# Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage

arXiv: https://arxiv.org/abs/1801.02128 · HTML: https://mlsanity.com/paper/1801.02128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning](https://mlsanity.com/paper/2108.03236/) (2021)
- [Context-Aware Agentic Power Resources Optimisation in EV using Smart2ChargeApp](https://mlsanity.com/paper/2512.12048/) (2025)

## Abstract (arXiv)

This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties --- charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. We propose a new metric to assess the impact on power grid without solving complete power flow equations. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms --- stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) --- are applied to derive the pricing and electricity procurement policy. A Pareto front of the multiobjective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.