Spatially and Temporally Explicit Energy System Modelling to Support the Transition to a Low Carbon Energy Infrastructure – Case Study for Wind Energy in the UK

Renewable energy sources and electricity demand vary with time and space and the energy system is constrained by the location of the current infrastructure in place. The transitioning to a low carbon energy society can be facilitated by combining long term planning of infrastructure with taking spatial and temporal characteristics of the energy system into account. There is a lack of studies addressing this systemic view. We soft-link two models in order to analyse long term investment decisions in generation, transmission and storage capacities and the effects of short-term fluctuation of renewable supply: The national energy system model UKTM (UK TIMES model) and a dispatch model. The modelling approach combines the benefits of two models: an energy system model to analyse decarbonisation pathways and a power dispatch model that can evaluate the technical feasibility of those pathways and the impact of intermittent renewable energy sources on the power market. Results give us the technical feasibility of the UKTM solution from 2010 until 2050. This allows us to determine lower bounds of flexible elements and feeding them back in an iterative process (e.g. storage, demand side control, balancing). We apply the methodology to study the long-term investments of wind infrastructure in the United Kingdom.

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

[2]  Christian von Hirschhausen,et al.  “Take the long way down”: Integration of large-scale North Sea wind using HVDC transmission , 2010 .

[3]  Carlos Silva,et al.  High-resolution modeling framework for planning electricity systems with high penetration of renewables , 2013 .

[4]  Tim Cockerill,et al.  Measuring significant variability characteristics: An assessment of three UK renewables , 2013 .

[5]  M. Haller,et al.  Decarbonization scenarios for the EU and MENA power system: Considering spatial distribution and short term dynamics of renewable generation , 2012 .

[6]  Manuel Welsch,et al.  Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code , 2012 .

[7]  G. Sinden.  Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand , 2007 .

[8]  Gareth Harrison,et al.  Variability and phasing of tidal current energy around the United Kingdom , 2013 .

[9]  R. Kannan,et al.  A Long-Term Electricity Dispatch Model with the TIMES Framework , 2013, Environmental Modeling & Assessment.

[10]  Keywan Riahi,et al.  Impacts of considering electric sector variability and reliability in the MESSAGE model , 2013 .

[11]  R. Green,et al.  Market behaviour with large amounts of intermittent generation , 2010 .

[12]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

[13]  Maryse Labriet,et al.  ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure , 2008, Comput. Manag. Sci..

[14]  Brian Ó Gallachóir,et al.  Soft-linking of a power systems model to an energy systems model , 2012 .

[15]  Thomas Huld,et al.  Medium-term demand for European cross-border electricity transmission capacity , 2013 .

[16]  Richard Loulou,et al.  ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation , 2008, Comput. Manag. Sci..

[17]  M. Haller,et al.  Fluctuating renewables in a long-term climate change mitigation strategy , 2011 .