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A Long-Term Electricity Dispatch Model with the TIMES Framework

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Abstract

A new Swiss TIMES (The Integrated MARKAL–EFOM System) electricity model with an hourly representation of inter-temporal detail and a century-long model horizon has been developed to explore the TIMES framework’s suitability as a long-term electricity dispatch model. To understand the incremental insights from this hourly model, it is compared to an aggregated model with only two diurnal timeslices like in most MARKAL/TIMES models. Two scenarios have been analysed with both models to answer the following questions: Are there differences in model solutions? What are the benefits of having a high number of timeslices? Are there any computational limitations? The primary objective of this paper is to understand the differences between the solutions of the two models, rather than Swiss policy implication or potential uncertainties in input parameters and assumptions. The analysis reveals that the hourly model offers powerful insights into the electricity generation schedule. Nevertheless, the TIMES framework cannot substitute for a dispatch model because some features cannot be represented; however, the long model time horizon and integrated system approaches of TIMES provide features not available in conventional dispatch models. The methodology of the model development and insights from the model comparison are described.

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Notes

  1. There is no heat demand in the model. In order to cope with operation of CHP, heat output from CHPs is currently modelled to be exported with small price incentive.

  2. Currency conversion : 1 euro = 1.4 CHF; 1 US$ = 0.93 CHF [55]

  3. See footnote 1.

  4. This assumption provides flexible exchange of electricity in the future years, but the future availability of interconnectors is quite uncertain and heavily dependent on electricity system development in the four markets.

  5. We analysed hourly electricity spot market prices for 2008 [19] and generated time-dependent electricity import prices for all the 288 timeslices. However, this approach did not imitate the historical trade pattern. Thus, the current approach is implemented to model the trading mechanism, but this represents an area of further model development.

  6. For instance, in May 2011, the Swiss Federal council decided to completely restrict investment in new nuclear power plants. The Base scenario does not include this restriction, which has been analysed in our other publications [33, 56]. Thus, we re-emphasise that the Base scenario is adopted as an illustrative case.

  7. From our extensive scenario analyses [33], we found that the highly fluctuating demands in Switzerland (requiring around 3.5 GW of flexible power plants) are easily managed by the availability of large dam and pumped hydro storage facilities. In addition, the interconnectors also serve as additional sources of supply (import) and most importantly load dumping (export).

  8. Years specified in the figures represent the mid-year of periods, i.e. 2020 represents 2018–22; 2048: 2041–55; 2080: 2071–2090. In the legends, Gas (Base) and Gas (Flex) refer to base load and dispatchable gas plants. Electricity consumed by pumped storage plant is shown as Pumps.

  9. Intel Core2 Quad (Q9400) @ 2.66 GHz and 3 GB RAM

References

  1. ADAM (2010). Adaptation and mitigation strategies: supporting European climate policy, Tyndall Centre. http://www.tyndall.ac.uk/adamproject/about. Accessed Sept 2012.

  2. Atukeren, E., Lassmann, A., Abrahamsen, Y. (2008). Switzerland’s electricity trade: a short-term forecasting model and an analysis of multivariate relationships, EEM (pp. 1–6). 5th International Conference on European. doi:10.1109/EEM.2008.4579018.

  3. Bahn, O., & Frei, C., (2000). GEM-E3 Switzerland: a computable general equilibrium model applied for Switzerland, PSI Bericht No. 00-01. Paul Scherrer Institute: Villigen. http://eem.web.psi.ch/Publications/Other_Reports/PSI_00-01.pdf.

  4. Bernard, A., Vielle, M., Viguier, L. (2005). Carbon tax and international emissions trading: a Swiss perspective. In A. Haurie, L. Viguier (Eds.), Coupling climate and economic dynamics (pp. 295–319). New York: Kluwer.

    Chapter  Google Scholar 

  5. BFE (2000–2010). Schweizerische Elektrizitätsstatistik, Bundesamt für Energie, Bern. http://www.bfe.admin.ch/themen/00526/00541/00542/00631/index.html?lang=de&dossier_id=00765.

