Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports

Abstract

As the airline industry seeks to reduce costs and transition to clean energy, electric ground support equipment is emerging as a favorable option. The integration of electric ground support equipment into airport operations requires careful planning for vehicle deployment, charging infrastructure, and grid impacts. We develop a flexible, bottom-up modeling framework to assess energy and infrastructure needs across more than 300 U.S. airports. Our analysis estimates site-specific power and energy demand, equipment counts by type, charger requirements, and costs. Here we quantify the magnitude of new electrical loads created by the electrification of airport ground support equipment, finding that peak power demand at the largest airports can reach up to 20 megawatts, with annual electricity consumption approaching 51,000 megawatt-hours. We further show that behind-the-meter battery energy storage systems and solar photovoltaic systems can reduce peak load and lower total system costs by as much as 10 million dollars.

He et al. model electrification of airport ground support equipment across more than 300 U.S. airports, estimating energy consumption, peak power demand, charging infrastructure requirements, and potential cost savings from battery storage and solar integration.

Introduction

The aviation section accounts for approximately 7–8% of global oil consumption and about 2–3% of global energy-related CO₂ emissions^1,2. While most emissions arise from aircraft, airport operations account for around 2–10% of total aviation emissions and are concentrated near population centers^3–7. These localized emissions significantly affect air quality and public health, highlighting airports as a key focus for targeted emissions reduction strategies. Recognizing the critical need to improve energy efficiency at airports, the U.S. Department of Energy (DOE) launched the Athena (Advanced Transportation Hub Efficiency Using Novel Analysis) Project to integrate and advance transformative technologies that support ambitious energy and climate objectives⁸. As part of the Athena Project, this study dives deep into one of the most critical yet understudied components of airport operations: ground support equipment (GSE).

Airports rely on GSE to provide critical services to aircraft at arrival and departure gates and ensure smooth and efficient operations. GSE encompasses a variety of equipment used on the tarmac to support aircraft between flights, including aircraft tractors, baggage tractors, ground power units (GPUs), belt loaders, and lavatory trucks. Upon aircraft arrival, these GSE vehicles assist with various essential tasks such as towing airplanes or luggage/freight carts, loading/unloading luggage/freight, providing power to the aircraft, loading potable water, removing sewage, loading food, de-icing airplanes, and firefighting⁹.

The growing demand for air travel in recent decades has intensified ground support operations at airports, leading to a significant increase in surface-level emissions. It is reported that GSE contributes to a large portion of the airport infrastructure emissions^10–12, an IEA-ETSAP (International Energy Agency – Energy Technology Systems Analysis Programme) life-cycle assessment study estimates that more than 50% of airport infrastructure emissions come from GSE operations¹³. Additionally, GSE are known to contribute significantly to NO_x pollution and fine particulate matter (PM_2.5), accounting for 13% of total NO_x emissions at airports across the United States¹⁴. The Airport Cooperative Research Program (ACRP) Report 78 indicates that nearly 40% of the nationwide GSE fleet uses spark-ignition engines and about 35% uses compression-ignition engines¹⁵, both of which rely on fossil fuels and produce exhaust emissions. Given that many airports are situated in or near metropolitan areas¹⁶, they are increasingly required to comply with stringent environmental regulations¹⁷. Driven by environmental pressures, airports and airlines in the United States are beginning to transition from diesel or gasoline GSE to cleaner and more advanced alternatives, with battery-powered electric GSE (eGSE) options emerging as the most widely adopted solution. Airlines have played a leading role in this transition, with several having partially or fully electrified their GSE fleets and/or set ambitious targets to achieve net-zero GSE operations in the coming years^18–21.

GSE vehicles are particularly well-suited for electrification due to the need for low-end torque, frequent start and stop cycles, extended idle periods, and short operational ranges. In addition to the benefit of zero tailpipe emissions, eGSE vehicles are easier to maintain, more reliable, quieter, and have lower operational costs than conventional GSE²². One study showed that an electric baggage tractor can be operated for under $9 per day, whereas the average daily operating cost of an internal combustion baggage tractor is $20 or more²³. The airports that have adopted eGSE are already reaping the benefits. For instance, Seattle-Tacoma International Airport’s implementation of eGSE has resulted in savings of approximately 10,000 metric tons of greenhouse gas emissions and around $2.8 million in fuel costs annually. In another example, Amsterdam Schiphol achieved a 90% reduction in CO₂ emissions by investing in electric GPUs to supply power to parked aircraft²⁴.

Given the multiple benefits and technological advancements in electric vehicles and charging infrastructure^25–27, eGSE has gained considerable traction in recent years. Relevant stakeholders, including airport operators, ground handling companies, utilities, original equipment manufacturers and policymakers, are exploring opportunities for GSE fleet electrification. However, planning and operating eGSE to optimize economic and energy benefits is still in its early stages. Key aspects such as the impact of GSE electrification on fleet operations, the extra energy and power demand from the distribution grid, and the assessment of charging infrastructure and vehicle requirements remain largely unexplored. Previous GSE-related studies have focused on the operational management of traditional GSE, aiming to improve handling efficiency and reduce costs^28–30. A few studies on eGSE have concentrated on feasibility assessments and cost comparisons between eGSE and their diesel-powered counterparts^31–39. More recent research has begun to examine mixed fuel and electric fleet scheduling, vehicle-to-grid applications, and stakeholder perspectives on alternative zero-emission GSE technologies. These studies often address specific operational or technological aspects in isolation^40–42. To ensure smooth transition to eGSE and efficient operation of eGSE without causing flight delays or putting excessive strain on the power system, it is essential to understand infrastructure requirements, assess the operational and energy impacts of replacing conventional GSE with electric alternatives, and develop effective charging strategies. A recent study by National Laboratory of the Rockies (NLR) estimated the hourly power demand from eGSE for the 50 largest U.S. airports⁴³. The study employed a top-down methodology, beginning with county-level annual GSE fuel consumption estimates generated using the EPA’s MOVES (MOtor Vehicle Emission Simulator) model. This data was then allocated to individual airports, converted to electricity consumption, and used to estimate hourly charging loads based on a simple assumption that charging demand is inversely related to flight activity. This approach offers a high-level estimate of eGSE power demand but does not consider the variations driven by charging strategies or provide detailed modeling of GSE fleet composition, operational patterns, or charger requirements.

