Smart Grid Charging of Electric Vehicles: EV-Owner Response to Scheduling and Pricing under Myopic Loss Aversion in an Ultimatum Two-Player Game

Upward expectations of future electric vehicle (EV) growth pose the question about the future load on the electricity grid. While the literature on demand side management of EV charging has focused on technical aspects and considered EV-owners as utility maximizers, this study looks at the neglected psychological dynamics of EV-owners facing charging decisions and interacting with the supplier. This study represents these dynamics by proposing a behavioral framework of utility maximization under myopic loss aversion within an ultimatum two-player game framework. The EV-owner and the electricity supplier are the two players, the EV-owner faces three decisions (i.e., whether to postpone the charging to off-peak periods, which discount to request to the supplier for off-peak charging, which discount to accept for supplier-controlled charging), and there are two contract durations where the EV-owner decides daily (short-term) or weekly (long-term). The experimental analysis included six treatment conditions from the combinations of the three decisions with the two contract durations, and results showed that: (i) EV-owners perform charging choices not as pure utility maximizers, but are affected by myopic loss aversion resulting from monetary considerations as well as the ultimatum game with the supplier; (ii) EV-owners are open towards centralized smart-grid strategies optimizing the load on the grid from a system optimum perspective; (iii) the frequency of charging decisions (daily versus weekly contract) favors on the one hand utility maximization behavior of EV-owners and induces on the other hand myopia with a favorable cost minimization for the supplier.

[1]  Kay W. Axhausen,et al.  Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation , 2013 .

[2]  Stefan Lindhard Mabit,et al.  Demand for alternative-fuel vehicles when registration taxes are high , 2011 .

[3]  Alois Knoll,et al.  A price-responsive dispatching strategy for Vehicle-to-Grid: An economic evaluation applied to the case of Singapore , 2014 .

[4]  Elisabetta Cherchi,et al.  On the stability of preferences and attitudes before and after experiencing an electric vehicle , 2013 .

[5]  Jong-Youn Rha,et al.  The mental accounting of time , 2009 .

[6]  M. O’Mahony,et al.  Travel to work in Dublin. The potential impacts of electric vehicles on climate change and urban air quality , 2011 .

[7]  C. Fitzpatrick,et al.  Demand side management of electric car charging: Benefits for consumer and grid , 2012 .

[8]  Mafalda Mendes Lopes,et al.  A rule-based approach for determining the plausible universe of electric vehicle buyers in the Lisbon Metropolitan Area , 2014 .

[9]  Andrew M. Hardin,et al.  Myopic loss aversion: Demystifying the key factors influencing decision problem framing , 2012 .

[10]  A. Tversky,et al.  The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test , 1997 .

[11]  Rabikar Chatterjee,et al.  Reservation Price as a Range: An Incentive-Compatible Measurement Approach , 2007 .

[12]  John K. Dagsvik,et al.  Potential demand for alternative fuel vehicles , 2002 .

[13]  Kenneth S Kurani,et al.  Do You Mind If I Plug in My Car? How Etiquette Shapes PEV Drivers' Vehicle Charging Behavior , 2013 .

[14]  Jonn Axsen,et al.  Anticipating plug-in hybrid vehicle energy impacts in California: Constructing consumer-informed recharge profiles , 2010 .

[15]  Donald V. Moser,et al.  Fairness in Ultimatum Games with Asymmetric Information and Asymmetric Payoffs , 1996 .

[16]  Silvia Canale,et al.  Electric vehicles charging control in a smart grid: A model predictive control approach , 2014 .

[17]  Rashid A. Waraich Plug-in hybrid electric vehicles and smart grids: Investigations based on a micro-simulation , 2009 .

[18]  Thomas P. Lyon,et al.  Is “smart charging” policy for electric vehicles worthwhile? , 2012 .

[19]  Alan G. Sanfey,et al.  Affective state and decision-making in the Ultimatum Game , 2006, Experimental Brain Research.

[20]  Kenneth Lebeau,et al.  The market potential for plug-in hybrid and battery electric vehicles in Flanders: A choice-based conjoint analysis , 2012 .

[21]  M. Raberto,et al.  An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland , 2012 .

[22]  Andrés Perea,et al.  On Loss Aversion in Bimatrix Games , 2007 .

[23]  Thomas Franke,et al.  Understanding charging behaviour of electric vehicle users , 2013 .

[24]  J. Stevens Intertemporal Choice , 2013 .

[25]  R. Thaler,et al.  Anomalies: Ultimatums, Dictators and Manners , 1995 .

[26]  R. Thaler,et al.  Myopic Loss Aversion and the Equity Premium Puzzle , 1993 .

[27]  Martin Wietschel,et al.  Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles , 2012 .

[28]  Jeremy J. Michalek,et al.  US residential charging potential for electric vehicles , 2013 .

[29]  F. A. Amoroso,et al.  Advantages of efficiency-aware smart charging strategies for PEVs , 2012 .

[30]  Henrik Madsen,et al.  Optimal charging of an electric vehicle using a Markov decision process , 2013, 1310.6926.

[31]  Juha Kiviluoma,et al.  Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a sys , 2011 .

[32]  U. Fischbacher z-Tree: Zurich toolbox for ready-made economic experiments , 1999 .