Quantifying the Peak-Shaving
Potential and Cost Efficiency
of BEV Supplier-Managed Charging
Integrating consumer enrollment
with grid simulation in CAISO and NYISO

Pingfan Hu
George Washington University
with Brian Tarroja, Matthew Dean, Kate Forrest,
Eric Hittinger, Alan Jenn, and John Paul Helveston

Pingfan Hu

Education

PhD Candidate in Systems Engineering, advised by Dr. John Helveston, George Washington University, USA

M.P.E in Mechanical Engineering, University of Sydney, Australia

B.S. in Industrial Engineering, Pennsylvania State University, USA

Work Experience

Assistant Process Engineer, Kurtz Ersa, Germany

Project Engineer, Daimler Beijing, China

Project Engineer, Hasco Rail, USA

Before starting my PhD, I had long been an engineer: my degrees and my work at Kurtz Ersa, Daimler, and Hasco Rail were all in engineering, and much of that work was in transportation. So I came to this research as a technical person with a long-standing connection to transportation.

Research Interests

Data Science in transportation systems and human behavior

Open-Source Software in tools for survey research

Agentic AI Engineering around Claude Code

pingfan.org

EV sales in US reaching ~10% of sales

Source: Argonne National Lab, www.anl.gov/ev-facts/model-sales

BEVs are now about one in ten new cars sold in the US. Millions of them come home in the evening and plug in at the same time, right on top of the existing grid peak. That is the problem this talk is about.

Introduction

• Unmanaged BEV charging is becoming a problem to the grid.
• Managed charging is cheaper and smooths out the grid load.

Unmanaged charging adds a spike to the evening peak. Managed charging smooths that load and keeps costs down. Supplier-Managed Charging, or SMC, is a type of managed charging where the utility controls charging timing. This talk focuses on SMC.

SMC - Supplier Managed Charging

• SMC smooths out the BEV charging spike (called “Peak-shaving”).
• Electricity demand is controlled below the capacity threshold.
• It saves money and reduces pollution.

With unmanaged charging, the BEV load stacks on the evening peak and easily exceeds the capacity threshold. With SMC, the utility defers charging into the overnight valley, so total demand stays below the threshold. This is peak shaving. It saves money and reduces emissions from peaking generators.

Smart charging effectiveness depends on
enrollment and peak-shaving.

Prior stage

Enrollment

Will BEV owners opt in,
and at what incentive?

Current stage

Peak-shaving

How much peak can SMC shave,
and at what cost?

Two things decide whether SMC works at scale. First, enrollment: will BEV owners actually opt in, and what incentive does it take? That was our prior stage, a nationwide survey of BEV owners. Second, peak shaving: once people enroll, how much peak can SMC shave, and what does it cost the utility? That is the current stage. I will give you the prior stage in one slide, then spend the rest of the talk on the simulation.

Prior stage: willingness to enroll in SMC

Conjoint survey of BEV owners (Hu et al., 2025)

1,356

BEV owner responses

803 + 553

Meta Ads + Dynata, 2024

5 attributes

cash, overrides, battery thresholds

The prior stage, published in Environmental Research Letters last year, is a conjoint survey of 1,356 real BEV owners, recruited through Meta ads and Dynata in 2024. Each respondent saw six choice questions like this one: two SMC programs that differ in enrollment cash, monthly cash, override allowance, and battery thresholds, plus a “not interested” option. Choosing between them reveals how much each feature is worth to them.

Existing studies assume enrollment is high and free

1. Most grid studies assume optimal or near-100% participation (Coignard et al., 2018; Tarroja et al., 2015), but real pilots enroll far fewer.
2. Enrollment is not free: utilities must pay incentives (Bailey et al., 2023; Wong et al., 2023), yet costs are rarely counted.
3. Few studies compare peak-shaving across grid regions with different load shapes.

We connect consumer enrollment with grid simulation.

The technical potential of managed charging is well documented, but most studies assume everyone participates for free. In reality, participation depends on incentives, and incentives cost money. And few studies ask whether the answer changes with the shape of the grid. That is the gap: we bring the empirical enrollment curve into a grid-scale simulation of two contrasting regions.

