  In the context of charging electric vehicles (EVs), the price-based demand
response (PBDR) is becoming increasingly significant for charging load
management. Such response usually encourages cost-sensitive customers to adjust
their energy demand in response to changes in price for financial incentives.
Thus, to model and optimize EV charging, it is important for charging station
operator to model the PBDR patterns of EV customers by precisely predicting
charging demands given price signals. Then the operator refers to these demands
to optimize charging station power allocation policy. The standard pipeline
involves offline fitting of a PBDR function based on historical EV charging
records, followed by applying estimated EV demands in downstream charging
station operation optimization. In this work, we propose a new decision-focused
end-to-end framework for PBDR modeling that combines prediction errors and
downstream optimization cost errors in the model learning stage. We evaluate
the effectiveness of our method on a simulation of charging station operation
with synthetic PBDR patterns of EV customers, and experimental results
demonstrate that this framework can provide a more reliable prediction model
for the ultimate optimization process, leading to more effective optimization
solutions in terms of cost savings and charging station operation objectives
with only a few training samples.
