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A stochastic receding horizon approach to manage an EV charging station participating in DR programs for RECs has been proposed.Additional developments may regard the adaptation of the procedure to dynamic traffic conditions, the integration of renewable resources, and the experimental validation of the approach in real-world RECsThe developed procedure implements a stochastic approach that is (i) able to exploit online information to schedule the EV charging such that the aggregated power consumption lies within prescribed energy bounds during DR windows, and is (ii) computationally feasible to be deployed in real-world applications.Moreover, the computation time remains feasible even for scenarios involving a high EV penetration.
A stochastic receding horizon approach to manage an EV charging
station participating in DR programs for RECs has been proposed. The
developed procedure implements a stochastic approach that is (i) able
to exploit online information to schedule the EV charging such that the
aggregated power consumption lies within prescribed energy bounds
during DR windows, and is (ii) computationally feasible to be deployed
in real-world applications. Simulation results show that the proposed
approach provides a substantial cost reduction with respect to the
uncoordinated charging, as well as an increase of the performance of
about 12% with respect to the optimization-based benchmark that does
not exploit future forecast flexibility. Moreover, the computation time
remains feasible even for scenarios involving a high EV penetration.
Ultimately, results suggest that EVCSs, acting as aggregators of EVs,
can be effective targets for DR programs: when smart charging policies are implemented, EVCSs can successfully contribute to the local
demand–supply balance by suitably shifting their loads.
Future works may include the description of the customer satisfaction by probabilistic constraints, to allow for a greater degree
of freedom, and robustification against distributional ambiguity in
the case of estimated distributions from finite historical data. Additional developments may regard the adaptation of the procedure to
dynamic traffic conditions, the integration of renewable resources, and
the experimental validation of the approach in real-world RECs
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