Impact of the level of temporal and operational detail in energy-system planning models

Applied Energy

Volumn 162, Issue , 2016, Pages 631-643

  Poncelet, Kris ^a,b,c   Delarue, Erik ^a,c   Six, Daan ^b,c   Duerinck, Jan ^b,c   D'haeseleer, William ^a,c  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b FLEMISH INSTITUTE FOR TECHNOLOGICAL RESEARCH VITO   (Belgium)

^c ENERGYVILLE   (Belgium)

Author keywords

Energy system planning; Integration of renewable energy sources; Operational constraints; Power systems; Temporal resolution; TIMES

Indexed keywords

NATURAL RESOURCES; RENEWABLE ENERGY RESOURCES; STANDBY POWER SYSTEMS;

ENERGY SYSTEMS; INTEGRATION OF RENEWABLE ENERGIES; OPERATIONAL CONSTRAINTS; TEMPORAL RESOLUTION; TIMES;

ELECTRIC POWER SYSTEM PLANNING;

ENERGY PLANNING; ENERGY RESOURCE; MODEL VALIDATION; OPERATIONS TECHNOLOGY; POWER PLANT; PRIORITIZATION; RENEWABLE RESOURCE; TECHNICAL EFFICIENCY; TEMPORAL ANALYSIS;

EID: 84948706294     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.10.100     Document Type: Article

References (37)

1. Connolly D., Lund H., Mathiesen B., Leahy M. A review of computer tools for analysing the integration of renewable energy into various energy systems. Appl Energy 2010, 87(4):1059-1082. . 10.1016/j.apenergy.2009.09.026.
2. Devogelaer D, Duerinck J, Gusbin D, Marenne Y, Nijs W, Orsini M, et al. Towards 100% renewable energy in Belgium by 2050, Tech. rep., VITO, ICEDD and the Belgian Federal Planning Bureau; 2013.
3. Karlsson K., Meibom P. Optimal investment paths for future renewable based energy systems-using the optimisation model Balmorel. Int J Hydrogen Energy 2008, 33(7):1777-1787. . 10.1016/j.ijhydene.2008.01.031.
4. Chiodi A., Gargiulo M., Rogan F., Deane J., Lavigne D., Rout U.K., et al. Modelling the impacts of challenging 2050 european climate mitigation targets on Irelands energy system. Energy Policy 2013, 53:169-189. . 10.1016/j.enpol.2012.10.045.
5. Strachan N., Balta-Ozkan N., Joffe D., McGeevor K., Hughes N. Soft-linking energy systems and GIS models to investigate spatial hydrogen infrastructure development in a low-carbon UK energy system. Int J Hydrogen Energy 2009, 34(2):642-657. . 10.1016/j.ijhydene.2008.10.083.
6. 2 mitigation option for germany?-a bottom-up approach. Energy Policy 2007, 35(4):2110-2120. . 10.1016/j.enpol.2006.06.017.
7. Most D, Genoese M, Esser A, Rentz O. European electricity and emission market modeling; the design of emission allocation plans and its effects on investment planning. In: 5th International conference on European Electricity market, 2008. EEM 2008; 2008. p. 1-6. doi:http://dx.doi.org/10.1109/EEM.2008.4579087. doi:10.1109/EEM.2008.4579087.
8. Luderer G., Krey V., Calvin K., Merrick J., Mima S., Pietzcker R., et al. The role of renewable energy in climate stabilization: results from the EMF27 scenarios. Climatic Change 2014, 123(3-4):427-441. 10.1007/s10584-013-0924-z.
9. Lew D, Brinkman G, Ibanez E, Florita A, Heaney M, Hodge B-M, et al. The western wind and solar integration study phase 2, Tech. rep., National Renewable Energy Laboratory (NREL); 2013.
10. Van den Bergh K, Delarue E, D'haeseleer W. The impact of renewable injections on cycling of conventional power plants. In: 2013 10th International conference on European Energy Market (EEM); 2013. p. 1-8. doi:http://dx.doi.org/10.1109/EEM.2013.6607322. doi:10.1109/EEM.2013.6607322.
11. Van den Bergh K, Delarue E. Facilitating variable generation of renewables by conventional power plant cycling. In: 9th Conference on energy economics and technology (ENERDAY); 2014.
12. Milligan M, Donohoo P, Lew D, Ela E, Kirby B, Holttinen H, et al. Operating reserves and wind power integration: an international comparison, Tech. rep., NREL; 2010.
13. Bruninx K, Delarue E, D'haeseleer W. The cost of wind power forecast errors in the belgian power system. In: IAEE, 37th international conference; 2014.
14. Palmintier B, Webster M. Impact of unit commitment constraints on generation expansion planning with renewables. In: 2011 IEEE Power and energy society general meeting; 2011. p. 1-7. doi:http://dx.doi.org/10.1109/PES.2011.6038963. doi:10.1109/PES.2011.6038963.
15. Pfenninger S., Hawkes A., Keirstead J. Energy systems modeling for twenty-first century energy challenges. Renew Sustain Energy Rev 2014, 33:74-86. . 10.1016/j.rser.2014.02.003.
16. Deane J., Chiodi A., Gargiulo M., Gallachóir B.P. Soft-linking of a power systems model to an energy systems model. Energy 2012, 42(1):303-312. 8th world energy system conference, WESC 2010. . 10.1016/j.energy.2012.03.052.
17. Rosen J., Tietze-Stöckinger I., Rentz O. Model-based analysis of effects from large-scale wind power production. Energy 2007, 32(4):575-583. eCOS 05. 18th international conference on efficiency, cost, optimization, simulation, and environmental impact of energy systems ECOS 05. . 10.1016/j.energy.2006.06.022.
18. Pina A., Silva C.A., Ferrão P. High-resolution modeling framework for planning electricity systems with high penetration of renewables. Appl Energy 2013, 112:215-223. . 10.1016/j.apenergy.2013.05.074.
19. Ludig S., Haller M., Schmid E., Bauer N. Fluctuating renewables in a long-term climate change mitigation strategy. Energy 2011, 36(11):6674-6685. http://www.sciencedirect.com/science/article/pii/S0360544211005512. 10.1016/j.energy.2011.08.021.
20. Haydt G., Leal V., Pina A., Silva C.A. The relevance of the energy resource dynamics in the mid/long-term energy planning models. Renew Energy 2011, 36(11):3068-3074. . 10.1016/j.renene.2011.03.028.
21. Pina A., Silva C., Ferrão P. Modeling hourly electricity dynamics for policy making in long-term scenarios. Energy Policy 2011, 39(9):4692-4702. . 10.1016/j.enpol.2011.06.062.
22. Welsch M., Deane P., Howells M., Gallachóir B.Ó., Rogan F., Bazilian M., et al. Incorporating flexibility requirements into long-term energy system models - a case study on high levels of renewable electricity penetration in Ireland. Appl Energy 2014, 135:600-615. . 10.1016/j.apenergy.2014.08.072.
23. Kannan R., Turton H. A long-term electricity dispatch model with the TIMES framework. Environ Model Assess 2013, 18(3):325-343. 10.1007/s10666-012-9346-y.
24. Nweke C, Leanez F, Drayton G, Kolhe M. Benefits of chronological optimization in capacity planning for electricity markets. In: 2012 IEEE international conference on power system technology (POWERCON); 2012. p. 1-6. doi:http://dx.doi.org/10.1109/PowerCon.2012.6401421. doi:10.1109/PowerCon.2012.6401421.
25. Loulou R, Remne U, Kanudia A, Lehtila A, Goldstein G. Documentation for the TIMES model: Part I, ETSAP, April 2005.
26. Van den Bergh K, Bruninx K, Delarue E, D'haeseleer W. Lusym, a unit commitment model formulated as a mixed-integer linear program, July 2015.
27. Simoes S, Nijs W, Ruiz P, Sgobbi A, Radu D, Bolat P, et al. The JRC-EU-TIMES model - assessing the long-term role of the SET plan energy technologies, Tech. rep., JRC's Institute for Energy and Transport; 2013.
28. Projected costs of generating electricity - 2010 edition, Tech. rep., IEA and NEA; 2010.
29. Schröder A, Kunz F, Meiss J, Mendelevitch R, von Hirschhausen C. Current and prospective costs of electricity generation until 2050, Tech. rep., DIW Berlin; 2013.
30. Elia grid data, July 2014. http://www.elia.be/en/grid-data.
31. World energy outlook 2013, Tech. rep., IEA; 2013.
32. Ueckerdt F., Brecha R., Luderer G., Sullivan P., Schmid E., Bauer N., et al. Variable renewable energy in modeling climate change mitigation scenarios. International energy workshop 2011, Stanford. 30th ed.
33. Jonghe C.D., Delarue E., Belmans R., D'haeseleer W. Determining optimal electricity technology mix with high level of wind power penetration. Appl Energy 2011, 88(6):2231-2238. . 10.1016/j.apenergy.2010.12.046.
34. Stoft S. Power system economics: designing markets for electricity 2002, IEEE Press and Wiley-Interscience.
35. de Sisternes FJ, Webster MD. Optimal selection of sample weeks for approximating the net load in generation planning problems, March 2013.
36. Nahmmacher P, Schmid E, Hirth L, Knopf B. Carpe diem: a novel approach to select representative days for long-term power system models with high shares of renewable energy sources. SSRN J. doi:http://dx.doi.org/10.2139/ssrn.2537072. doi:10.2139/ssrn.2537072.
37. Poncelet K, Höschle H, Delarue E, D'haeseleer W. Selecting representative days for investment planning models, KU Leuven, Division of applied mechanics and energy conversion, Working paper EN2015-10.