Here, we present a comprehensive bottom-up approach to quantifying both the energy impacts and the infrastructure requirements of transitioning from conventional GSE to eGSE at 317 major airports in the United States, based on flight data that includes arrival and departure times as well as aircraft classifications. This study focuses on eight major types of GSE that have commercially available electric counterparts: aircraft tractors, GPUs, baggage tractors, belt loaders, cargo loaders, catering trucks, lavatory trucks, and water trucks. We develop an agent-based simulation model to simulate eGSE operations, capturing both aircraft service and charging events for each GSE vehicle. We generate charging load profiles, identify the peak power demand, estimate the fleet requirements and charging infrastructure needs for each airport, and assess the trade-off between upfront investments and charging costs under various charging strategies. We further evaluate the potential energy and cost benefits of integrating behind-the-meter technologies (energy storage systems and photovoltaic (PV) panels) to support the energy demands of eGSE operations. A flowchart that summarizes the overall research workflow, including data inputs, modeling steps, and outputs, is presented in Fig. 1. Based on the analysis, we find that electrifying major GSE at airports could significantly increase power and energy demand on the grid, particularly at large airports where the added load could reach up to 20 megawatts (MW) and annual energy consumption reach up to 51,000 megawatt-hours (MWh). We also find that charging strategies have a substantial impact on both peak power demand and the required number of GSE units and chargers. The behind-the-meter storage (BTMS) and on-site generation analysis indicates that integrating stationary battery storage and PV systems can meaningfully reduce peak demand and charging costs, with the extent of these benefits varying by airport.

Fig. 1. Methodological workflow of the electric ground support equipment (eGSE) energy and infrastructure analysis.

This schematic illustrates the integrated workflow used to quantify the operational, energy, and infrastructure impacts of electrifying GSE at U.S. airports. Flight activity data and equipment specifications are used to assign GSE to aircraft and simulate service and charging events using an agent-based model. The framework estimates required fleet sizes and charger deployment, generates charging load profiles, and determines peak power demand and annual energy consumption. It further evaluates the potential of behind-the-meter storage (BTMS) and on-site photovoltaic generation to mitigate grid impacts and reduce costs under different charging strategies and charger power levels.

Results

Ground support equipment description and specifications

The eight types of GSE evaluated in this study (i.e., aircraft tractors, GPUs, baggage tractors, belt loaders, cargo loaders, catering trucks, lavatory trucks, and water trucks) are primary equipment used in airport operations, each playing a vital role in supporting efficient aircraft turnaround. Aircraft tractors are used to tow or push aircraft during ground movements such as pushback and repositioning at the gate, while GPUs supply electrical power to aircraft on the ground, allowing onboard systems to operate without using the aircraft’s engine or onboard power. Baggage tractors transport baggage carts between terminals and aircraft, and belt loaders transfer baggage and cargo between ground level and aircraft cargo holds. Cargo loaders are used primarily for wide-body aircraft to lift and position unit load devices during loading and unloading operations. Catering trucks deliver and load in-flight meals, beverages, and service equipment into aircraft galleys, lavatory trucks service aircraft waste systems by removing waste and replenishing fluids, and water trucks supply potable water for aircraft cabin and galley use.

Each time a GSE vehicle performs a service task, it consumes a certain amount of energy, and the power requirement, service duration, and power utilization rate that determine the energy consumption vary across GSE types because the tasks they perform are different. The energy consumption per task for each GSE type serves as the basis for estimating airport-level energy use and power demand, as well as for determining the state of charge (SOC) of electric GSE, which in turn informs the calculation of the charging need and number of vehicles required when transitioning to electric alternatives. Energy consumption per task also depends on vehicle specifications, which vary across manufacturers. For consistency and illustrative purposes, this study collected representative specifications for each GSE type and used them throughout the analysis for all airports. Information on GSE vehicles’ operating durations is presented in Supplementary Table 1. These durations are sourced from the TRB ACRP Report⁴⁴, which incorporates observations from a diverse range of U.S. airports and thus provides a more representative and up-to-date dataset compared to the default values used in the Aviation Environmental Design Tool (AEDT). In addition to gate-based operations, certain GSE types also perform off-gate activities such as traveling between terminals, baggage areas, and fuel facilities. Average travel distances and durations for these off-gate operations by airport size are detailed in Supplementary Table 2, as reported by ACRP⁴⁴. The specifications of GSE vehicles that were used for calculating task-related energy consumption and tracking vehicle SOC levels, are provided in Supplementary Table 3. These specifications were collected from GSE manufacturers websites^45–56. Due to various design and operational factors, most GSE vehicles operate at significantly less than their rated full power. This analysis assumed that eGSE operate with power utilization rates comparable to those of conventional engine-powered GSE and adopted the engine load factors (defined as the proportion of actual engine output power relative to its rated maximum power) provided by the ACRP report⁴⁴ to represent the power utilization rates for different GSE types, as detailed in Supplementary Table 4. The parameters listed in Supplementary Tables 1–4 jointly determine the energy consumption of GSE for each task. Battery size is another key parameter that, together with per-task energy consumption, determines the duty cycle of an electric GSE. Table 1 summarizes the per-task energy consumption (calculated from Supplementary Tables 1–4) and battery capacity for each GSE and aircraft type.

Table 1.

Per-task energy consumption and battery size of eGSE by aircraft type

GSE Fleet	Per-task energy consumption (kWh)		Battery size (kWh)
	Wide-body aircraft	Narrow-body aircraft	Wide-body aircraft	Narrow-body aircraft
Aircraft tractor	30.4	15.6	168.0	72.0
Baggage tractor	12.7	7.7	50.0	50.0
Belt loader	13.0	0.8	34.0	31.7
Catering truck	33.6	27.4	246.7	246.7
Cargo loader	15.2	N/A	90.0	N/A
Lavatory truck	6.8	2.3	107.0	40.0
Water truck	5.0	1.5	106.0	40.0
GPU	150.8	50.6	310.0	160.0

eGSE electric ground support equipment, GPU ground power unit, kWh kilowatt-hour.

