How to cite this WIREs title:
WIREs Energy Environ.

Impact Factor: 2.922

Energy systems modeling: Trends in research publication

Overview

Pedro Gerber Machado, Dominique Mouette, Luz D. Villanueva, A. Ricardo Esparta, Bruno Mendes Leite, Edmilson Moutinho dos Santos

Published Online: Dec 12 2018

DOI: 10.1002/wene.333

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

This study analyzes 34 tools through 442 publications on energy systems modeling, aiming to identify the most relevant trends in research and the research gaps in energy planning. For detecting these gaps, the publications analyzed were categorized based on pre‐defined criteria regarding geographical coverage, sectoral coverage, time horizon, analytical approach and energy source. The categories were designed to be a broad classification with the appropriate level of information required to differentiate between models. The results show that there is a lack of studies concerned with better understanding the energy systems in Africa and South America. Environmentally‐oriented publications account for 50.4% of all the publications studied. Modelers have thus shown a greater interest in studying issues related to climate change mitigation than in other areas of the energy chain. This article is categorized under: Energy Policy and Planning > Economics and Policy Energy Policy and Planning > Systems and Infrastructure Concentrating Solar Power > Economics and Policy

First screening of tools and models

[ Normal View | Magnified View ]

Structure of the model by country

[ Normal View | Magnified View ]

Purpose of the model by country/region

[ Normal View | Magnified View ]

Publications keywords distribution

[ Normal View | Magnified View ]

Energy sources analyzed in the publications

[ Normal View | Magnified View ]

Sectoral coverage of publications

[ Normal View | Magnified View ]

Geographical level of publications

[ Normal View | Magnified View ]

Geographical coverage of publications

[ Normal View | Magnified View ]

Geographical distribution of publications, including regions and global level

[ Normal View | Magnified View ]

Structure of the models

[ Normal View | Magnified View ]

Purpose of the model

[ Normal View | Magnified View ]

Number of publications per year

[ Normal View | Magnified View ]

