23.

Text

edit

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.[1][2] 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.

References

  1. ^ Jacobson, Mark Z; Delucchi, Mark A; Bauer, Zack AF; Goodman, Savannah C; Chapman, William E; Cameron, Mary A; Bozonnat, Cedric; Chobadi, Liat; Clonts, Hailey A; Enevoldsen, P; Erwin, Jenny R; Fobi, Simone N; Goldstrom, Owen K; Hennessy, Eleanor M; Liu, Jingyi; Lo, Jonathan; Meyer, Clayton B; Morris, Sean B; Moy, Kevin R; O'Neill, Patrick L; Petkov, Ivalin; Redfern, Stephanie; Schucker, Robin; Sontag, Michael A; Wang, Jingfan; Weiner, Eric; Yachanin, Alexander S (24 October 2016). 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world (PDF). Retrieved 2016-11-23.
  2. ^ Delucchi, Mark A; Jacobson, Mark Z; Bauer, Zack AF; Goodman, Savannah C; Chapman, William E (2016). Spreadsheets for 139-country 100% wind, water, and solar roadmaps. Retrieved 2016-07-26. Direct URL: xlsx-spreadsheets.

Open energy system databases

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OpenGridMap

edit
  • status: transferred
Project Host License Access Data formats Scope/type
OpenGridMap Technical University of Munich CC BY 3.0 IGO website CSV, XML, CIM electricity grid data worldwide

OpenGridMap

edit

Not used.[1]

References

  1. ^ Zeiss, Geoff (30 December 2016). "OpenGridMap: open, crowdsourced project to map power grid infrastructure". Between the Poles. Retrieved 2017-04-11.

Power Match

edit
  • status: add when goes live later in 2017
Project Host License Access Data formats Scope/type
Power Match World Resources Institute CC BY 3.0 US website global electricity

Power Match

edit
Project Power Match
Host World Resources Institute
Status no yet live
Scope/type global electricity
Code license TBA
Data license CC BY 3.0 US
Website
Wiki
Repository
Documentation

Text text.

Sources

edit

References

Open software communities

edit
  • status: work up
  • this location is temporary until it grows too big


  • Katz et al (2016)[1]
    • 5.10 Building Sustainable User Communities for Scientific Software (see quote below)
    • evince ~/synk/pdfs/2016-katz-etal-report-third-workshop-sustainable-software-for-science.pdf &

User communities are the lifeblood of sustainable scientific software. The user community includes the developers, both internal and external, of the software; direct users of the software; other software projects that depend on the software; and any other groups that create or consume data that is specific to the software. Together these groups provide both the reason for sustaining the software and, collectively, the requirements that drive its continued evolution and improvement.[1]: 31 

References

  1. ^ a b Katz, Daniel; Choi, Sou-Cheng; Niemeyer, Kyle; Hetherington, James; Löffler, Frank; Gunter, Dan; Idaszak, Ray; Brandt, Steven; Miller, Mark; Gessing, Sandra; Jones, Nick; Weber, Nic; Marru, Suresh; Allen, Gabrielle; Penzenstadler, Birgit; Venters, Colin; Davis, Ethan; Hwang, Lorraine; Todorov, Ilian; Patra, Abani; Val-Borro, Miguel de (21 October 2016). "Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)". Journal of Open Research Software. 4 (1): e37. doi:10.5334/jors.118. ISSN 2049-9647. Retrieved 2016-12-13.{{cite journal}}: CS1 maint: unflagged free DOI (link)

Background

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UK model quality assurance report

edit
  • UK model quality assurance report (does not mention energy models though)[1]

References

  1. ^ Review of quality assurance of Government analytical models — Final report (PDF). London, United Kingdom: HM Treasury. March 2013. ISBN 978-1-909096-53-0. Retrieved 2016-11-11.

Open energy-economy models

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

Open energy-economy models
(combining a simplified energy system and a regional global economy)
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