Airport flight data analysis and ground support equipment charging scenarios

Given that GSE operations are closely aligned with flight schedules, realistic modeling of GSE operational events requires accurate flight activity data. To this end, we utilized the Bureau of Transportation Statistics (BTS) Airline On-Time data⁵⁷, along with the BTS T-100 Segment data⁵⁸ to capture total daily flight activities at 317 airports in the United States mainland and estimate operational demands placed on GSE fleets at each airport (see Methods, Approach for scaling up flight data).

To understand differences in flight characteristics across airports of varying sizes, the 317 airports were categorized into four groups according to the Federal Aviation Administration (FAA) classifications in the Part 139 Airport Certification Status List⁵⁹: large hub, medium hub, small hub, and non-hub (the list of airports in each category is provided in Supplementary Table 5). Figure 2 presents the flight characteristics by airport group. Figure 2a shows the number of airports within each group. Among the 317 airports, the majority are classified as non-hub airports (60.5%), large hubs and medium hubs each include 30 airports (9.5%), and small hubs comprise 65 airports (20.5%). Figure 2b illustrates the distribution of annual total flight arrival counts across the 317 airports. The airport classification is not strictly based on annual flight arrival counts. For instance, a medium hub airport may have more total flight arrivals than one classified as a large hub. Additionally, there can be significant variation within each category, particularly among large hub airports, where annual flight arrivals span from about 50,000 to 400,000 per year. Figure 2c depicts the distribution of daily flight arrivals for 10 illustrative airports in each group, and Fig. 2d shows the distribution of flight arrivals by time of day for the same 10 sample airports in each category.

Fig. 2. Distributions of flight arrival counts and arrival times.

a Number of airports within each group. b Distribution of annual flight arrivals across the airports. c Box plots depicting the daily flight arrivals for 10 illustrative airports in each group. d Distribution of flight arrivals by time of day for the same 10 illustrative airports in each group.

The flight arrival data for each airport were input into an eGSE model developed in this study to estimate the number of GSE, energy, and charging infrastructure requirements for GSE electrification. The model uses a bottom-up approach to simulate the operational and charging schedules of each individual GSE unit (see Methods, GSE operation and charging simulation). While GSE service activities can be systematically inferred from known flight schedules, charging activities remain flexible and can be strategically managed by fleet operators based on their operational preferences, eGSE specifications, infrastructure constraints, or economic considerations. Charging strategies affect the availability of both vehicles and chargers, thereby influencing the required fleet size and charging infrastructure to ensure reliable service, and leading to varying power demand profiles. Furthermore, charger power ratings determine the duration of recharging, which in turn impacts the timing of vehicle and charger availability. To evaluate how these factors influence overall system requirements and power demand, we modeled three distinct charging strategies in combination with two charger power levels (see Supplementary Note 1 for detailed description of the charging strategies and charger power levels).

The three charging strategies combined with the two charger power levels yield a total of six charging scenarios, as shown in Table 2. Each charging scenario was thoroughly examined to assess its impact on power demand, associated infrastructure requirements, and costs.

Table 2.

GSE charging scenarios by condition and power level

Charging Strategy	Charger Power Level
	40 kW Charger	20 kW Charger
Charge when vehicle SOC is insufficient for next service	Scenario 1 (S1)	Scenario 2 (S2)
Charge immediately after each task completion	Scenario 3 (S3)	Scenario 4 (S4)
Charge during off-peak hours	Scenario 5 (S5)	Scenario 6 (S6)

GSE ground support equipment, kW kilowatt, SOC state of charge.

Ground support equipment charging load profiles and energy demand

We first generated eGSE load profiles under the six defined charging scenarios. The Methods section details how individual eGSE charging events were modeled and subsequently aggregated to produce airport-level load profiles (i.e., power demand over time). Full-year load profiles were generated for each of the 317 airports (Data availability). To illustrate daily eGSE power demand patterns, October 20^th, 2023, was randomly selected to show the load profiles for all the airports. Figure 3 presents the resulting load profiles for the airports in each airport group on October 20^th, 2023 under the six charging scenarios (each line represents one airport). Note that the y-axis ranges differ across subplots to better represent the power demand variability and scale of each airport group. The x-axis in all plots represents hours of the day.

Fig. 3. Load profiles for airports in each airport group on October 20^th, 2023 under different charging scenarios.

a Charging when battery SOC is insufficient for the next service using 40 kW chargers. b Charging when battery SOC is insufficient for the next service using 20 kW chargers. c Charging starts immediately after each GSE vehicle completes a service task using 40 kW chargers. d Charging starts immediately after each GSE vehicle completes a service task using 20 kW chargers. e Charging during off-peak hours using 40 kW chargers. f Charging during off-peak hours using 20 kW chargers. SOC state of charge, GSE ground support equipment.

In terms of electrical load magnitude, large hub airports exhibit significantly higher peak power demand compared to other airport groups, with substantial intra-group variability, ranging from 1–2 MW up to 10–20 MW across charging scenarios. Medium and small hub airports generally require power demands below 5 MW, and non-hub airports consistently remain under 1 MW. Comparing the three charging strategies, the load profiles indicate that initiating charging when an eGSE’s SOC is insufficient for the next service (Fig. 3a, b) and charging immediately after each service event (Fig. 3c and Fig. 3d) result in similar temporal patterns and power demand levels, though the latter strategy might be more difficult to implement from an operational stand-point and may lead to accelerated battery degradation. In both charging strategies, power demand is concentrated during daytime hours, aligning with periods of high flight activity, and a noticeable decrease in power demand occurs from midnight to early morning, followed by an increase as flight operations resume. In contrast, the overnight off-peak charging strategy (Fig. 3e and Fig. 3f) yields a very different load profile. Under this strategy, the majority of eGSE charging demand is concentrated between 10:00 p.m. and 8:00 a.m., with only a small fraction extending into peak hours due to charging sessions that begin shortly before 8:00 a.m. Compared to the other two strategies, this approach generally results in higher peak power demand, which is expected as all vehicles are deliberately scheduled to charge within the fixed time window. However, this period coincides with minimal overall airport electricity usage, and in regions with time-of-use (TOU) utility rate structures, electricity prices and demand charges are typically lower during this period, which may offset the cost of the peak demands. A comparison of the load profiles using chargers of different power ratings reveals that the two charger power levels produce generally similar load profiles. The use of lower-power, 20 kW chargers, leads to a reduction in peak demand for some airports on the selected day and results in a smoother load profile.