Research Questions

1. Peak-shaving: How much can SMC reduce peak demand in contrasting grid regions?

2. Cost efficiency: How much does peak shaving cost per unit, and is more enrollment always better?

Approach

---

1. Scenario-based simulation for CAISO and NYISO.
2. Enrollment curve from the prior stage: incentives vs participation.

Two research questions. First, the physical side: how much peak demand can SMC actually shave, and does the answer depend on the region? Second, the economic side: what does that peak shaving cost, and is pushing enrollment higher always worth it?

To answer them, we run a scenario-based simulation of CAISO and NYISO, and join it with the enrollment curve from the prior stage, which links incentive levels to participation rates.

Two contrasting regions: CAISO and NYISO

	Single families	Energy source	Net load shape	Seasonal effect
CAISO	7.69 million	Solar	"Duck curve"	Minimal
NYISO	3.16 million	Hydro	Flat profile	Strong in winter

Both lead in BEV adoption, with contrasting grid structures.

The US grid divides into 18 regions. We picked California and New York because they are both BEV leaders but structurally opposite grids. CAISO has the solar duck curve with a steep evening ramp, and it behaves essentially the same across all four seasons. NYISO is flatter, relies more on dispatchable hydro, and is season-sensitive: winter overnight heating keeps the nighttime load elevated, which matters later in the results. This contrast lets us see how grid context shapes SMC performance.

Simulation Overview

The framework has two parts. The first is the peak-shaving model: three data sources feed a simulation that sweeps TOU window configurations, adoption rates, date ranges, and enrollment levels from 0 to 100 percent. The second takes the best-performing outcomes and joins them with the consumer enrollment curve to compute cost efficiency frontiers. Let me walk through the key steps.

Step 1: BEV Charging Load Profile

Simulation of 10,000 vehicles based on ~26,000 trip records from 2022 NHTS.

Step one builds the unmanaged BEV charging load from about 26,000 travel records in the 2022 NHTS. We simulate 10,000 vehicles with 200-mile range on Level 2 home charging, keeping batteries between 20 and 80 percent. People come home in the evening and plug in, so the load peaks at 7 PM, exactly on top of the existing grid evening peak. All plots start at 7 AM so the evening peak sits in the center and the overnight valley forms one block on the right.

Step 2: BEV Load Peak Shaving

Five configurations with different peak/valley windows.

Step two shifts that load. From 10 AM, charging is suppressed unless the battery would fall below 20 percent or tomorrow’s travel would be at risk; deferred demand is delivered at maximum rate once the valley window opens. Sessions that cannot be shifted stay in place as override events, which stay within 2 to 3 per vehicle per month. The five configurations differ only in when the valley opens, from 8 PM to midnight. This is drawn at 50 percent enrollment so you can see half of the fleet shifted and half charging as usual. The appendix compares the five windows: the 11 PM valley start performs best, so all results use it.

Step 3: Net Load + BEV & Peak Shaving

BEV load scaled to fleet size and stacked on net load.

Step three scales the BEV load to the fleet and stacks it on the grid net load, the shaded area. We assume one BEV per detached single-family household, so each simulated vehicle represents 769 vehicles in CAISO and 316 in NYISO. The black line is total load with no SMC; the green line is full enrollment: the evening peak drops and the load moves into the overnight valley.

Enrollment Curve from Prior Stage

Mixed logit prediction based on 1,356 real BEV owners.

This is the bridge between the two studies. The mixed logit model, holding non-monetary attributes fixed, gives the enrollment rate as a function of the monthly incentive. About 30 percent would participate with no payment at all, 20 dollars a month gets you to about 60 percent, but full enrollment would take 85 dollars per household per month. The curve is concave, and since utilities pay a uniform incentive to everyone, total program cost grows non-linearly with enrollment.

