TIMES-DK: Technology-rich multi-sectoral optimisation model of the Danish energy system

Abstract As Denmark progresses towards a carbon neutral future, energy system models are required to address the challenges of the energy transition. This article describes design, input data and current usage of TIMES-DK, the first Danish energy system model that includes the complete national energy system, covering long-term technology investments. The article aims at explaining the modelling approach; highlighting strengths and reflecting upon limitations of the model; illustrating possible applications of TIMES-DK and inspiring new model developments. Some of the key strengths of the model include simultaneous optimisation of operation and investments across the complete energy system over the whole modelling horizon, explicit representation of the most important sectors of the economy, modular structure and the possibility of linking to a computable general equilibrium model for an additional insight on, e.g. public finance or CO2-leakage. TIMES-DK is being developed in close collaboration between an energy agency, a university and a consulting firm, to improve its robustness, relevance and impact on policy making. It allows for a wide range of applications including exploratory energy scenarios and policy analysis. To meet challenges of the future, further development of the model is needed and consequently the article provides references to ongoing projects addressing current development needs, such as improved representation of transport and flexible handling of the temporal dimension. To support a democratic and transparent process around decisions for the future Danish energy system, TIMES-DK should become available to interested parties.

[1]  Jacopo Tattini,et al.  Reaching carbon neutral transport sector in Denmark – Evidence from the incorporation of modal shift into the TIMES energy system modeling framework , 2018 .

[2]  Iain Staffell,et al.  Opening the black box of energy modelling: strategies and lessons learned , 2017, ArXiv.

[3]  William D'haeseleer,et al.  Impact of the level of temporal and operational detail in energy-system planning models , 2016 .

[4]  S. Iniyan,et al.  A review of energy models , 2006 .

[5]  Kenneth Karlsson,et al.  Heat supply planning for the ecological housing community Munksøgård , 2016 .

[6]  Jeppe Rich,et al.  The Danish national passenger model – Model specification and results , 2016 .

[7]  Kenneth Bernard Karlsson,et al.  Danish heat atlas as a support tool for energy system models , 2014 .

[8]  Adam Hawkes,et al.  Energy systems modeling for twenty-first century energy challenges , 2014 .

[9]  W. Nijs,et al.  How far away is hydrogen? Its role in the medium and long-term decarbonisation of the European energy system , 2016 .

[10]  C. Thiel,et al.  Hydrogen supply chain architecture for bottom-up energy systems models. Part 2: Techno-economic inputs for hydrogen production pathways , 2014 .

[11]  D. Tonini,et al.  Environmental implications of the use of agro‐industrial residues for biorefineries: application of a deterministic model for indirect land‐use changes , 2016 .

[12]  Maurizio Gargiulo,et al.  Bridging the gap using energy services: Demonstrating a novel framework for soft linking top-down and bottom-up models , 2019, Energy.

[13]  Frans Berkhout,et al.  Bridging analytical approaches for low-carbon transitions , 2016 .

[14]  Semida Silveira,et al.  OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development , 2011 .

[15]  Brian Ó Gallachóir,et al.  Long‐term energy models: Principles, characteristics, focus, and limitations , 2013 .

[16]  Kenneth Bernard Karlsson,et al.  Residential heat pumps in the future Danish energy system , 2016 .

[17]  J. DeCarolis,et al.  The case for repeatable analysis with energy economy optimization models , 2012, 2001.10858.

[18]  Alessio Boldrin,et al.  Optimised biogas production from the co-digestion of sugar beet with pig slurry: Integrating energy, GHG and economic accounting , 2016 .

[19]  Stefan Petrovic,et al.  Model for Determining Geographical Distribution of Heat Saving Potentials in Danish Building Stock , 2014, ISPRS Int. J. Geo Inf..

[20]  Steve Pye,et al.  Modelling sustainable urban travel in a whole systems energy model , 2015 .

[21]  M. Thring.  World Energy Outlook , 1977 .

[22]  Subhes C. Bhattacharyya,et al.  A review of energy system models , 2010 .

[23]  Will McDowall,et al.  Energy scenario choices: Insights from a retrospective review of UK energy futures , 2016 .

[24]  Eamonn Mulholland,et al.  A Long-Term Strategy to Decarbonise the Danish Inland Passenger Transport Sector , 2018 .

[25]  Son H. Kim,et al.  Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands , 2011 .

[26]  Detlef P. van Vuuren,et al.  Exploring the implications of lifestyle change in 2 °C mitigation scenarios using the IMAGE integrated assessment model , 2016 .

[27]  Patrícia Fortes,et al.  Long-term energy scenarios: : Bridging the gap between socio-economic storylines and energy modeling , 2015 .

[28]  Christopher Yang,et al.  Improving the representation of modal choice into bottom-up optimization energy system models - The MoCho-TIMES model , 2018 .

[29]  H. Wenzel,et al.  Environmental consequences of different carbon alternatives for increased manure-based biogas , 2014 .

[30]  Pantelis Capros,et al.  Implications of delaying transport decarbonisation in the EU: A systems analysis using the PRIMES model , 2018, Energy Policy.

[31]  Neil Strachan,et al.  Reinventing the energy modelling–policy interface , 2016, Nature Energy.

[32]  M. Strubegger,et al.  The marker quantification of the Shared Socioeconomic Pathway 2: A middle-of-the-road scenario for the 21st century , 2017 .

[33]  J. Murphy,et al.  The potential for biomethane from grass and slurry to satisfy renewable energy targets. , 2013, Bioresource technology.

[34]  Weilong Huang,et al.  TIMES modelling of transport sector in China and USA: Comparisons from a decarbonization perspective☆ , 2016 .

[35]  Marie Münster,et al.  STREAM–an energy scenario modelling tool , 2018, Energy Strategy Reviews.

[36]  Toshihiko Nakata,et al.  Application of energy system models for designing a low-carbon society , 2011 .

[37]  Kalai Ramea,et al.  Achieving California's 80% greenhouse gas reduction target in 2050: Technology, policy and scenario analysis using CA-TIMES energy economic systems model , 2015 .