The load profiles in Fig. 3 illustrate eGSE power demand patterns over a day across different airport groups. To further evaluate the impact of GSE electrification on airport power grids, especially under worst-case conditions, and the spatial distribution of these impacts across the United States, we calculated the annual peak power demand for each airport under the six charging scenarios and further calculated the peak power demand differences between scenarios S2-S6 with the base scenario S1. The results are presented in Fig. 4. Figure 4a shows the annual peak power demand for each airport under the base scenario S1, while Fig. 4b–f illustrate the impact of different charging strategies and charger power levels on peak power demand. As shown in Fig. 4a, airport-level eGSE peak demand is strongly correlated with flight arrival volume, the peak demand distribution aligns closely with the distribution of flight arrivals depicted in Fig. 2b. Large hub airports that have the highest number of flight arrivals among all the airport groups also have the highest peak demand. Medium hub airports, with comparatively fewer arrivals, exhibit lower peak demands, and small hub and non-hub airports generally show the lowest peak demand levels.

Fig. 4. Peak power demand distribution across the 317 airports under the six charging scenarios with varying charger power and strategies.

a Peak power demand distribution under the base scenario (S1), in which charging occurs when battery SOC is insufficient for the next service using 40 kW chargers. b Distribution of changes in peak power demand between S2, charging when battery SOC is insufficient for the next service using 20 kW chargers, and the base scenario (S1). c, Distribution of changes in peak power demand between S3, charging starts immediately after each GSE vehicle completes a service task using 40 kW chargers, and the base scenario (S1). d Distribution of differences in peak power demand between S4, charging starts immediately after each GSE vehicle completes a service task using 20 kW chargers, and the base scenario (S1). e Distribution of changes in peak power demand between S5, charging during off-peak hours using 40 kW chargers, and the base scenario (S1). f Distribution of changes in peak power demand between S6, charging during off-peak hours using 20 kW chargers, and the base scenario (S1). SOC state of charge, GSE ground support equipment.

Figure 4b–f demonstrate that both charging strategy and charger power level have a substantial influence on eGSE peak demand. Compared with the base scenario S1, scenario S2—charging only when the battery SOC is insufficient for the next service using 20 kW chargers—results in lower peak power demand for most airports across the four airport categories (as indicated by hollow circles representing negative peak demand changes in Fig. 4b). Figure 4c shows that with 40 kW chargers, the second charging strategy, i.e., charging immediately after completing each service, results in relatively higher peak demand for most airports across all categories. Notably, Fig. 4d indicates that using the same strategy with 20 kW chargers produces mixed results: some airports experience higher peak demand, while others experience lower demand compared to the base scenario S1. Figure 4e illustrates that under the overnight charging strategy with 40 kW chargers, most airports exhibit substantially higher peak demand relative to S1, with a few exceptions in the small and non-hub categories. Figure 4f shows that overnight charging with 20 kW chargers leads to higher peak demand for large and medium hubs compared to S1, though the increase is smaller than that observed in S5. Meanwhile, more airports in the small and non-hub categories show lower peak demand than in the base scenario. These findings align with the load magnitudes shown in Fig. 3, while also providing a national geospatial view of peak loads and the quantitative differences between scenarios S2–S6 and S1.

In addition to power demand, airport managers and GSE operators may also be interested in understanding the amount of energy required for eGSE operations. The energy demand of eGSE represents the total electricity consumed over a given time period and is generally unaffected by charging strategies or charger power levels. Energy demand is a key component of GSE operational costs, as it directly influences the energy charges on electricity bills. The annual eGSE energy consumption for each airport was calculated by integrating the power demand over time using the airport’s load profile. Subsequently, the average daily energy consumption was calculated for each airport. Table 3 summarizes the minimum, maximum, and mean of both annual and daily energy consumption for each of the four airport groups. The annual energy consumption of the eight GSE types can be as high as around 51,000 MWh at large airports, with an average daily consumption reaching up to approximately 140 MWh. In contrast, smaller airports may consume only a few MWh annually, with daily averages in hundreds of kilowatt-hours (kWh). While energy consumption is strongly correlated with flight arrival volume, the relationship is not strictly linear. It also depends on the composition of arriving aircraft types, as different aircraft vary in size and service requirements, which influence the type, quantity, and operating duration of GSE vehicles utilized. Average daily energy consumption breakdown by GSE type is presented in Supplementary Note 2. We found that among all GSE types, GPUs account for the largest share of energy consumption, contributing nearly half of the total demand, while lavatory trucks and water trucks consume the least energy due to their short service durations. Catering trucks also represent a notable share, primarily because of travel between staging areas and gates, whereas other GSE types such as aircraft tractors, baggage tractors, cargo loaders, and belt loaders contribute moderate to small portions of overall energy use. Detailed daily energy consumption per airport is provided (Data availability).

Table 3.

Annual and average daily energy consumption of eGSE by airport category

Airport category	Annual flight arrivals range (thousand)	eGSE energy consumption (MWh)
		Annual			Average daily
		Min	Max	Mean	Min	Max	Mean
Large hub	89–377	10,819	50,878	26,709	29	139	73
Medium hub	26–92	2941	12,102	5934	8	33	16
Small hub	5–74	577	9,239	1617	2	25	4
Non-hub	0.1–10	8	1204	265	0.1	4	0.8

eGSE electric ground support equipment, MWh megawatt-hour.