  6. BFE (2000–2010). Schweizerische Gesamtenergiestatistik, Bundesamt für Energie, Bern; various publication 2000–2009. http://www.bfe.admin.ch/themen/00526/00541/00542/00631/index.html?lang=de&dossier_id=00763.

  7. BFE. (2007). Die Energieperspektiven 2035. Bern: Bundesamt für Energie.

    Google Scholar 

  8. BFE (2010). Stilllegungsfonds für Kernanlagen & Entsorgungsfonds für Kernkraftwerke - Faktenblatt Nr. 1: Rechtsgrundlagen, Organisation und allgemeine Informationen, Bundesamt für Energie, Bern. http://www.news.admin.ch/NSBSubscriber/message/attachments/20511.pdf.

  9. Blesl, M. (2008). TIMES Pan European Model (TIMES-PEM of NEEDS). http://www.etsap.org/Applications/NEEDS-TIMES-PEM-summary-MB.pdf.

  10. Boqiang, R., & Chuanwen, J. (2009). A review on the economic dispatch and risk management considering wind power in the power market. Renewable and Sustainable Energy Reviews, 13, 2169–2174.

    Article  Google Scholar 

  11. Bretschger, L., Ramer, R., Schwark, F. (2009). How rich is the 2000 Watt Society? Impact of energy conservation policy Measures on innovation, investment and long-term development of the Swiss Economy, Swiss Federal Institute of Technology. http://www.cer.ethz.ch/resec/people/rramer/Brochure_2kW.pdf.

  12. Chang, Y., Ho, H. K., Leong, K. C., Osman, R., Toh, K. C., Chen, Y., Kannan, R. (2006). Final report on the economics of the Kyoto Protocol: a cost-benefit analysis for Singapore. Singapore: Ministry of Trade and Industry.

    Google Scholar 

  13. Chaudry, M., Dougamas, A., Ekins, P., Kannan R, Shakoor A, Skea J, Strbac G, Wang X. (2009). A resilient UK Energy system: building in resilience, UKERC Research Report. http://www.ukerc.ac.uk/support/tiki-download_file.php?fileId=618.

  14. Connolly, D., Lund, H., Mathiesen, B. V., Leahy, M. (2010). A review of computer tools for analysing the integration of renewable energy into energy systems. Applied Energy, 87, 1059–1082.

    Article  Google Scholar 

  15. Econability (2010). GENESWIS computable general equilibrium model for Switzerland. http://www.econability.com/models.htm.

  16. Ecoplan (2006). Branchenszenarien Schweiz: Langfristszenarien zur Entwicklung der Wirtschaftsbranchen mit einem rekursiv-dynamischen Gleichgewichtsmodell (SWISSGEM). http://www.ecoplan.ch.

  17. Ecoplan (2006). Zukunfts- und wachstumsorientiertes Steuersystem (ZUWACHS). http://www.ecoplan.ch.

  18. Ecoplan (2008). Volkswirtschaftliche Auswirkungen von CO2-Abgaben und Emissionshandel für das Jahr 2020. http://www.ecoplan.ch/download/co2g_sb_de.pdf.

  19. EEX–European Electricity Exchange. Market Data (2008). http://www.eex.com/en/Market%20Data. Accessed 15 August 2010.

  20. ENTSO–European Network of Transmission System Operators for Electricity (2010). https://www.entsoe.eu/resources/data-portal/.

  21. ETEM (2011). Energy technology environment model. http://apps.ordecsys.com/etem.

  22. ETS–Der Energie Trialog Schweiz (2009). Energie-Strategie 2050—Impulse für die schweizerische Energiepolitik. Grundlagenbericht. http://www.energietrialog.ch/cm_data/Grundlagenbericht.pdf.

  23. ETSAP (2008). Categories of models and applications. http://www.etsap.org/Models&applicationsMainPage.asp.

  24. EWI (2010). Dispatch and investment model for electricity markets in Europe, The Institute of Energy Economics, University of Cologne. http://www.ewi.uni-koeln.de/fileadmin/user/PDFs/DIME_Model_description_.pdf.