References

Agrawal,, N., Ahiduzzaman,, M., & Kumar,, A. (2018). The development of an integrated model for the assessment of water and GHG footprints for the power generation sector. Applied Energy, 216, 558–575. https://doi.org/10.1016/J.APENERGY.2018.02.116
Allegrini,, J., Orehounig, K., Mavromatidis, G., Ruesch, F., Dorer, V., & Evins, R. (2015). A review of modelling approaches and tools for the simulation of district‐scale energy systems. Renewable and Sustainable Energy Reviews, 52, 1391–1404. https://doi.org/10.1016/j.rser.2015.07.123
Bačeković,, I., & Østergaard,, P. A. (2018). Local smart energy systems and cross‐system integration. Energy, 151, 812–825. https://doi.org/10.1016/J.ENERGY.2018.03.098
Bazmi,, A. A., & Zahedi,, G. (2011). Sustainable energy systems: Role of optimization modeling techniques in power generation and supply ‐ A review. Renewable and Sustainable Energy Reviews, 15(8), 3480–3500. https://doi.org/10.1016/j.rser.2011.05.003
Bergendahl,, P.‐A., & Bergström,, C. (1982). Long‐term oil substitution—The IEA‐Markal model and some simulation results for Sweden. In The impact of rising oil prices on the world economy (pp. 97–112). London, England: Palgrave Macmillan UK. https://doi.org/10.1007/978-1-349-06361-1_7
Bhattacharyya,, S. C., & Timilsina,, G. R. (2010). A review of energy system models. International Journal of Energy Sector Management, 4(4), 494–518. https://doi.org/10.1108/17506221011092742
Bouckaert,, S., Selosse, S., Dubreuil, A., Assoumou, E., & Maïzi, N. (2012). Analyzing water supply in future energy systems using the TIMES Integrated Assessment Model (TIAM‐FR). Journal of Systemics, Cybernetics and Informatics, 10(1), 89–94.
Capros,, P., Mantzos,, L., Papandreou,, V., Tasios,, N., & Klaassen,, G. (2008). Energy systems analysis of CCS development in Europe. 2008 5th International Conference on the European Electricity Market (pp. 1–6). IEEE. https://doi.org/10.1109/EEM.2008.4579071
Capros,, P., Tasios,, N., & Marinakis,, A. (2012). Very high penetration of renewable energy sources to the European electricity system in the context of model‐based analysis of an energy roadmap towards a low carbon EU economy by 2050. 2012 9th International Conference on the European Energy Market (pp. 1–8). IEEE. https://doi.org/10.1109/EEM.2012.6254669
Capros,, P., Paroussos, L., Fragkos, P., Tsani, S., Boitier, B., Wagner, F., … Bollen, J. (2014). Description of models and scenarios used to assess European decarbonisation pathways. Energy Strategy Reviews, 2(3–4), 220–230. https://doi.org/10.1016/j.esr.2013.12.008
Chiodi,, A., Gargiulo, M., Deane, J. P., Lavigne, D., Rout, U. K., & Ó Gallachóir, B. P. (2013). Modelling the impacts of challenging 2020 non‐ETS GHG emissions reduction targets on Ireland`s energy system. Energy Policy, 62, 1438–1452.
Collins,, S., Deane,, J. P., Gallachóir,, Ó., & B. (2017). Adding value to EU energy policy analysis using a multi‐model approach with an EU‐28 electricity dispatch model. Energy, 130, 433–447.
Connolly,, D., Lund, H., Mathiesen, B. V., & Leahy, M. (2010). A review of computer tools for analysing the integration of renewable energy into various energy systems. Applied Energy, 87(4), 1059–1082. https://doi.org/10.1016/j.apenergy.2009.09.026
Criqui,, P., Mima, S., Menanteau, P., & Kitous, A. (2015). Mitigation strategies and energy technology learning: An assessment with the POLES model. In Technological forecasting and social change (pp. 119–136). London: Elsevier Inc. https://doi.org/10.1016/j.techfore.2014.05.005
Czyrnek‐Delêtre,, M. M., Chiodi, A., Murphy, J. D., & Ó Gallachóir, B. P. (2016). Impact of including land‐use change emissions from biofuels on meeting GHG emissions reduction targets: The example of Ireland. Clean Technologies and Environmental Policy, 18(6), 1745–1758. https://doi.org/10.1007/s10098-016-1145-8
Daly,, H. E., Ramea, K., Chiodi, A., Yeh, S., Gargiulo, M., & Ó Gallachóir, B. (2014). Incorporating travel behaviour and travel time into TIMES energy system models. Applied Energy, 135, 429–439. https://doi.org/10.1016/j.apenergy.2014.08.051
de L. Musgrove,, A. R. (1984). A linear programming analysis of liquid‐fuel production and use options for Australia. Energy, 9(4), 281–302. https://doi.org/10.1016/0360-5442(84)90100-2
de Moura,, G. N. P., Legey,, L. F. L., & Howells,, M. (2018). A Brazilian perspective of power systems integration using OSeMOSYS SAMBA – South America Model Base – And the bargaining power of neighbouring countries: A cooperative games approach. Energy Policy, 115, 470–485. https://doi.org/10.1016/J.ENPOL.2018.01.045
Després,, J., Mima, S., Kitous, A., Criqui, P., Hadjsaid, N., & Noirot, I. (2017). Storage as a flexibility option in power systems with high shares of variable renewable energy sources: A POLES‐based analysis. Energy Economics, 64, 638–650. https://doi.org/10.1016/J.ENECO.2016.03.006
Egberts,, G. (1981). MARKAL — Ein LP‐Modell für die Internationale Energieagentur. Operations Research Spektrum, 3(2), 95–100. https://doi.org/10.1007/BF01720101
Gargiulo,, M., & Gallachóir,, B. Ó. (2013). Long‐term energy models: Principles, characteristics, focus, and limitations. WIREs Energy and Environment, 2(2), 158–177. https://doi.org/10.1002/wene.62
Gielen,, D., & Changhong,, C. (2001). The CO₂ emission reduction benefits of Chinese energy policies and environmental policies: A case study for Shanghai, period 1995–2020. Ecological Economics, 39(2), 257–270. https://doi.org/10.1016/S0921-8009(01)00206-3
Haiges,, R., Wang, Y.D., Ghoshray, A., & Roskilly, A. P. (2017). Optimization of Malaysia`s power generation mix to meet the electricity demand by 2050. Energy Procedia, 142, 2844–2851. https://doi.org/10.1016/j.egypro.2017.12.431
Hall,, L. M. H., & Buckley,, A. R. (2016). A review of energy systems models in the UK: Prevalent usage and categorisation. Applied Energy, 169, 607–628. https://doi.org/10.1016/j.apenergy.2016.02.044
Handayani,, K., Krozer,, Y., & Filatova,, T. (2017). Trade‐offs between electrification and climate change mitigation: An analysis of the Java‐Bali power system in Indonesia. Applied Energy, 208, 1020–1037. https://doi.org/10.1016/J.APENERGY.2017.09.048
Holmgren,, K., & Amiri,, S. (2007). Internalising external costs of electricity and heat production in a municipal energy system. Energy Policy, 35(10), 5242–5253. https://doi.org/10.1016/j.enpol.2007.04.026
Huang,, W., Chen,, W., & Anandarajah,, G. (2017). The role of technology diffusion in a decarbonizing world to limit global warming to well below 2°C: An assessment with application of Global TIMES model. Applied Energy, 208(March), 291–301. https://doi.org/10.1016/j.apenergy.2017.10.040