Ground support equipment fleet and infrastructure requirements

The integration of eGSE into the airport turnaround process necessitates substantial upfront investments, not only in the vehicle fleet but also in charging infrastructure. To ensure reliable service delivery and minimize costs, it is essential to understand the minimum number of vehicles and chargers required, accounting for both operational demands and charging constraints. Figure 5a illustrates the distribution of GSE vehicle counts by GSE type and the corresponding charger requirements across different airport categories under the base charging scenario S1. Figure 5b–f present the differences in vehicle counts and charger requirements between each of the remaining charging scenarios and the base scenario. The height of each bar represents the average number of units within each group, while the overlaid dots indicate the values for individual airports within the group.

Fig. 5. GSE counts and charger requirements under the six charging scenarios across airport categories.

a Number of GSE vehicles by type and chargers required for each airport category under the base scenario (S1), in which charging occurs when battery SOC is insufficient for the next service using 40 kW chargers. b Changes in vehicle counts by type and required chargers relative to S1 under S2, which applies the same charging strategy but using 20 kW chargers. c Changes in vehicle counts and required chargers relative to S1 under S3, charging starts immediately after each GSE vehicle completes a service task using 40 kW chargers. d Changes in vehicle counts and required chargers relative to S1 under S4, charging starts immediately after each GSE vehicle completes a service task using 20 kW chargers. e Changes in vehicle counts and required chargers relative to S1 under S5, charging during off-peak hours using 40 kW chargers. f Changes in vehicle counts and required chargers relative to S1 under S6, charging during off-peak hours using 20 kW chargers. GPU ground support unit, AT aircraft tractor, BT baggage tractor, CL cargo loader, BL belter loader, CT catering truck, LT lavatory truck, WT water truck, CH charger, GSE ground support equipment, SOC state of charge.

It can be seen from Fig. 5a that, as expected, large and medium hub airports generally require more GSE vehicles and chargers than small and non-hub airports due to higher operational intensity. Figure 5a provides a baseline view of the magnitude of GSE and charger requirements across airport categories, while Fig. 5b–f illustrate how these requirements change under alternative charging strategies and charger power levels. Overall, the results demonstrate that both charging strategy and charger power level play important roles in determining equipment needs. As shown in Fig. 5b, Scenario 2 (S2), which uses 20 kW chargers, requires slightly more GSE vehicles and more chargers than the base scenario using 40 kW chargers (Fig. 5a). This outcome is expected due to the longer charging durations associated with lower charger power. Figure 5c, d indicate that the second charging strategy, charging immediately after task completion, under both 40 kW (S3) and 20 kW (S4) chargers, results in comparable or lower GSE vehicle requirements but higher charger requirements compared to the base scenario. The third charging strategy, overnight charging within a fixed time window (Fig. 5e and f), requires significantly more equipment in terms of both GSE vehicles (especially GPUs, aircraft tractors, and baggage tractors) and chargers than the other two strategies. This increase is attributed to the fact that the overnight charging scenario restricts the vehicle to charge only during designated off-peak nighttime hours, whereas the other two strategies allow charging during both daytime and nighttime periods. Under deferred charging, vehicles become unavailable if their batteries are depleted before the charging window, requiring additional GSE units to maintain operational continuity. Moreover, concentrating charging activity within a limited overnight window necessitates a larger number of chargers to meet peak demand during that period.

The detailed numbers of GSE vehicles by type and the corresponding charger requirements for each airport are provided (Data availability). These estimates are based on the defined modeling assumptions and parameters, and represent the minimum operational requirements, excluding considerations such as equipment downtime due to maintenance or unexpected failures. An airport-specific spare ratio might need to be implemented to make actual procurement decisions.

The analysis shows that the overnight charging strategy shifts load to off-peak hours but requires significantly more GSE units and chargers compared to the other strategies. An economic assessment was conducted to understand the trade-off between charging costs and upfront investment and results are presented in Supplementary Note 3.

As many modern airports are increasingly equipped with passenger boarding bridges (PBBs) that provide fixed electrical power and pre-conditioned air at the gate, in this analysis, we also considered a scenario in which PBBs supply electricity and pre-conditioned air to aircraft during gate turnaround. In this configuration, aircraft receive power through a shore power connection integrated into the bridge, drawing electricity directly from the airport grid rather than from mobile GPUs. Since this setup provides the same functional service as a mobile GPU, the use of mobile GPUs is eliminated at equipped gates, leading to reduced GSE energy demand and fleet size requirements. The annual and average daily eGSE energy consumption by airport category under this scenario is presented in Supplementary Table 6, and the energy consumption breakdown by GSE type is summarized in Supplementary Note 2. The GSE vehicle and charger requirements under this scenario is shown in Supplementary Fig. 3. Moreover, because electricity is supplied directly from the grid instead of through GSE chargers, this scenario results in lower power load profiles resulted from GSE chargers compared to scenarios relying on mobile GPUs. Daily load profiles for all airports on October 20^th, 2023 under this scenario are illustrated in Supplementary Fig. 4. Peak demand distributions across all airports for this scenario is provided in Supplementary Fig. 5.

Behind-the-meter storage and on-site generation analysis

As the above analysis indicates, electrifying GSE vehicles can significantly increase energy demand and place additional stress on airport electrical infrastructure, especially at large airports. This may require costly upgrades such as transformer or substation expansions. BTMS and on-site generation technologies such as PV are often viewed as cost-effective strategies to manage energy use and mitigate peak demand, potentially reducing charging costs and the need for major distribution system enhancements. However, PV and battery storage systems also entail substantial upfront capital investment and ongoing maintenance expenses. Therefore, determining optimal PV and battery storage configurations and understanding their potential to mitigate costs and grid impacts are essential. There are several but limited studies that have analyzed airport-level PV and battery storage integration through single-airport techno-economic case studies⁶⁰, assessed BTMS for specific airport rental car centers⁶¹, and evaluated the technical feasibility of PV deployment at airports⁶². These studies are typically restricted to single-airport case studies or do not explicitly quantify the cost benefits of combined BTMS and PV deployment. In this study, we used NLR’s EVI-EDGES (Electric Vehicle Infrastructure – Enabling Distributed Generation Energy Storage) tool⁶³ to evaluate the trade-off between infrastructure investment and reduced charging costs, and to quantify the optimal sizing and cost benefits of BTMS and PV systems across multiple airports. The eGSE load profile of the base charging scenario, i.e., charging when battery SOC is insufficient for the next service using 40 kW chargers, was utilized for the analysis. By inputting the load profiles into EVI-EDGES, we identified the optimal combinations of PV and battery storage capacities that minimize the life cycle cost for each airport. The results of the EVI-EDGES analysis for 10 illustrative airports in each airport group are presented in Table 4.

