Optimizing Vehicle-Grid Interaction Pricing to Reduce Community Load Fluctuations Considering Travel Behavior and Battery Degradation

Vehicle–Grid Interaction Pricing Optimization Considering Travel Probability and Battery Degradation to Minimize Community Peak–Valley Load

Abstract: Vehicle-to-Grid (V2G) technology has gained significant traction in recent years. Under time-of-use pricing, users independently determine their charging and discharging behaviors to maximize economic benefits—charging during low-price periods, discharging during high-price periods, and minimizing battery degradation. However, inappropriate electricity pricing can lead to outcomes that deviate from the grid’s goal of minimizing peak-to-valley load differences. This study establishes a user optimal decision model influenced by battery degradation and electricity costs, considering factors such as depth of discharge, charging rate, and charging energy loss, based on electricity data from a community in Beijing and electric vehicle (EV) travel behavior simulated through Monte Carlo methods. A mixed-integer linear programming algorithm is constructed to minimize the costs for EV users while analyzing grid load fluctuations under various pricing strategies. The study derives formulations and adjustment rules for optimal electricity pricing, achieving significant load stabilization. With 30% V2G participation, grid load fluctuations were reduced from 31.81% to 5.19%. This research addresses the challenge of determining optimal electricity prices to encourage user participation in V2G and minimize peak-to-valley load fluctuations.

Keywords: vehicle to grid; optimal electricity price; minimal load fluctuation; battery degradation; travel behavior

1. Introduction

Fluctuations in grid load, particularly in relation to renewable energy, present significant challenges to the stability of power systems. With the rapid adoption of electric passenger vehicles (EVs), buses, and trucks, the demand for vehicle charging has surged. Different charging modes can lead to varying levels of peak load growth, complicating the operation of the power grid. Additionally, the large-scale integration of renewable energy can cause grid instability due to dynamic fluctuations in generation and load.

As smart grid technologies advance, both unidirectional orderly charging (V1G) and Vehicle-to-Grid (V2G) technologies have become increasingly critical for reducing grid peak-to-valley load fluctuations. V1G optimizes EVs’ charging time and power while meeting their demand, and it is already compatible with commercial electric vehicle supply equipment (EVSE). Conversely, V2G allows EVs to charge and then feed excess electricity back to the grid during non-travel periods, effectively achieving peak shaving and valley filling.

In prior research, we quantitatively compared the potential of V1G and V2G in reducing grid load fluctuations, finding that V2G has a clear advantage in peak shaving, particularly evident at 40% participation.

Under time-of-use (TOU) pricing, users can independently decide when to charge or discharge their EVs, taking advantage of low electricity prices for charging and high prices for discharging. However, inappropriate pricing can exacerbate grid load fluctuations, and increased V2G participation may reverse expected load patterns. Moreover, the batteries of EVs face degradation through frequent charging and discharging, meaning that the high-frequency energy exchanges facilitated by V2G can accelerate battery wear.

This research aims to optimize electricity pricing to encourage user participation in V2G while minimizing peak-to-valley load fluctuations. The study will also examine the implications of battery degradation and energy loss costs in this context.

2. Proposed Methodology and Optimization Algorithm

The analysis framework involves multiple inputs: Monte Carlo simulation results of EV random charging, a battery degradation and energy loss model, and a predefined electricity pricing scheme. The optimization objective is to maximize the economic profits of users participating in V2G after accounting for various costs.

2.1. Simulation of Random Charging and EV Travel Characteristics

Travel characteristics of EVs were obtained from the Beijing Transport Development Annual Report. These characteristics were used to estimate charging load through Monte Carlo simulations, ensuring that simulated data reflects realistic EV usage patterns.

2.2. Battery Degradation and Energy Loss Model

The main factors affecting battery lifespan include depth of discharge (DoD) and charge/discharge rate. This study develops a multi-parameter model to quantify costs during V2G charging and discharging processes, incorporating degradation costs from both DoD and charge/discharge rate, alongside energy losses due to battery resistance.

2.3. Electricity Pricing Formulation and Adjustment Strategy

Electricity pricing is typically positively correlated with grid load levels. The proposed pricing strategy encourages users to charge during low load periods and discharge during high load periods, thereby stabilizing grid load. The adjustment of electricity prices is based on fluctuations in grid load, guiding users toward optimal charging and discharging decisions.

3. Case Study and Results Analysis

The analysis explores optimal electricity prices and their effects on load management. Various pricing strategies were tested, and findings illustrate that under 30% V2G participation, the grid’s relative fluctuation decreased significantly.

4. Discussion

The study highlights the relationship between battery lifespan improvement and associated costs. As battery lifespan extends, the degradation costs during charging and discharging processes are expected to decrease, positively affecting V2G participation and grid stability.

5. Conclusions

This research presents a comprehensive model for quantifying costs related to EV battery degradation and energy loss. The proposed dynamic electricity pricing strategy effectively stabilizes grid load while aligning with user economic interests. The findings demonstrate the importance of considering battery health and user behavior in formulating optimal pricing strategies for V2G participation.

Overall, this study provides valuable insights for optimizing vehicle-grid interactions to enhance grid stability and promote sustainable energy practices.