Huang,, W., Ma,, D., & Chen,, W. (2017). Connecting water and energy: Assessing the impacts of carbon and water constraints on China`s power sector. Applied Energy, 185, 1497–1505.
IEA. (2015). Energy and climate change. World Energy Outlook Special Report, 1, 1–200. https://doi.org/10.1038/479267b
IEA. (2018). IEA statistics. Retrieved from https://www.iea.org/statistics/
Ioakimidis,, C., Koukouzas, N., Chatzimichali, A., Casimiro, S., & Itskos, G. (2011). Assessment for carbon capture and storage opportunities. In Computer aided chemical engineering (pp. 1939–1943). London: Elsevier. https://doi.org/10.1016/B978-0-444-54298-4.50166-5
Jebaraj,, S., & Iniyan,, S. (2006). A review of energy models. Renewable and Sustainable Energy Reviews, 10(4), 281–311. https://doi.org/10.1016/j.rser.2004.09.004
Jia,, L., Wenying, C., Deshun, L., Liu, J., Chen, W., & Liu, D. (2011). Scenario analysis of China`s future energy demand based on TIMES model system. Energy Procedia, 5, 1803–1808. https://doi.org/10.1016/j.egypro.2011.03.307
Keirstead,, J., Jennings,, M., & Sivakumar,, A. (2012). A review of urban energy system models: Approaches, challenges and opportunities. Renewable and Sustainable Energy Reviews, 16(6), 3847–3866. https://doi.org/10.1016/J.RSER.2012.02.047
Krajačić,, G., Duić,, N., & da Carvalho,, M. G. (2009). H2RES, energy planning tool for Island energy systems – The case of the Island of Mljet. International Journal of Hydrogen Energy, 34(16), 7015–7026. https://doi.org/10.1016/J.IJHYDENE.2008.12.054
Krey,, V. (2014). Global energy‐climate scenarios and models: A review. WIREs Energy and Environment, 3, 363–383. https://doi.org/10.1002/wene.98
Laha,, P., & Chakraborty,, B. (2017). Energy model – A tool for preventing energy dysfunction. Renewable and Sustainable Energy Reviews, 73, 95–114. https://doi.org/10.1016/j.rser.2017.01.106
Luukkanen,, J., Akgün,, O., Kaivo‐oja,, J., Korkeakoski,, M., Pasanen,, T., Panula‐Ontto,, J., & Vehmas,, J. (2015). Long‐run energy scenarios for Cambodia and Laos: Building an integrated techno‐economic and environmental modelling framework for scenario analyses. Energy, 91, 866–881. https://doi.org/10.1016/J.ENERGY.2015.08.091
Martinez Soto,, A., & Jentsch,, M. F. (2016). Comparison of prediction models for determining energy demand in the residential sector of a country. Energy and Buildings, 128, 38–55. https://doi.org/10.1016/j.enbuild.2016.06.063
Mati,, A. A., Ajuji,, A. S., & Bajoga,, B. G. (2012). End‐use electricity demand profile analysis using MESSAGE modeling approach. Asia‐Pacific Power and Energy Engineering Conference (APPEEC), IEEE (pp. 1–4). https://doi.org/10.1109/APPEEC.2012.6307570
McCollum,, D., Yang, C., Yeh, S., & Ogden, J. (2012). Deep greenhouse gas reduction scenarios for California ‐ Strategic implications from the CA‐TIMES energy‐economic systems model. Energy Strategy Reviews, 1(1), 19–32. https://doi.org/10.1016/j.esr.2011.12.003
Meschede,, H., Child,, M., & Breyer,, C. (2018). Assessment of sustainable energy system configuration for a small canary Island in 2030. Energy Conversion and Management, 165, 363–372. https://doi.org/10.1016/J.ENCONMAN.2018.03.061
Nogueira de Oliveira,, L. P., Rodriguez Rochedo, P. R., Portugal‐Pereira, J., Hoffmann, B. S., Aragão, R., Milani, R., … Schaeffer, R. (2016). Critical technologies for sustainable energy development in Brazil: Technological foresight based on scenario modelling. Journal of Cleaner Production, 130, 12–24. https://doi.org/10.1016/J.JCLEPRO.2016.03.010
Paltsev,, S. (2017). Energy scenarios: The value and limits of scenario analysis. WIREs Energy and Environment, 6, e242. https://doi.org/10.1002/wene.242
Pan,, L. J., Xie,, Y. B., & Li,, W. (2013). An analysis of emission reduction of chief air pollutants and greenhouse gases in Beijing based on the LEAP model. Procedia Environmental Sciences, 18(X), 347–352. https://doi.org/10.1016/j.proenv.2013.04.045
Pfenninger,, S., Hawkes,, A., & Keirstead,, J. (2014). Energy systems modeling for twenty‐first century energy challenges. Renewable and Sustainable Energy Reviews, 33, 74–86. https://doi.org/10.1016/j.rser.2014.02.003
Proost,, S., Van Regemorter, D., Lantz, F., & Saint‐Antonin, V. (2000). Limiting air pollution from transport: Economic evaluation of policy options for the European Union. scopus, 14(1–4), 320–330.
Rafaj,, P., & Kypreos,, S. (2007). Internalisation of external cost in the power generation sector: Analysis with global multi‐regional MARKAL model. Energy Policy, 35(2), 828–843.
Sailor,, V. L., & Rath‐Nagel,, S. (1982). Markal, a computer model designed for multi‐national energy system analyses. Energy Modelling Studies and Conservation, 1, 635. https://doi.org/10.1016/B978-0-08-027416-4.50057-0
Sgobbi,, A., Nijs, W., De Miglio, R., Chiodi, A., Gargiulo, M., & Thiel, C. (2016). How far away is hydrogen? Its role in the medium and long‐term decarbonisation of the European energy system. International Journal of Hydrogen Energy, 41(1), 19–35.
Suganthi,, L., & Samuel,, A. A. (2012). Energy models for demand forecasting ‐ A review. Renewable and Sustainable Energy Reviews, 16(2), 1223–1240. https://doi.org/10.1016/j.rser.2011.08.014
Taliotis,, C., Shivakumar,, A., Ramos,, E., Howells,, M., Mentis,, D., Sridharan,, V., … Mofor,, L. (2016). An indicative analysis of investment opportunities in the African electricity supply sector — Using TEMBA (The Electricity Model Base for Africa). Energy for Sustainable Development, 31, 50–66. https://doi.org/10.1016/J.ESD.2015.12.001
Tsai,, M.‐S., & Chang,, S.‐L. (2013). Taiwan`s GHG mitigation potentials and costs: An evaluation with the MARKAL model’. Renewable and Sustainable Energy Reviews, 20, 294–305. https://doi.org/10.1016/J.RSER.2012.12.007
van Vliet,, O., McCollum, D., Pachauri, S., Nagai, Y., Rao, S., & Riahi, K. (2012). Synergies in the Asian energy system: Climate change, energy security, energy access and air pollution. Energy Economics, 34, S470–S480. https://doi.org/10.1016/J.ENECO.2012.02.001
Wagh,, M. M., & Kulkarni,, V. V. (2018). Modeling and optimization of integration of Renewable Energy Resources (RER) for minimum energy cost, minimum CO₂ Emissions and sustainable development, in recent years: A review. Materials Today: Proceedings, 5(1), 11–21. https://doi.org/10.1016/j.matpr.2017.11.047
Yin,, X., & Chen,, W. (2013). Comparison of carbon emission scenarios predicted by the China TIMES model. scopus, 53(9), 1315–1321.
Zhang,, H., Chen,, W., & Huang,, W. (2016). TIMES modelling of transport sector in China and USA: Comparisons from a decarbonization perspective. Applied Energy, 162, 1505–1514. https://doi.org/10.1016/j.apenergy.2015.08.124