Table 4.

EVI-EDGES analysis results

Airport group	IATA code	Without BTMS		Optimal BTMS design using EVI-EDGES
		Peak demand (MW)	Net present life cycle cost ($1 M)	PV size (MW)	Battery storage size (MWh)	Peak demand (MW)	Net present life cycle cost ($1 M)	Peak demand reduction (%)	Life cycle cost reduction (%)
Large hub	ATL	13.56	104.98	10.64	8.53	9.76	98.33	28.00	6.34
	CLT	9.56	68.25	9.42	9.49	6.17	62.36	35.48	8.63
	DEN	12.52	85.83	12.18	9.71	8.74	78.95	30.18	8.02
	DFW	15.76	101.92	15.26	11.73	10.55	93.12	33.08	8.64
	EWR	8.68	69.89	3.28	10.20	6.60	66.86	23.95	4.33
	JFK	11.60	93.25	1.58	10.06	9.48	89.13	18.24	4.42
	LAX	11.84	92.17	11.62	6.33	9.72	82.68	17.92	10.29
	ORD	13.64	106.69	4.74	15.44	9.60	100.81	29.62	5.51
	SEA	8.16	60.35	0.70	4.59	6.04	57.13	25.97	5.33
	SFO	8.44	61.19	8.42	4.67	6.59	54.59	21.93	10.79
Medium hub	CLE	2.16	13.67	0.70	2.34	1.60	12.31	25.91	9.95
	CVG	3.60	23.68	1.29	2.97	2.56	21.52	28.89	9.14
	DAL	3.20	20.84	1.85	2.54	1.92	19.01	39.99	8.79
	HOU	3.40	19.08	1.72	3.73	1.84	17.17	45.94	9.98
	MCI	2.40	15.08	0.91	1.56	1.48	13.67	38.14	9.35
	MEM	4.24	28.57	1.47	3.52	3.16	26.31	25.37	7.89
	OAK	2.56	17.21	1.64	2.86	1.72	15.19	32.81	11.75
	PDX	4.48	24.44	0.34	2.33	3.22	22.93	28.11	6.19
	RDU	3.04	19.71	1.17	2.40	2.24	18.14	26.32	7.97
	STL	3.08	19.66	0.72	1.58	2.24	18.19	27.21	7.46
Small hub	BUF	1.68	8.57	0.12	1.10	0.94	7.52	44.00	12.24
	CAE	1.08	5.29	0.02	0.83	0.53	4.33	51.20	18.23
	ELP	1.40	7.93	0.90	0.56	0.99	6.63	29.23	16.40
	GEG	1.64	9.10	0.39	1.57	0.96	7.89	41.46	13.26
	GRR	1.40	7.08	0.08	1.27	0.68	6.16	51.37	12.99
	MYR	1.40	6.56	0.74	1.37	0.65	5.43	53.90	17.33
	OKC	1.44	8.08	0.26	1.09	0.89	7.12	38.28	11.82
	ORF	1.56	8.31	0.42	1.31	0.81	7.35	48.36	11.57
	SDF	3.88	23.21	1.29	3.34	2.44	21.26	37.11	8.39
	TYS	1.40	6.73	0.18	0.90	0.87	5.92	37.50	12.10
Non-hub	ABE	0.80	3.67	0.00	0.69	0.35	2.93	56.77	20.15
	ASE	0.76	3.50	0.28	0.48	0.45	2.81	40.90	19.69
	AZA	0.92	4.15	0.08	0.94	0.40	3.30	56.38	20.64
	BIL	1.12	5.26	0.19	1.03	0.51	4.32	54.49	17.91
	FWA	1.04	3.87	0.06	0.74	0.56	3.27	46.01	15.33
	RFD	0.92	5.04	0.68	0.80	0.46	3.90	50.33	22.68
	ROA	0.80	3.68	0.12	0.64	0.35	3.03	55.76	17.68
	SBN	0.92	3.71	0.00	1.00	0.45	3.08	51.49	16.92
	SHV	0.68	3.32	0.17	0.72	0.32	2.72	52.72	18.12
	TLH	0.88	3.63	0.13	0.64	0.46	3.03	47.58	16.52

IATA International Air Transport Association, BTMS behind-the-meter storage, MW megawatt, M million, MWh megawatt-hour.

EVI-EDGES determines the optimal PV and battery storage sizes for each airport based on its unique load profile and location-specific solar resource. The results suggest that installing PV systems and battery storage could help reduce both peak demand and life cycle costs across all 40 selected airports. For large and medium hub airports, peak demand reductions of 20–30% and cost savings of 5–10% are achievable, equating to reductions of up to a few megawatts in peak load and several million dollars in annual cost savings. Small and non-hub airports could experience even greater benefits, with peak demand reductions of 30–50% and cost savings of 10–20%.

Discussion

Electrifying GSE at airports offers significant benefits for both passengers and airport personnel, but requires a thorough understanding of the associated impacts, infrastructure requirements, and operational considerations. This study presented a bottom-up modeling approach to simulate GSE operations and charging events, enabling the estimation of energy and power demands as well as equipment requirements for replacement of conventional GSE with electric alternatives. The methodology was grounded in airport-level flight arrival data and simulated turnaround service activities for each arriving flight. These were then aggregated to derive airport-level demand profiles and equipment needs. The analysis focused on eight key GSE types for which electric models are commercially available.

