# Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.03236 · HTML: https://mlsanity.com/paper/2108.03236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Smoothed Least-Laxity-First Algorithm for EV Charging](https://mlsanity.com/paper/2102.08610/) (2021)
- [Online Coordinated Charging Decision Algorithm for Electric Vehicles without Future Information](https://mlsanity.com/paper/1310.3580/) (2013)
- [Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks](https://mlsanity.com/paper/1511.03611/) (2015)
- [Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage](https://mlsanity.com/paper/1801.02128/) (2018)

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

- [Laxity-Aware Scalable Reinforcement Learning for HVAC Control](https://mlsanity.com/paper/2306.16619/) (2023)

## Abstract (arXiv)

Effectively operating electrical vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces scales with the number of EVs and is thus very large and time-varying. This dimensionality issue affects the efficiency and convergence properties of generic RL algorithms. We develop aggregation schemes that are based on the emergency of EV charging, namely the laxity value. A least-laxity first (LLF) rule is adopted to consider only the total charging power of the EVCS which ensures the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Based on the proposed representation, policy gradient method is used to find the best parameters for the linear Gaussian policy . Numerical results have validated the performance improvement of the proposed representation approaches in attaining higher rewards and more effective policies as compared to existing approximation based approach.