# Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning

arXiv: https://arxiv.org/abs/2203.01654 · HTML: https://mlsanity.com/paper/2203.01654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning](https://mlsanity.com/paper/1809.10679/) (2018)

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

- [Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning](https://mlsanity.com/paper/2203.14078/) (2022)

## Abstract (arXiv)

Electric vehicle (EV) charging stations represent a substantial load with significant flexibility. The exploitation of that flexibility in demand response (DR) algorithms becomes increasingly important to manage and balance demand and supply in power grids. Model-free DR based on reinforcement learning (RL) is an attractive approach to balance such EV charging load. We build on previous research on RL, based on a Markov decision process (MDP) to simultaneously coordinate multiple charging stations. However, we note that the computationally expensive cost function adopted in the previous research leads to large training times, which limits the feasibility and practicality of the approach. We, therefore, propose an improved cost function that essentially forces the learned control policy to always fulfill any charging demand that does not offer any flexibility. We rigorously compare the newly proposed batch RL fitted Q-iteration implementation with the original (costly) one, using real-world data. Specifically, for the case of load flattening, we compare the two approaches in terms of (i) the processing time to learn the RL-based charging policy, as well as (ii) the overall performance of the policy decisions in terms of meeting the target load for unseen test data. The performance is analyzed for different training periods and varying training sample sizes. In addition to both RL policies performance results, we provide performance bounds in terms of both (i) an optimal all-knowing strategy, and (ii) a simple heuristic spreading individual EV charging uniformly over time