The results of this study indicate that electrifying GSE at airports can impose substantial energy, power, and infrastructure challenges, and that charging strategies and charger types play a critical role in shaping these impacts. Energy and power requirements scale strongly with airport size. Large hub airports with high aircraft turnover may experience peak power demands of up to 20 MW and annual energy consumption exceeding 50,000 MWh, whereas smaller airports typically remain below 1 MW and only a few thousand MWh annually. Energy consumption also varies by GSE type. Among all analyzed equipment categories, ground power units account for the largest share of total energy demand, while lavatory and water trucks consume the least due to their short service durations. Catering trucks contribute a notable portion of total energy use because of travel between staging areas and gates, whereas other GSE types represent moderate to smaller shares of overall consumption. Charger power level significantly influences both peak demand and infrastructure needs. Lower-power chargers generally reduce peak power demand but require a larger GSE fleet and a greater number of chargers to maintain operational reliability. Charging strategy does not affect total energy consumption, but it strongly influences peak power demand and infrastructure requirements. Compared to opportunity-based charging, overnight charging shifts load to off-peak hours but can substantially increase peak power demand and require additional vehicles and charging units. Finally, integrating photovoltaic generation and battery storage systems can meaningfully mitigate grid impacts and costs. Behind-the-meter solutions reduce peak demand associated with GSE electrification by approximately 20–50% and lower life-cycle costs by 5–20%, offering a cost-effective pathway to managing increased electricity demand at airports.

While the current analysis provides a strong foundation for eGSE modeling and planning, future research could benefit from incorporating more detailed data on airport grid infrastructure and metering configurations, as well as accounting for the effects of climate and temperature on eGSE charging performance. These additions would enhance model accuracy and support more precise infrastructure planning.

Methods

Approach for scaling up flight data

To estimate airport-wide GSE energy and power demand as well as fleet and infrastructure requirements, flight activity was scaled using two BTS data sources. The Airline On-Time dataset⁵⁷ provides detailed operational data (e.g., flight arrival and departure times, tail numbers, origin and destination airports) but is limited to domestic flights by major U.S. carriers. The T-100 Segment dataset⁵⁸ includes monthly aggregated flight counts for both domestic and international operations by U.S. and foreign carriers but lacks operational detail. To account for all flights, detailed patterns from the On-Time dataset were scaled to the broader flight totals from the T-100 Segment dataset. Specifically, we first calculated the monthly discrepancy in flight counts between the two datasets to estimate the number of domestic and international flights not captured in the On-Time data. Next, we derived the flight arrival time distribution from the On-Time dataset and applied it as a temporal scaling factor to the estimated missing flights. Finally, we concatenated the scaled records with the existing On-Time data to construct a complete, time-resolved flight activity profile at each airport.

Ground support equipment operation and charging simulation

The operating and charging schedules of GSE vehicles at each airport were derived from the flight arrival activity at that airport, with the assumption that each arriving flight necessitates a specific number of GSE vehicles to perform ground services, and that each vehicle operates for a defined duration based on the corresponding aircraft type. Information on the number and types of GSE vehicles required, as well as their associated service durations for aircraft turnaround operations, was obtained from the literature^44,64. Supplementary Tables 1 and 2 report per-task operating durations for each GSE type, separately for gate and off-gate operations⁴⁴. Supplementary Table 7 summarizes the number of each GSE vehicle type required for servicing wide-body and narrow-body aircraft⁶⁴.

With flight arrival information and established GSE operation patterns, GSE service tasks—defined by their start time, duration, and end time—can be systematically determined. Let $\mathcal{N}=\left\{\mathrm{1,\; 2},\dots ,n\right\}$ denote the set of arrival flights at an airport. For flight $i\in \mathcal{N}$, denote its arrival and departure times by $t_i^{arr}$ and $t_i^{dep}$, respectively, and let ${\epsilon }_i\in \left\{\mathrm{wide}-\mathrm{body},\mathrm{narrow}-\mathrm{body}\right\}$ indicate its aircraft type. Let $\mathcal{K}$ denote the set of GSE vehicle types, $\mathcal{K}=\left\{\mathrm{GPU},\mathrm{aircraft}\mathrm{trac}\mathrm{tor},\mathrm{baggag}\mathrm{e}\mathrm{tractor},\mathrm{belt}\mathrm{loader},\mathrm{cargo}\mathrm{loader},\mathrm{lavatory}\mathrm{truck},\mathrm{catering}\mathrm{truck},\mathrm{water}\mathrm{truck}\right\}$. For each GSE vehicle type $k\in \mathcal{K}$, define ${\mathcal{V}}_{i,k}=\left\{\mathrm{1,\; 2},\cdots ,V_{i,k}\right\}$ as the set of required vehicle numbers (i.e., tasks) for flight $i$, with counts from the Supplementary Table 7 based on aircraft type ${\epsilon }_i$. For each GSE vehicle task $v\in {\mathcal{V}}_{i,k}$, the gate service duration ${\tau }_{i,k,v}^{gate}$ and off-gate operation time ${\tau }_{i,k,v}^{off}$ are obtained from Supplementary Tables 1 and 2. It is assumed that all GSE vehicles except for aircraft tractors begin to move toward to the arrival gate upon aircraft arrival. Therefore, for a GSE task $v\in {\mathcal{V}}_{i,k},k\in \mathcal{K}/\left\{aircrafttractor\right\}$, the start and end times, $S_{i,k,v}^{start}$ and $S_{i,k,v}^{end}$, are defined as by Eqs. (1)-(2):

$$S_{i,k,v}^{start}=t_i^{arr},\forall v\in {\mathcal{V}}_{i,k},k\mathcal{\backslash in}\mathcal{K}/\left\{aircrafttractor\right\},i\mathcal{\backslash in}\mathcal{N}$$ 1

$$S_{i,k,v}^{end}=t_i^{arr}+2{\tau }_{i,k,v}^{off}+{\tau }_{i,k,v}^{gate},\forall v\in {\mathcal{V}}_{i,k},k\mathcal{\backslash in}\mathcal{K}/\left\{aircrafttractor\right\},i\mathcal{\backslash in}\mathcal{N}$$ 2

