# Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging

arXiv: https://arxiv.org/abs/2404.10311 · HTML: https://mlsanity.com/paper/2404.10311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [On the Differentiability of the Solution to Convex Optimization Problems](https://mlsanity.com/paper/1804.05098/) (2018)
- [DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model](https://mlsanity.com/paper/2308.09857/) (2023)
- [Deadline Differentiated Pricing of Deferrable Electric Loads](https://mlsanity.com/paper/1407.1601/) (2014)

## Built on by (most cited first, 1 of 1)

- [Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times](https://mlsanity.com/paper/2606.19199/) (2026)

## Abstract (arXiv)

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.