Modeling light-duty plug-in electric vehicles for national energy and transportation planning

This paper sets forth a family of models of light-duty plug-in electric vehicle (PEV) fleets, appropriate for conducting long-term national-level planning studies of the energy and transportation sectors in an integrated manner. Using one of the proposed models, three case studies on the evolution of the U.S. energy and transportation infrastructures are performed, where portfolios of optimum investments over a 40-year horizon are identified, and interdependencies between the two sectors are highlighted. The results indicate that with a gradual but aggressive introduction of PEVs coupled with investments in renewable energy, the total cost from the energy and transportation systems can be reduced by 5%, and that overall emissions from electricity generation and light-duty vehicle (LDV) tailpipes can be reduced by 10% over the 40-year horizon. The annual gasoline consumption from LDVs can be reduced by 66% by the end of the planning horizon, but an additional 800TWh of annual electricity demand will be introduced. In addition, various scenarios of greenhouse gas (GHG) emissions reductions are investigated. It is found that GHG emissions can be significantly reduced with only a marginal cost increment, by shifting electricity generation from coal to renewable sources.

[1]  Tony Markel,et al.  Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology , 2007 .

[2]  Dionysios Aliprantis,et al.  Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles , 2012, IEEE Transactions on Smart Grid.

[3]  K. Palmer,et al.  COST-EFFECTIVENESS OF RENEWABLE ELECTRICITY POLICIES , 2005 .

[4]  Vicki Norberg-Bohm,et al.  Technology policy and renewable energy: public roles in the development of new energy technologies , 1999 .

[5]  Richard Barney Carlson,et al.  Calculating Results and Performance Parameters for PHEVs , 2009 .

[6]  W. Short,et al.  A manual for the economic evaluation of energy efficiency and renewable energy technologies , 1995 .

[7]  Eduardo Ibanez A multiobjective optimization approach to the operation and investment of the national energy and transportation systems , 2011 .

[8]  Kevin P. Schneider,et al.  Impacts Assessment of Plug-in Hybrid Vehicles on Electric Utilities and Regional US Power Grids: Part 1: Technical Analysis , 2007 .

[9]  Danilo J. Santini,et al.  Assessing Tank-to-Wheel Efficiencies of Advanced Technology Vehicles , 2003 .

[10]  James D. McCalley,et al.  Modeling Operational Effects of Wind Generation Within National Long-Term Infrastructure Planning Software , 2013, IEEE Transactions on Power Systems.

[11]  Zhenhong Lin,et al.  A Plug-in Hybrid Consumer Choice Model with Detailed Market Segmentation , 2010 .

[12]  M. J. Hutzler,et al.  Emissions of greenhouse gases in the United States , 1995 .

[13]  Seth Blumsack,et al.  Modeling the Impact of Increasing PHEV Loads on the Distribution Infrastructure , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[14]  Alec Brooks,et al.  Demand Dispatch , 2010, IEEE Power and Energy Magazine.

[15]  Tony Markel,et al.  Energy Management Strategies for Plug-In Hybrid Electric Vehicles , 2007 .

[16]  Stacy Cagle Davis,et al.  Transportation energy data book , 2008 .

[17]  S. S. Venkata,et al.  Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses , 2011, IEEE Transactions on Smart Grid.

[18]  Nilay Shah,et al.  Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses , 2010, IEEE PES T&D 2010.

[19]  Lizhi Wang,et al.  Heuristic algorithms for the inverse mixed integer linear programming problem , 2011, J. Glob. Optim..

[20]  Mariesa Crow,et al.  The New Centurions , 2010, IEEE Power and Energy Magazine.

[21]  James D. McCalley,et al.  Multiobjective evolutionary algorithm for long-term planning of the national energy and transportation systems , 2011 .

[22]  Jason Taylor,et al.  Integrating plug-in- electric vehicles with the distribution system , 2009 .

[23]  Lizhi Wang,et al.  Potential impact of recharging plug‐in hybrid electric vehicles on locational marginal prices , 2010 .

[24]  J. McCalley,et al.  A Multiperiod Generalized Network Flow Model of the U.S. Integrated Energy System: Part II—Simulation Results , 2007, IEEE Transactions on Power Systems.

[25]  Mark Jaccard,et al.  The renewable portfolio standard:: design considerations and an implementation survey , 2001 .

[26]  A E Pisarski,et al.  NATIONAL TRANSPORTATION STATISTICS , 2000 .

[27]  Dionysios Aliprantis,et al.  Potential impacts of aggregator-controlled plug-in electric vehicles on distribution systems , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[28]  Liz Marshall,et al.  Biofuels and the Time Value of Carbon: Recommendations for GHG Accounting Protocols , 2009 .

[29]  J. McCalley,et al.  A Multiperiod Generalized Network Flow Model of the U.S. Integrated Energy System: Part I—Model Description , 2007, IEEE Transactions on Power Systems.

[30]  Cong Liu,et al.  Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power , 2011 .

[31]  Dionysios Aliprantis,et al.  National long-term investment planning for energy and transportation systems , 2010, IEEE PES General Meeting.

[32]  Claire Weiller,et al.  Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States , 2011 .

[33]  Sekyung Han,et al.  Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation , 2010, IEEE Transactions on Smart Grid.

[34]  P Frías,et al.  Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks , 2011, IEEE Transactions on Power Systems.

[35]  A. Somani,et al.  National Energy and Transportation Systems: Interdependencies within a Long Term Planning Model , 2008, 2008 IEEE Energy 2030 Conference.

[36]  S. Maithel Energy Efficiency and Renewable Energy , 2008 .

[37]  Jonn Axsen,et al.  Reflexive Layers of Influence (RLI): A Model of Social Influence, Vehicle Purchase Behavior, and Pro-Societal Values , 2010 .

[38]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[39]  Matthew Chambers,et al.  Transportation Statistics Annual Report 2010 , 2010 .

[40]  Javier Campos,et al.  A review of HSR experiences around the world , 2007 .

[41]  Yang Gu,et al.  Sustainability and Resiliency Measures for Long-Term Investment Planning in Integrated Energy and Transportation Infrastructures , 2012 .

[42]  Stanton W. Hadley,et al.  Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation , 2009 .

[43]  Di Wu,et al.  Electric Energy and Power Consumption by Light-Duty Plug-In Electric Vehicles , 2011, IEEE Transactions on Power Systems.

[44]  Riccardo Fagiani,et al.  Cost and emissions impacts of plug-in hybrid vehicles on the Ohio power system , 2010 .

[45]  M. Ilic,et al.  Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets , 2011, IEEE Transactions on Power Systems.

[46]  Margaret J. Eppstein,et al.  An agent-based model to study market penetration of plug-in hybrid electric vehicles , 2011 .

[47]  Jinxu Ding,et al.  Parallel Computing Solution for Capacity Expansion Network Flow Optimization Problems , 2012 .