  25. Foley, A. M., Gallachóir, B. P. Ó., Hur, J., Baldick, R., McKeogh, E. J. (2010). A strategic review of electricity systems models. Energy, 35(12), 4522–4530.

    Article  Google Scholar 

  26. Garcés, F. F. (2004) Electric power: transmission and generation reliability and adequacy. Encyclopaedia of Energy, 301–308

  27. Gül, T., Kypreos, S., Turton, H., Barreto, L. (2009). An energy-economics scenario analysis of alternative fuels for transport using the global multi-regional MARKAL Model GMM. Energy, 34(10), 1423–1437.

    Article  Google Scholar 

  28. Howells, M., et al. (2011). OSeMOSYS: the open source energy modeling system. Energy Policy. doi:10.1016/j.enpol.2011.06.033.

  29. Hirschberg, S., Bauer, C., Burgherr, P., Biollaz, S., Durisch, W., Foskolos, K., et al. (2005). Neue Erneuerbare Energien und neue Nuklearanlagen: Potenziale und Kosten. PSI-Report no.05-04. Paul Scherrer Institut, Villigen PSI, Switzerland. http://gabe.web.psi.ch/pdfs/PSI_Report/PSI-Bericht_05-04sc.pdf.

  30. Jakob, M. (2007). The drivers of and barriers to energy efficiency in renovation decisions of single-family home-owners, Center for Energy Policy and Economics CEPE, Department of Management, Technology and Economics, ETH Zurich. http://www.cepe.ethz.ch/publications/workingPapers/CEPE_WP56.pdf.

  31. Johnsson, F. (2011). Methods and models used in the project used in the project pathways to sustainable european energy systems, Alliance for Global Sustainability. http://www.energy-pathways.org/pdf/Metod_reportJan2011.pdf.

  32. JRC–Joint Research Centre (2009). Photovoltaic geographical information system (PVGIS): geographical assessment of solar resource and performance of photovoltaic technology. http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php. September 2009.

  33. Kannan, R., & Turton, H. (2012). Cost of ad-hoc nuclear policy uncertainties in the evolution of the Swiss electricity system, Energy Policy. Doi: 10.1016/j.enpol.2012.07.035.

  34. Kannan, R., & Turton, H. (2011). Documentation on the development of Swiss TIMES electricity model (STEM-E). Switzerland: Paul Scherrer Institut.

    Google Scholar 

  35. Kannan, R. (2009). Uncertainties in key low carbon power generation technologies—Implication for UK decarbonisation targets. Applied Energy, 86(10), 1873–1886.

    Article  CAS  Google Scholar 

  36. Kannan, R. (2010). Can a TIMES model be substituted for an economic dispatch model?—insights from a Swiss TIMES electricity model. Stockholm: ETSAP Workshop.

    Google Scholar 

  37. Kannan, R. (2010). Experience from the development of a new Swiss TIMES electricity model. New Delhi: Joint TERI-ETSAP Workshop.

    Google Scholar 

  38. Kannan, R. (2011). The development and application of a temporal MARKAL energy system model using flexible time slicing. Applied Energy, 88(6), 2261–2272.

    Article  Google Scholar 

  39. Kypreos, S. (1992). CO2 emission control in Switzerland using mathematical programming. Infor, 30, 194–206.

    Google Scholar 

  40. Laurent, D., Alain, H., Maryse, L., Philippe, T., Marc, V., Laurent, V. (2005). A coupled bottom-up / top-down model for GHG abatement scenarios in the housing sector of Switzerland. In, Loulou, R., Waaub, J.-P., Zaccour, G (Ed.) Energy and Environment, Springer, New York, pp. 27–61

  41. Loulou, R., Goldstein, G., Noble, K. (2004). MARKAL reference manual. Energy technology systems analysis programme. www.etsap.og.

