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

TL;DR

This paper develops stochastic dynamic pricing and energy management strategies for EV charging stations with renewable energy and storage, addressing demand and supply uncertainties to optimize profitability and grid impact.

Contribution

It introduces a new impact metric on the power grid and compares stochastic dynamic programming with a greedy benchmark for pricing and energy procurement.

Findings

SDP algorithm achieves up to 7% higher profit than greedy algorithm.

Pricing strategies can reshape charging demand to mitigate grid impact.

The model balances profitability, customer satisfaction, and grid stability.

Abstract

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.