Towards smart grids: Identifying the risks that arise from the integration of energy and transport supply chains

This paper identifies the risks for the functionality and reliability of the grid that arise from the integration of the transport and supply chain. The electrification of transport is a promising option for the transition to a low carbon energy and transport system. But on the short term, the electrification of transport also creates risks. More specifically, when promising technological such as vehicle-to-grid and smart-grids are not yet available on a large scale, the rapid diffusion of electric vehicles and the recharging behaviour of consumers can create risks for grid functioning. In order to assess these risks, this paper present a GIS-based simulation method that assesses electricity demand and supply on the neighbourhood level. The paper combines local level electric vehicle diffusion forecasts, with neighbourhood level data about the grid additional capacity. Application of the model to the Netherlands shows that risks for grid functioning already appear as early as 2015. More specifically, the diffusion of electric vehicles is found to compromise the functioning of the grid on the short term in densely populated areas such as Amsterdam. In these neighbourhoods early and fast adoption of electric vehicles coincides with the presence of an older grid with less additional capacity. The model provides insights for grid operators as well as for policy makers that seek to stimulate the transition to sustainable energy and transport systems, and can be used as a strategic tool to plan (smart) grid investments.

[1]  Jillian Anable,et al.  The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles , 2013 .

[2]  J. Bekkering,et al.  Balancing gas supply and demand with a sustainable gas supply chain – A study based on field data , 2013 .

[3]  Wilfried van Sark,et al.  Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study , 2015 .

[4]  Arnulf Grubler,et al.  The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change in Transport , 1990 .

[5]  Fredrik Wallin,et al.  Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .

[6]  Saifur Rahman,et al.  An investigation into the impact of electric vehicle load on the electric utility distribution system , 1993 .

[7]  Maria Saxe,et al.  Strategies for a road transport system based on renewable resources – The case of an import-independent Sweden in 2025 , 2010 .

[8]  Stanton W. Hadley,et al.  Impact of Plug-in Hybrid Vehicles on the Electric Grid , 2006 .

[9]  Floortje Alkemade,et al.  Managing the Diffusion of Low Emission Vehicles , 2012, IEEE Transactions on Engineering Management.

[10]  Shiwei Yu,et al.  Carbon emission coefficient measurement of the coal-to-power energy chain in China , 2014 .

[11]  Stephen Skippon,et al.  Responses to battery electric vehicles: UK consumer attitudes and attributions of symbolic meaning following direct experience to reduce psychological distance , 2011 .

[12]  Yunfei Mu,et al.  A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles ☆ , 2014 .

[13]  Rona Webster Can the electricity distribution network cope with an influx of electric vehicles , 1999 .

[14]  Mikko Kolehmainen,et al.  Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption , 2013 .

[15]  Johan Driesen,et al.  The impact of vehicle-to-grid on the distribution grid , 2011 .

[16]  Robert C. Green,et al.  The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook , 2010, IEEE PES General Meeting.

[17]  Andrew Higgins,et al.  Combining choice modelling and multi-criteria analysis for technology diffusion: An application to the uptake of electric vehicles , 2012 .

[18]  Johann Kranz,et al.  The role of smart metering and decentralized electricity storage for smart grids: The importance of positive externalities , 2012 .

[19]  Taraneh Sowlati,et al.  Modeling and analysing storage systems in agricultural biomass supply chain for cellulosic ethanol production , 2013 .

[20]  Christian Thiel,et al.  Assessing factors for the identification of potential lead markets for electrified vehicles in Europe: expert opinion elicitation , 2012 .

[21]  Floortje Alkemade,et al.  Expectations as a key to understanding actor strategies in the field of fuel cell and hydrogen vehicles , 2012, Technological forecasting and social change.

[22]  Ger Devlin,et al.  Energy Regulations: A Case for Peat and Wood Fibre in Ireland. , 2014 .

[23]  E. Rogers Diffusion of Innovations , 1962 .

[24]  André Faaij,et al.  Fulfilling the electricity demand of electric vehicles in the long term future: An evaluation of centralized and decentralized power supply systems , 2013 .

[25]  J. C. Fisher,et al.  A simple substitution model of technological change , 1971 .

[26]  Zhe Chen,et al.  Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China , 2013 .