  42. Loulou, R., Remne, U., Kanudia, A., Lehtila, A., Goldstein, G. (2005). Documentation for the TIMES Model, Energy Technology Systems Analysis Programme. http://www.etsap.org/Docs/TIMESDoc-Intro.pdf

  43. Marcucci, A., & Turton, H. (2012). Swiss energy strategies under global climate change and nuclear policy uncertainty. The Swiss Journal of Economics and Statistics, 148(2), 317–345.

    Google Scholar 

  44. Federal Office of Meteorology and Climatology (2012). MeteoSwiss, Hourly wind speed—Chasseral, Federal Office of Meteorology and Climatology, Zürich. www.meteoswiss.admin.ch.

  45. Noble, K. (2006). Enhanced timeslices, technology filters and ADRATIO Rules, MARKAL/ANSWER updates (15 August 2006). Australia: Noble-Soft Systems Pty Ltd.

    Google Scholar 

  46. Ochoa, P., & Ackere, A. (2009). Policy changes and the dynamics of capacity expansion in the Swiss electricity market. Energy Policy, 37(5), 1983–1998.

    Article  Google Scholar 

  47. Paul Scherrer Institut (2010). A proposal for developing a Swiss TIMES Energy system Model (STEM) for transition scenario analyses. http://energyeconomics.web.psi.ch/Projects/BFE_times.html.

  48. Paul Scherrer Institut (2010). Ernergie-Spiegel, No.20. http://www.psi.ch/media/aktuelles-energiespiegel-nr_20.

  49. Prognos (2010). Appraisal of Power Plant Projects. http://www.prognos.com/fileadmin/pdf/English/PDF/Power_plant_fleet.pdf.

  50. Rahimi, A., Deroover, M., Jiminez, I. (1989). Electric power system planning in the framework of the overall energy system, Energy Conversion Engineering Conference, Proceedings of the 24th Intersociety, 6, 2943–2948. doi:10.1109/IECEC.1989.74412.

  51. Reiter, U. (2010). Assessment of the European Energy Conversion Sector under Climate Change Scenarios, Ph.D. Thesis. Nr. 18840, ETH Zürich.

  52. Remme, U., & Blesl, M. (2008). Timeslices and storages in TIMES, TIMES Training Course (13-16 October). London: Policy Studies Institute.

  53. SATW–Swiss Academy of Engineering Sciences (2007). Road map-renewable energies Switzerland: an analysis with a view to harnessing existing potentials by 2050. http://www.satw.ch/publikationen/schriften/39_roadmap_e.pdf.

  54. SNB–Swiss National Bank (2010). Historical time series 4: interest rates and yields. http://www.snb.ch/en/iabout/stat/statpub/histz/id/statpub_histz_actual. Accessed 10 August 2010

  55. SNB–Swiss National Bank (2012). Quarterly Bulletin (3), March. http://www.snb.ch/en/mmr/reference/quartbul_2012_1_komplett/source/quartbul_2012_1_komplett.en.pdf.

  56. Weidmann, N., Kannan, R., Turton, H. (2012). Swiss climate change and nuclear policy: a comparative analysis using an energy system approach and a sectoral electricity model. The Swiss Journal of Economics and Statistics, 148(2), 275–316.

    Google Scholar 

  57. Wind-data.ch (2010). Die Website für Windenergie-Daten der Schweiz: SwissMetNet Stationen. http://www.wind-data.ch/messdaten/list.php?field=ff50&dir=ASC&wmo=67350&typ=perm. Accessed 28 June 2010.

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Acknowledgments

Earlier versions of this paper were presented at the ETSAP Workshop held in New Delhi and Stockholm [36, 37]. The author thank many people, who offered their support during the development of this model, particularly addressing the computational and solver issues, MIP formulation and fixing the storage algorithms. This paper is partly conceptualised based on the review comments from an earlier publication [38] and the contribution from the reviewers is highly appreciated.

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Kannan, R., Turton, H. A Long-Term Electricity Dispatch Model with the TIMES Framework. Environ Model Assess 18, 325–343 (2013). https://doi.org/10.1007/s10666-012-9346-y

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