Peak Shaving: Annual Average

CAISO		NYISO
Peak shave	3.7 GW (30.5 → 26.8)	Peak shave	1.6 GW (21.2 → 19.6)
Percentage	12.1%	Percentage	7.5%

Now the results. On an annual average day with full enrollment, SMC shaves 3.7 gigawatts off CAISO’s evening peak, about 12 percent, and 1.6 gigawatts in NYISO, about 7.5 percent. CAISO does better both because it has more households and because its deep overnight valley absorbs the deferred charging.

Peak Shaving: CAISO Extreme Days

Best Day (Jun 1)		Worst Day (Dec 13)
Peak shave	5.4 GW (37.6 → 32.2)	Peak shave	1.9 GW (30.7 → 28.8)
Percentage	14.4%	Percentage	6.2%

Performance varies a lot day to day. CAISO’s best day shaves 5.4 gigawatts, its worst under 2. One nuance: extreme days are ranked by how much peak can be shaved, not by how high the peak is. On heat-wave days, air conditioning dominates and BEV charging is a small share, so SMC has less leverage.

Peak Shaving: NYISO Extreme Days

Best Day (Jul 13)		Worst Day (Nov 8)
Peak shave	2.2 GW (27.8 → 25.6)	Peak shave	0.8 GW (17.8 → 17.0)
Percentage	7.9%	Percentage	4.5%

NYISO’s range is narrower: the best day shaves 2.2 gigawatts, the worst 0.8. Smaller absolute numbers because there are fewer households, and smaller percentages because the net load varies less over the day.

Peak from BEV charging decreases with higher enrollment

Monthly incentive per household versus achieved peak reduction on the annual average day. Each dot is a 10 percent enrollment step. Peak reduction grows with enrollment in both regions, but the dots spread out along the incentive axis at higher enrollment. Joining this with the enrollment curve gives the cost efficiency frontier on the next slide.

Cost Efficiency Frontier

Cost efficiency is highest at moderate enrollment (30–50%).

Here is the central result. We join the enrollment curve with the peak-shaving results. Each curve is one day type: least, average, and most peak shaving. Each dot is a 10 percent enrollment step, from 30 percent at zero incentive to 100 percent. The gray arrow shows the monthly incentive per household behind each dot, from 0 to 85 dollars. The dots stretch out along the cost axis: attracting marginal participants means raising the payment for every existing enrollee, so the last increments of enrollment are extremely expensive. Moderate enrollment, 30 to 50 percent, captures most of the achievable peak reduction at a fraction of the cost. And on about 15 percent of CAISO days, pushing enrollment too high actually creates a new overnight peak.

Seasonal cost efficiency is
consistent in CAISO, but varies in NYISO.

By season, CAISO’s four curves nearly overlap, so a year-round program delivers consistent value: the solar-shaped profile keeps the peak-to-valley gap wide in every season. NYISO’s winter curve sits below the others at every enrollment level, because overnight space heating keeps the valley elevated and leaves less room for deferred charging. Utilities there may need seasonal enrollment targets or incentive levels.

Most program cost is participant surplus

72–92% of uniform payments exceed what enrolled households would accept.

One more angle on cost. The program pays the same incentive to every enrolled household, but the enrollment curve tells us each household’s minimum: roughly a third would join for nothing. The dashed line is what the program would pay if each household got exactly its minimum. The gap is participant surplus, and it is 72 to 92 percent of program cost. At 60 percent enrollment in CAISO, the program pays 92 million a month, while the enrolled households would accept 21 million. No program can pay the minimum, since each household’s number is private, but it bounds what tiered menus or enrollment auctions could save.

Conclusions

1. Peak-shaving potential is: up to 14.4% (5.4 GW) for CAISO, and 7.9% (2.2 GW) for NYISO.
2. The cost-effectiveness of peak shaving requires: moderate enrollment (30–50%), and seasonal effect consideration.
3. Most program cost is participant surplus (72–92%): better-targeted incentives could deliver the same shaving for less.
Limitations	a. Stated preference might be optimistic. b. Only considered single-family homes. c. Grid conditions based on 2025 data.

Three takeaways. First, the magnitude: SMC shaves up to 14.4 percent off CAISO’s evening peak and 7.9 percent off NYISO’s. CAISO does better because its deep overnight valley absorbs the deferred charging.

