  We consider the sequential decision problem faced by the manager of an
electric vehicle (EV) charging station, who aims to satisfy the charging demand
of the customer while minimizing cost. Since the total time needed to charge
the EV up to capacity is often less than the amount of time that the customer
is away, there are opportunities to exploit electricity spot price variations
within some reservation window. We formulate the problem as a finite horizon
Markov decision process (MDP) and consider a risk-averse objective function by
optimizing under a dynamic risk measure constructed using a convex combination
of expected value and conditional value at risk (CVaR). It has been recognized
that the objective function of a risk-averse MDP lacks a practical
interpretation. Therefore, in both academic and industry practice, the dynamic
risk measure objective is often not of primary interest; instead, the
risk-averse MDP is used as a computational tool for solving problems with
predefined "practical" risk and reward objectives (termed the base model). In
this paper, we study the extent to which the two sides of this framework are
compatible with each other for the EV setting -- roughly speaking, does a "more
risk-averse" MDP provide lower risk in the practical sense as well? In order to
answer such a question, the effect of the degree of dynamic risk-aversion on
the optimal MDP policy is analyzed. Based on these results, we also propose a
principled approximation approach to finding an instance of the risk-averse MDP
whose optimal policy behaves well under the practical objectives of the base
model. Our numerical experiments suggest that EV charging stations can be
operated at a significantly higher level of profitability if dynamic charging
is adopted and a small amount of risk is tolerated.
