Skip to main content

Reducing computing time of energy system models by a myopic approach

A case study based on the PERSEUS-NET model

Abstract

In this paper, the performance of the existing energy system model PERSEUS-NET is improved in terms of computing time. Therefore, the possibility of switching from a perfect foresight to a myopic approach has been implemented. PERSEUS-NET is a linear optimization model generating scenarios of the future German electricity generation system until 2030, whilst considering exogenous regional characteristics such as electricity demand and existing power plants as well as electricity transmission network restrictions. Up to now, the model has been based on a perfect foresight approach, optimizing all variables over the whole time frame in a single run, thus determining the global optimum. However, this approach results in long computing times due to the high complexity of the problem. The new myopic approach splits the optimization into multiple, individually smaller, optimization problems each representing a 5 year period. The change within the generation system in each period is determined by optimizing the subproblem, whilst taking into account only the restrictions of that particular period. It was found that the optimization over the whole time frame with the myopic approach takes less than one tenth of the computing time of the perfect foresight approach. Therefore, we analyse in this paper the advantages and draw-backs of a change in the foresight as a way of reducing the complexity of energy system models. For PERSEUS-NET it is found that the myopic approach with stable input parameters is as suitable as the perfect foresight approach to generate consistent scenarios, with the advantage of significantly less computing time.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Abbreviations

DEMPROC:

Demand processes

ec:

Energy carriers and materials \(( {{ ec}\in { EC}})\)

EC\(_\mathrm{seas}\), EC\(_\mathrm{non-seas}\) :

Seasonal and non-seasonal energy carriers

elec:

Electricity as energy carrier

exp:

Sinks of the graph structure \(( {{ exp} \in { EXP}})\)

GENPROC:

Generation processes

Imp:

Sources of the graph structure \(( {{ imp}\in { IMP}})\)

kyo:

CO\(_{2}\) emission allowances \(( {{ kyp}\in { KYO}})\)

proc:

Processes \(( {{ proc}\in { PROC}})\)

prod:

Producers \(( {{ prod}\in { PROD}})\)

seas:

Time slots \(( {{ seas}\in { SEAS}})\)

t:

Year, period \(( {t\in T})\)

unit:

Units \(( {{ unit}\in { UNIT}})\)

Avai\(_\mathrm{unit,t}\) :

Availability factor for the generation unit unit in period \(t\)

\(\alpha _\mathrm{t }\) :

Discount factor

\(\lambda _\mathrm{proc,ec }\) :

Share of energy carrier ec related to total input/output of the process proc

\(\eta _\mathrm{prod,prod^{^{\prime }},ec,t }\) :

Flow efficiency of energy carrier ec between producers prod and prod’

\(\eta _\mathrm{proc,t }\) :

Efficiency of process proc in period \(t\)

CapRes\(_\mathrm{unit,t}\) :

Installed capacity of unit unit at the beginning of period \(t\)

Cfix\(_\mathrm{unit,t }\) :

Fixed annual operation costs of the generation unit unit in period \(t\)

Cfuel\(_\mathrm{imp,prod^{\prime },ec }\) :

Fuel costs for the delivery of the energy carrier ec to producer prod’ in period \(t\)

Cinv\(_\mathrm{unit,t}\) :

Specific investment for commissioning the generation unit unit in period \(t\)

Ckyo\(_\mathrm{kyo,t}\) :

Costs for the acquisition of CO\(_{2}\) allowances from the contingent kyo in period \(t\)

Cload\(_\mathrm{unit,t}\) :

Load change costs for the generation unit unit in period \(t\)

Cvar\(_\mathrm{proc,t}\) :

Variable operating costs of the process proc in period \(t\)

D\(_\mathrm{t,seas}\) :

Demand for electricity in time slice seas in period \(t\)

h\(_\mathrm{seas}\) :

Number of hours in season seas

Cap\(_\mathrm{unit,t}\) :

Installed capacity of the generation unit unit in period \(t\)

Fl\(_\mathrm{imp,prod^{\prime },ec,t}\) :

Level of ec-flow from the source of the graph structure imp to producer prod’ per year

Fl\(_\mathrm{prod,prod^{\prime },ec,t}\) :

Level of ec-flow from producer prod’ to producer prod per year

Fl\(_\mathrm{prod,exp,ec,t}\) :

Level of ec-flow from producer prod to the sink of the graph structure exp per year

FS\(_\mathrm{prod,prod^{\prime },ec,t,seas}\) :

Level of ec-flow from producer prod’ to producer prod per time slot

FS\(_\mathrm{prod,exp,ec,t,seas}\) :

Level of ec-flow from producer prod to the sink of the graph structure exp per year

KyoCert\(_\mathrm{kyo,t}\) :

Procurement of CO\(_{2}\) allowances kyo in period \(t\)

LVchange\(_\mathrm{unit,seas-1,seas,t }\) :

Load change of generation unit unit between time slices seas-1 and seas in \(t\)

NewCap\(_\mathrm{unit,t}\) :

Newly installed capacity of generation unit unit in a period \(t\)

PL\(_\mathrm{proc,t}\) :

Activity level of process proc per year in period \(t\)

PS\(_\mathrm{proc,t,seas}\) :

Activity level of process proc in time slot seas in period \(t\)

References

  1. 1.

    Möst, D., Genoese, M., Eßer, A., Renz, O.: European electricity and emission market modeling—the design of emission allocation plants and its effects on investment planning. Paper presented at the 5th Conference on European electricity market (EEM), Lisbon (2008)

  2. 2.

    Loulou, R., Remme, U., Kanudia, A., Lehtila, A., Goldstein, G.: Documentation for the TIMES model. http://www.iea-etsap.org/web/Documentation.asp (2005)

  3. 3.