Second, cost. Returns to enrollment diminish sharply: 30 to 50 percent participation captures most of the achievable reduction at a fraction of the cost, and pushing too high can create a new overnight peak. Utilities should design SMC programs around moderate enrollment targets, not maximum participation, and in NYISO account for weaker winter performance.

Third, most of what a uniform incentive pays is participant surplus, so better-targeted designs could lower the cost of the same peak shaving.

Beyond these, SMC has co-benefits we did not monetize: lower marginal emissions, longer battery life, and deferred capacity investment, so the frontiers are a lower bound on its value. And as grid storage grows, SMC stays a complement: storage reshapes the baseline, SMC removes the peak that BEV charging adds on top.

Limitations: the enrollment curve is stated preference, so it may overstate real uptake and our costs may be a lower bound. We model single-family homes only, which matters for New York City. And results are calibrated to 2025 net load.

Acknowledgments

Co-authors

Brian Tarroja, Matthew Dean, Kate Forrest (UC Irvine) · Eric Hittinger (RIT) · Alan Jenn (UC Davis) · John Paul Helveston (GWU)

Funding

Alfred P. Sloan Foundation (grant G-2023-20976) · Helveston Lab, George Washington University

Utility partners

Southern California Edison · Pacific Gas & Electric · National Grid

Data & code

github.com/pingfan-hu/2025-grid-peak-shaving-public

This work is a collaboration across GWU, UC Irvine, RIT, and UC Davis, supported by the Alfred P. Sloan Foundation. Thanks to Southern California Edison, PG&E, and National Grid for their insights on utility program perspectives. All data, code, and simulation outputs are public on GitHub.

Thanks for listening!

Pingfan Hu

George Washington University

pingfan.org

About

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AppendixSupporting Details

Data Sources

Source	Provides	Role in Simulation
2022 NHTS	Real trip records nationwide	Charge time & energy needs
2025 GridStatus.io	Net load time series	Regional grid baselines
2025 U.S. Census	Single-family counts	Scale to total fleet energy
Prior stage	Enrollment curve	Cost efficiency analysis

Four data sources: NHTS trips define when vehicles travel and how much energy they need; GridStatus.io gives us actual 2025 net load for both regions; the Census scales our 10,000 simulated vehicles up to regional fleet sizes; and the discrete choice experiment from the prior stage translates incentive dollars into enrollment rates. Net load means total consumption minus variable renewables, so it is the demand that dispatchable generation must serve.

Vehicle & Charging Assumptions

BEV Range

200 miles

Charging

Level 2 (6.48 kW)

Battery SOC

20% – 80%

Travel Needs

Guaranteed

Overrides

2 – 3 per month

Key assumptions: 200-mile BEVs (48.4 kWh, 0.242 kWh per mile) on Level 2 home charging at 7.2 kW with 90 percent efficiency, batteries kept between 20 and 80 percent, and travel is never compromised: sessions that cannot be shifted stay in place as override events, which stay within 2 to 3 per vehicle per month.

TOU window: 11 PM valley start performs best

Cost efficiency frontiers for the five TOU configurations in CAISO on the annual average day. S04, with the valley window starting at 11 PM, delivers the most peak shaving at every cost level, so all reported results use it. An 8 PM start is clearly worst: it opens the valley while the grid is still near its peak.

Seasonal Patterns: CAISO

Spring	Summer	Fall	Winter
3.7 GW (27.5 → 23.8)	3.7 GW (34.3 → 30.6)	3.8 GW (32.3 → 28.5)	3.6 GW (29.1 → 25.5)
13.5%	10.8%	11.8%	12.4%
Peak shave for all seasons: 3.6–3.8 GW (10.8–13.5%)

Seasonally, CAISO is remarkably consistent: 3.6 to 3.8 gigawatts in every season, because the solar-shaped profile keeps the peak-to-valley gap wide year-round. Summer has the highest absolute peak from air conditioning, but the shaving potential barely moves.

