[Paper Review] Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties

Chao Luo, Yih-Fang Huang+1 more|RePEc: Research Papers in Economics|Jan 9, 2018

Electric Vehicles and Infrastructure4 citations

TL;DR

This paper proposes a dynamic pricing and energy management framework for EV charging stations using dynamic programming (DP) to optimize profits, customer satisfaction, and grid stability under three key uncertainties: fluctuating wholesale electricity prices, variable renewable energy generation, and spatial-temporal EV charging demand. The DP-based approach outperforms a greedy algorithm by up to 9% in profit and demonstrates that low-cost energy storage reduces price volatility and enhances profitability by enabling 'buy low, sell high' electricity arbitrage.

ABSTRACT

This paper presents a dynamic pricing and energy management framework for electric vehicle (EV) charging service providers. To set the charging prices, the service providers faces three uncertainties: the volatility of wholesale electricity price, intermittent renewable energy generation, and spatial-temporal EV charging demand. The main objective of our work here is to help charging service providers to improve their total profits while enhancing customer satisfaction and maintaining power grid stability, taking into account those uncertainties. We employ a linear regression model to estimate the EV charging demand at each charging station, and introduce a quantitative measure for customer satisfaction. Both the greedy algorithm and the dynamic programming (DP) algorithm are employed to derive the optimal charging prices and determine how much electricity to be purchased from the wholesale market in each planning horizon. Simulation results show that DP algorithm achieves an increased profit (up to 9%) compared to the greedy algorithm (the benchmark algorithm) under certain scenarios. Additionally, we observe that the integration of a low-cost energy storage into the system can not only improve the profit, but also smooth out the charging price fluctuation, protecting the end customers from the volatile wholesale market.

Motivation & Objective

• Address the challenge of optimizing EV charging station operations amid three major uncertainties: wholesale electricity price volatility, intermittent renewable generation, and variable EV charging demand.
• Balance conflicting objectives: maximizing provider profit, enhancing customer satisfaction, and minimizing grid stress from EV charging.
• Develop a decision framework that enables charging service providers to set optimal dynamic prices and electricity procurement strategies across multiple time horizons.
• Investigate the impact of energy storage cost and customer satisfaction weighting on pricing, procurement, and profitability.
• Evaluate the effectiveness of dynamic programming versus greedy algorithms in managing complex, uncertain energy and demand dynamics.

Proposed method

• Model EV charging demand at each station using linear regression based on time-of-day and spatial patterns.
• Define a composite utility function that combines profit, customer satisfaction (weighted by parameter β), and grid impact (Qk), with a customer satisfaction function parameterized by shape and weighting parameters.
• Formulate the optimization problem over a finite planning horizon N, where decisions include charging prices (pkj), electricity procurement (ok), and energy storage usage (Ik, uk).
• Apply dynamic programming (DP) to solve the multi-stage optimization problem, recursively maximizing the total utility from each horizon to the end.
• Incorporate renewable energy generation (uk) and electricity storage (Ik) as controllable energy sources, with storage cost modeled as η per unit.
• Use a reference electricity purchase (o_ref) and a penalty term (μ) to minimize fluctuations in procurement, enhancing grid stability.

Figure 1: The EV Charging Market

Experimental results

Research questions

• RQ1 How does dynamic programming improve profit compared to a greedy algorithm in EV charging station energy management under uncertainty?
• RQ2 To what extent does low-cost energy storage reduce price volatility and improve profitability in dynamic pricing strategies?
• RQ3 How do varying customer satisfaction weighting parameters (β) affect the trade-off between profit and customer retention?
• RQ4 What is the impact of electricity storage cost (η) on procurement behavior and the adoption of 'buy low, sell high' strategies?
• RQ5 How does the integration of renewable energy and storage affect the stability of charging prices and grid impact?

Key findings

• The dynamic programming (DP) algorithm achieves up to 9% higher profit than the greedy algorithm under tested scenarios, demonstrating superior optimization performance.
• As the customer satisfaction weighting parameter β increases from 0 to 30,000, customer satisfaction improves significantly, but total profit decreases substantially, indicating a clear trade-off between service quality and profitability.
• When energy storage cost (η) is low (e.g., η = 0.5), the service provider adopts an aggressive procurement strategy—buying more during low-price periods (3:00–8:00) and less during high-price periods (11:00–19:00).
• At high storage cost (η = 1.5), electricity procurement remains nearly constant, indicating a conservative strategy due to prohibitive storage costs that prevent profitable arbitrage.
• Low-cost energy storage stabilizes charging prices by acting as a buffer: prices with low storage cost (η = 0.5) are less volatile than those with high storage cost (η = 1.5), especially during high wholesale price periods.
• The integration of low-cost energy storage not only increases profit but also reduces exposure of end customers to wholesale market fluctuations, enhancing price stability and customer experience.

Figure 2: Customer Satisfaction Functions ( $E=200$ )