    Martinsen, D., Krey, V., Markewitz, P., Vögele, S.: A new dynamical bottom-up energy model for Germany: model structure and model results. Paper presented at the 6th IAEE European Conference, Zürich (2004)

  4. 4.

    Rosen, J.: The future role of renewable energy sources in European electricity supply: a model-based analysis for the EU-15. Dissertation, Universität Karlsruhe, Karlsruhe (2008)

    Google Scholar 

  5. 5.

    Nagl, S., Fürsch, M., Paulus, M., Richter, J., Trüby, J., Lindenberger, D.: Energy policy scenarios to reach challenging climate projection targets in the German electricity sector until 2050. Utility Policy 19(3), 8 (2011)

    Article  Google Scholar 

  6. 6.

    BMWi, BMU: The Federal government’s energy concept of 2010 and the transformation of the energy system of 2011. In: N.C.a.N.S. (ed) Federal Ministry of Economics and Technology and Federal Ministry for the Environment, Berlin (2011)

  7. 7.

    EIA: System for the analysis of global energy markets (SAGE). Model documentation report, energy information administration, U.S. Department of Energy (2003)

  8. 8.

    IIASA: Myopic MESSAGE—a model to analyze energy systems and to evaluate policies over a short-to-medium time horizon. http://www.iiasa.ac.at/web/home/research/researchPrograms/Energy/MYOPIC-MESSAGE.en.html (2012)

  9. 9.

    Martinsen, D., Krey, V., Markewitz, P., Vögele, S.: A time step energy process model for Germany: model structure and results. Energy Stud. Rev. 14(1), 3 (2006)

    Google Scholar 

  10. 10.

    Krey, V.: Vergleich kurz- und langfristig ausgerichteter Optimierungsansätze mit einem multi-regionalen Energiesystemmodell unter Berücksichtigung stochastischer Parameter. Dissertation, Ruhr-Universität Bochum (2006)

  11. 11.

    Fishbone, L.G., Abilock, H.: MARKAL: a linear programming model for energy system analysis—technical description on the BNL version. Intern. J. Energy Res. 5/4, 21 (1981)

    Google Scholar 

  12. 12.

    Messner, S.: User’s guide for the matrix generator of message II: model description and implementation guide. IASA WP 84–71a, Laxenburg (1984)

  13. 13.

    Keppo, I., Strubegger, M.: Short term decisions for long term problems: the effect of foresight on model based energy systems analysis. Energy 35(5), 2033–2042 (2010). doi:10.1016/j.energy.2010.01.019

    Article  Google Scholar 

  14. 14.

    Babiker, M., Gurgel, A., Paltsev, S., Reilly, J.: Forward-looking versus recursive-dynamic modeling in climate policy analysis: a comparison. Econ. Model. 26, 14 (2009)

    Article  Google Scholar 

  15. 15.

    Enzensberger, N.: Entwicklung und Anwendung eines Strom- und Zertifikatmarktmodells für den europäischen Energiesektor. Dissertation, Universität Karlsruhe (2003)

  16. 16.

    Schönfelder, M., Jochem, P., Fichtner, W.: Energiesystemmodelle zur Szenarienbildung - Potentiale und Grenzen. In: Dieckhoff, C., (ed.) Energieszenarien. Konstruktion, Bewertung und Wirkung - “Anbieter” und “Nachfrager” im Dialog. KIT Scientific Publishing, Karlsruhe (2011)

  17. 17.

    Keppo, I., Strubegger, M.: Implications of limited foresight and sequential decision making for long-term energy system planning: an application of the myopic MESSAGE model. In: Interim Report IR-09-006. International Institute for Applied Systems Analysis, Laxenburg (2009)

  18. 18.

    Chand, S., Hsu, V.N., Sethi, S.: Forecast, solution, and rolling horizons in operations management problems: a classified bibliography. Manuf. Serv. Oper. Manag. 4(1), 25–43 (2001). doi:10.1287/msom.4.1.25.287

    Google Scholar 

  19. 19.

    Eßer-Frey, A.: Analyzing the regional long-term development of the German power system using a nodal priceing approach. Dissertation, Karlsruher Institute of Technologie (2012)

  20. 20.

    Nolden, C., Schönfelder, M., Eßer-Frey, A., Bertsch, V., Fichtner, W.: Network constraints in techno-economic energy system models. Energy Systems (2013). doi:10.1007/s12667-013-0078-0

  21. 21.

    BGBI: EnLAG - Gesetz zur Beschleunigung des Ausbaus der Höchstspannungsnetze. In: Bundesanzeiger (ed.). p. 2870. Bundesgestzblatt I (2009)

  22. 22.

    BMU: Pilot study 2010. In: Ministry, G.F.E. (ed.). Berlin (2010)

  23. 23.

    Eßer-Frey, A., Fichtner, W.: Analyzing the regional development of the German power system using a nodal pricing approach. Paper presented at the 8th Conference on the European Energy Market (EEM), Zagreb (2011)

  24. 24.

    Gass, S.I.: Linear programming: methods and applications, 5th edn. Dover Publications (2010)

  25. 25.

    IEA: World energy outlook. International Energy Agency, France (2008)

  26. 26.

    Platts: World electric power plants database. Platts, Washington (2005)

  27. 27.

    Nöther, A.: Kraftwerke-online. Database, http://www.kraftwerke-online.de (2012)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sonja Babrowski.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Babrowski, S., Heffels, T., Jochem, P. et al. Reducing computing time of energy system models by a myopic approach. Energy Syst 5, 65–83 (2014). https://doi.org/10.1007/s12667-013-0085-1

Download citation

Keywords

  • Myopic
  • Perfect foresight
  • Energy system modelling
  • PERSEUS