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.

这篇论文提出了一个新的决策为中心的端到端框架，将模型学习阶段中的预测误差和下游优化成本误差相结合，用于准确预测充电需求并优化充电站功率分配策略，实验证明该框架能够提供更可靠的预测模型，从而在仅有少量训练样本的情况下实现更有效的成本节约和充电站运营目标优化。