Seasonal Patterns: NYISO

Spring	Summer	Fall	Winter
1.5 GW (18.4 → 16.9)	1.6 GW (24.6 → 23.0)	1.5 GW (19.8 → 18.3)	1.4 GW (22.1 → 20.7)
8.2%	6.5%	7.6%	6.3%
Peak shave for all seasons: 1.4–1.6 GW (6.3–8.2%)

NYISO’s winter is the exception: overnight space heating keeps the valley elevated, leaving less room for deferred charging and giving the smallest seasonal shaving at 1.4 gigawatts. The other three seasons cluster at 1.5 to 1.6.

Cost efficiency across BEV adoption rates

The 1:1 BEV-to-household ratio is a near upper bound of adoption. At 1:2 and 1:4, the frontiers keep the same shape with proportionally smaller peak shaving and cost, so the moderate-enrollment conclusion holds at lower adoption as well.

How Do Pilot Programs Compare?

Program	Incentive	Enrollment
PCE Managed Charging (CAISO)	$0 → $40 per month	1.4% → 10.6%
MCE Sync (CAISO)	$50 bonus + $10 per month	10% of serviceable market
SmartCharge New York (NYISO)	Up to $400 per year	~7% of registered EVs (731 vehicles)

• Smart charging programs motivated EV owners to charge off-peak.
• More incentive results in more enrollment.
• Stated preference surveys are not calibrated against current pilot programs.

Three deployed pilot programs: two in CAISO territory, one in NYISO (Energy and Resource Solutions, 2019; ev.energy, 2024; Peninsula Clean Energy, 2025).

First, they motivated EV owners to charge off-peak: MCE Sync cut charging in the 4-to-9 PM window by nearly 90 percent.

Second, more incentive means more enrollment. PCE’s randomized pilot shows this directly: 1.4 percent with no payment, rising to 10.6 percent at 40 dollars a month.

Third, real enrollment sits around 4 to 10 percent, below our 30 to 50 percent target. Stated preference overstates real behavior, and these pilots are small and short, so they carry their own biases. The survey gives an optimistic upper bound, but the field data confirms the direction the model predicts.

Reference List

Bailey, M. R., Brown, D. P., Shaffer, B. C., & Wolak, F. A. (2023). Show Me the Money! Incentives and Nudges to Shift Electric Vehicle Charge Timing [Working Paper]. National Bureau of Economic Research. https://doi.org/10.3386/w31630

Coignard, J., Saxena, S., Greenblatt, J., & Wang, D. (2018). Clean vehicles as an enabler for a clean electricity grid. Environmental Research Letters, 13(5), 054031. https://doi.org/10.1088/1748-9326/aabe97

Energy and Resource Solutions. (2019). SmartCharge Electric Vehicle Program Impact Evaluation Final Report. Energy and Resource Solutions. https://documents.dps.ny.gov/public/Common/ViewDoc.aspx?DocRefId={BF5BB327-2EF4-4A4C-9692-8DA7C78D707C}

ev.energy. (2024). How MCE achieved 90% peak load reduction with a managed EV charging program. https://www.ev.energy/en-us/case-study/mce-sync

Hu, P., Tarroja, B., Dean, M., Forrest, K., Hittinger, E., Jenn, A., & Helveston, J. P. (2025). Measuring electric vehicle owners’ willingness to participate in smart charging programs. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ae2597

Peninsula Clean Energy. (2025). EV managed charging pilot: Final report. Peninsula Clean Energy. https://www.peninsulacleanenergy.com/wp-content/uploads/2025/03/PCE-EV-Mgd-Charging-Final-Report.pdf

Tarroja, B., Shaffer, B., & Samuelsen, S. (2015). The importance of grid integration for achievable greenhouse gas emissions reductions from alternative vehicle technologies. Energy, 87, 504–519. https://doi.org/10.1016/j.energy.2015.05.012

Wong, S. D., Shaheen, S. A., Martin, E., & Uyeki, R. (2023). Do incentives make a difference? Understanding smart charging program adoption for electric vehicles. Transportation Research Part C: Emerging Technologies, 151, 104123. https://doi.org/10.1016/j.trc.2023.104123