Source-linked AI summary
Exploiting the Benefits of V2B Application on Peak Shaving of Data Center Loads
Arya Joshi, Hamed Haggi, Chinmay Morankar
TL;DR
Rapid AI-driven data center growth is straining power grids and increasing emissions, motivating the search for ways to use existing energy assets more efficiently. This paper develops and evaluates a V2B framework for shaving data center cooling loads through literature-informed modeling and simulation. Strategic V2B dispatch between 12–5 pm offsets approximately 13–36% of peak or gross cooling demand across seasonal profiles.
Problem
AI-driven data center growth is straining power grids and increasing emissions, while V2B feasibility and techno-economic operational constraints in data center applications remain largely unaddressed.
Method
The paper combines a V2B framework, literature review, temperature-dependent load simulation, and techno-economic analysis using a corporate EV fleet for data center cooling-load management.
Results
13–36% of peak cooling demand can be offset through strategic V2B dispatch across simulated summer and winter profiles.
Takeaways & Limitations
Strategic deployment of corporate EVs can support peak cooling-load shaving in data center applications, with the relative effect varying by seasonal cooling demand.
Abstract
from arXiv · showhide
The accelerated growth in data center projects has introduced a demand-driven bottleneck throughout power grids and contributed to a substantial increase in carbon emissions. These concerns are fueling discussions on methods to use existing energy assets to drive operational efficiency. To this end, this paper explores the benefits of Vehicle-to-Building (V2B) applications to support peak shaving of data center cooling loads. Initially, a literature review was conducted considering V2B constraints and optimization methods including SoC limitations, EV participation, tariffs, and building loads. This analysis was then used to develop a conceptual case study of a 10 MW data center in Loudoun County, VA by simulating a temperature-dependent load profile and adjusting the V2B participation of 40 commercial and passenger EVs. Simulation results indicate that, depending on seasonal variations in cooling load demands, strategic deployment of V2B assets between 12-5pm can offset gross cooling loads by 13-36%.
I. INTRODUCTION
Rapid growth in AI-driven data center demand is straining power grids, increasing emissions, and creating reliability and cost concerns. The paper proposes examining V2B to shave data center cooling peaks while addressing previously unaddressed feasibility and techno-economic constraints.
- Loudoun County, VA, has 200 operational data centers and 117 more in development, prompting utilities to delay fossil-fuel generation retirements.
- V2B uses EVs and bidirectional chargers to deliver controlled power to buildings and offset peak loads without requiring expensive battery storage systems.
- V2B peak shaving can reduce grid reliance, peak charge costs, and demand charges, while supporting grid reliability and renewable energy penetration.
- The paper proposes a V2B peak-shaving framework supported by real-world simulation and techno-economic operational analysis for data center applications.
II. V2B FRAMEWORK AND LITERATURE REVIEW
Prior V2B research emphasizes SoC, EV availability, fleet size, tariffs, incentives, and temperature-dependent loads as determinants of feasibility and performance. Reported studies show economic, renewable-energy, and peak-load benefits, but also highlight the importance of realistic participation and seasonal demand modeling.
- SoC constraints and EV participation determine the dispatchable energy capacity available for V2B operations.
- A five-EV model supporting a 70-kW data center achieved a 10% energy cost reduction but did not model dynamic EV participation.
- 4–9% daily savings were reported using a Monte Carlo arrival model that represented EV availability more realistically.
- 8–29 EVs in summer and 2–15 in winter were estimated to support a 30-kW peak load, reflecting higher summer cooling demand.
- Effective V2B feasibility and optimization analyses should incorporate fleet size, EV participation, SoC constraints, tariffs, and temperature-dependent load profiles.
- Tariffs and availability incentives can offset battery degradation costs, while V2B has been associated with 45.1% higher renewable penetration and up to 38.9% lower LCOE.
III. SIMULATION RESULTS AND NUMERICAL ANALYSIS
The simulation models temperature-dependent cooling loads and EV participation for a 10 MW data center, then evaluates how V2B dispatch changes winter and summer net loads. V2B effects are concentrated in the afternoon and vary with seasonal cooling demand.
- Case study and load model: A 10 MW data center in Loudoun County, VA was evaluated using average winter and summer day profiles from TMY data.The case study used January 1 and August 1 profiles.
- Case study and load model: Cooling loads were modeled with a cosine baseline and adjusted hourly using Loudoun County temperature data.The model used a 20 C base temperature and a cooling variation value of 0.03 kW/C.
- Simulation results: V2B did not offset cooling loads until 9 am, when passenger EV participation began increasing.The hourly net load profile incorporated EV mix, participation schedules, and SoC constraints.
- Simulation results: 13-36%: strategic V2B deployment reduced daily gross loads by approximately this range, with the relative effect varying by season.Load offset was more pronounced in winter, while higher summer cooling demand reduced V2B’s relative contribution.
IV. CONCLUSIONS AND FUTURE WORKS
Rapid AI-driven data center growth is straining power grids and increasing emissions, motivating V2B deployment using EV fleets to offset cooling loads. Simulations indicate 13–36% peak cooling-demand offsets, while tariff structures and EV incentives remain future-work considerations.
- Rapid AI-driven data center growth is straining power grids and increasing emissions.
- The paper proposes deploying Ford F-150 Lightnings and Tesla Model 3s in a corporate EV fleet for V2B data-center cooling-load offsets.
- Simulated summer and winter profiles assess strategic V2B dispatch for offsetting data-center cooling loads.
- 13–36% of peak cooling demand can be offset through strategic V2B dispatch.
- Future work will assess how tariff structures and EV incentives affect energy cost savings for data-center applications.