Further Reading

E3MLab and ICCS. (2013) PRIMES model. Athens. Retrieved from http://www.e3mlab.eu
Heaps,, C. G. (2016). Long‐range energy alternatives planning (LEAP) system. Somerville, MA: Stockholm Environment Institute.
Keramidas,, K., Kitous, A. G., Després, J., Schmitz, A., Vazquez, A. D., Mima, S., … Wiesenthal, T. (2017). POLES‐JRC model documentation. Luxemburg. https://doi.org/10.2760/225347
Morris,, S. C., Goldstein,, G. A., & Fthenakis,, V. M. (2002). NEMS and MARKAL‐MACRO models for energy‐environmental‐economic analysis: A comparison of the electricity and carbon reduction projections. Environmental Modeling and Assessment, 7(3), 207–216. https://doi.org/10.1023/A:1016332907313
Schrattenholzer,, L. (1981). The energy supply model MESSAGE (pp. 81–31). Retrieved from https://www.scopus.com/record/display.uri?eid=2‐s2.0‐0019700448%26origin=resultslist%26sort=r‐f%26src=s%26st1=MESSAGE+energy+model%26nlo=%26nlr=%26nls=%26sid=d485a8d2782f8a0b03063ee94085b573%26sot=b%26sdt=b%26sl=35%26s=TITLE‐ABS‐KEY%28MESSAGE+energy+model%29%26relpos=6%26citeCnt=2%26searchTerm=

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts