Abstract
Soft-linking TIMES models with carefully selected complementary models can provide useful additional insights into the results from the TIMES model and can usefully scrutinize specific TIMES results in greater detail with another model. This multi-model approach can take advantage of the individual strengths of different modelling approaches. This chapter collates methodologies and results from a number of soft-linking exercises with TIMES. Two specific examples are given; firstly the soft-linking of TIMES to a power system model to investigate the TIMES results and provide additional insights into power system flexibility, reliability and market issues. The second example comprises the soft-linking of a TIMES model to a power system and a housing stock model to explore the impacts of increased electrification of residential heating on the power system and associated emissions from the residential sector. These examples show how a multi-model approach and soft-linking can provide a strong complementary analysis to TIMES modelling exercises and generate insights into results that otherwise would be difficult to achieve with a single model approach.
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Notes
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The authors are cognisant that we have simplified how the ETS target is applied in this chapter, i.e. at Member State level rather than EU wide, while the Directive applies the target reduction across the entire EU.
References
Deane JP, Chiodi A, Gargiulo MÓ, Gallachóir BP (2012) Soft-linking of a power systems model to an energy systems model. Energy 42:303–312. doi:10.1016/j.energy.2012.03.052
Deane JP, Drayton GÓ, Gallachóir BP (2014) The impact of sub-hourly modelling in power systems with significant levels of renewable generation. Appl Energy 113:152–158. doi:10.1016/j.apenergy.2013.07.027
De Miglio R, Gargiulo M, Gelmini A, Lanati F (2012) The multiregional energy system model: MONET—a comprehensive tool for energy-environmental analysis at both regional and national level in Italy. Presented at the 12th IAEE European energy conference, Venice, Italy
Dineen DÓ, Gallachóir BP (2011) Modelling the impacts of building regulations and a property bubble on residential space and water heating. Energy Build 43:166–178. doi:10.1016/j.enbuild.2010.09.004
Holttinen H (2012) Wind integration: Experience, issues, and challenges. Wiley Interdiscip. Rev. Energy Environ 1:243–255. doi:10.1002/wene.18
Kannan R (2011) The development and application of a temporal MARKAL energy system model using flexible time slicing. Appl Energy 88:2261–2272. doi:10.1016/j.apenergy.2010.12.066
Ludig S, Haller M, Schmid E, Bauer N (2011) Fluctuating renewables in a long-term climate change mitigation strategy. Energy 36:6674–6685. doi:10.1016/j.energy.2011.08.021
Pina A, Silva C, Ferrão P (2011) Modeling hourly electricity dynamics for policy making in long-term scenarios. Energy Policy 39:4692–4702. doi:10.1016/j.enpol.2011.06.062
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Deane, J.P., Gracceva, F., Chiodi, A., Gargiulo, M., Ó Gallachóir, B. (2015). Soft-Linking Exercises Between TIMES, Power System Models and Housing Stock Models. In: Giannakidis, G., Labriet, M., Ó Gallachóir, B., Tosato, G. (eds) Informing Energy and Climate Policies Using Energy Systems Models. Lecture Notes in Energy, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-16540-0_18
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