For aircraft tractors which operate at the end of the turnaround process to push the aircraft from the gate, the start and end times of the GSE task $v\in {\mathcal{V}}_{i,k},k\in \left\{aircrafttractor\right\}$ are based on the aircraft departure time $t_i^{dep}$. Thus, their start and end times are defined as by Eqs. (3)-(4):

$$S_{i,k,v}^{start}=t_i^{dep}-{\tau }_{i,k,v}^{off},\forall v\in {\mathcal{V}}_{i,k},k\in \left\{aircrafttractor\right\},i\mathcal{\backslash in}\mathcal{N}$$ 3

$$S_{i,k,v}^{end}=t_i^{dep}+{\tau }_{i,k,v}^{gate}+{\tau }_{i,k,v}^{off},\forall v\in {\mathcal{V}}_{i,k},k\in \left\{aircrafttractor\right\},i\mathcal{\backslash in}\mathcal{N}$$ 4

With all GSE service tasks defined by their respective start and end times, we developed an agent-based simulation model to generate feasible service and charging schedules for each vehicle and to determine the number of GSE vehicles required for each GSE type under different charging strategies. The model considers both operational feasibility and battery energy constraints. Depending on the operational objectives and infrastructure conditions, airport operators may adopt different GSE charging strategies, which in turn influence vehicle availability and charger demand. This study investigated three possible charging strategies that may be adopted by GSE fleet operators: (1) threshold-based charging: charging is triggered only when the vehicle’s remaining SOC is insufficient for the next service, and the vehicle will charge to its full SOC; (2) immediate charging: charging is initiated after finishing each service task and continues until the battery is full or the next task begins; and (3) scheduled overnight charging: charging is restricted to a predefined off-peak window $\left[\alpha ,\beta \right]$, aiming to minimize the grid impact.

The simulation proceeds through the following major steps:

Step 1: Initial task chaining. Let ${\mathcal{L}}_k=\left\{\mathrm{1,\; 2},\dots ,L_k\right\}$ denote the set of all service tasks for GSE type $k$, let $S_{\mathcal{l},k}^{start}$ and $S_{\mathcal{l},k}^{end}$ respectively represent the start and end times of task $\mathcal{l}\in {\mathcal{L}}_k$. All tasks ${\mathcal{L}}_k$ were sorted in chronological order of $S_{\mathcal{l},k}^{start}$. A greedy chaining algorithm was adopted in the model to assign tasks to vehicles such that for each vehicle, a new task $\mathcal{l}$ is assigned to the vehicle and removed from the task set ${\mathcal{L}}_k$ if its start time is greater than or equal to the end time of the preceding task $l^{\prime }$, as defined in Eq. (5):

$$S_{\mathcal{l},k}^{start}\ge S_{{\mathcal{l}}^{\prime },k}^{end},\forall {\mathcal{l}}^{\prime },\mathcal{l}\mathcal{\backslash in}{\mathcal{L}}_k,{\mathcal{l}}^{\prime }\mathrm{precedes}\mathcal{l}$$ 5

This process yields an initial estimate of the number of GSE vehicles needed for each GSE type and an ordered sequence of tasks for each vehicle without considering the charging needs. Let ${\mathcal{M}}_k=\left\{1,2,\dots ,M_k\right\}$ represent the initial number of vehicles required for GSE type $k\in \mathcal{K}$, and let ${\mathcal{H}}_{m,k}=\left\{1,2,\dots ,H_{m,k}\right\}$ denote the sequence of tasks for vehicle $m\in {\mathcal{M}}_k$.

Step 2: Energy consumption tracking. For each task $h\in {\mathcal{H}}_{m,k}$ assigned to vehicle $m\in {\mathcal{M}}_k$, the energy consumption $e_{m,k,h}$ is computed by Eq. (6):

$$e_{m,k,h}={\delta }_k^{service}\cdot {\tau }_{m,k,h}^{gate}+{2\cdot \delta }_k^{transit}\cdot {\tau }_{m,k,h}^{off},\forall h\in {\mathcal{H}}_{m,k},m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 6

where ${\delta }_k^{service}$ and ${\delta }_k^{transit}$ denote the energy consumption rate of GSE type $k$ during service and during travel, respectively, ${\tau }_{m,k,h}^{gate}$ and ${\tau }_{m,k,h}^{off}$ denote the vehicle’s gate service time and off-gate travel time, respectively.

During operation, GSE vehicles must maintain their SOC within a prescribed range [${SOC}_{m,k}^{low},{SOC}_{m,k}^{upp}$] to ensure battery health. The initial SOC of each vehicle is assumed to be ${SOC}_{m,k}^0={SOC}_{m,k}^{upp}$, the upper bound of battery SOC. After each task $h$, the SOC is updated by Eq. (7):

$${SOC}_{m,k}^h={SOC}_{m,k}^{h-1}-e_{m,k,h}/{\pi }_k,\forall h\in {\mathcal{H}}_{m,k},m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 7

where ${\pi }_k$ is the battery capacity of GSE type $k$.

Step 3: Charging strategy implementation. Charging events were inserted between tasks depending on the adopted strategy. The charging event’s start and end times are formulated differently under each strategy. Let $C_{m,k,h}^{start}$ and $C_{m,k,h}^{end}$ denote the charging start time and end time, respectively.

Strategy 1 (threshold-based charging):

Charging is inserted only if ${SOC}_{m,k}^h-\frac{e_{m,k,h+1}}{{\pi }_k}<{SOC}_{m,k}^{low}$.

If true, $C_{m,k,h}^{start}$ and $C_{m,k,h}^{end}$ will be calculated by Eqs. (8) and (9):

$$C_{m,k,h}^{start}=S_{m,k,h}^{end}+{\tau }_{m,k,h}^{charge},\forall m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 8

$$C_{m,k,h}^{end}=C_{m,k,h}^{start}+\left({\pi }_k-{\pi }_k\cdot {SOC}_{m,k}^h\right)/\theta p,m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 9

where ${\tau }_{m,k,h}^{charge}$ denotes the setup time required to initiate the charging process, $p$ represents the charging power of GSE chargers, and $\theta$ denotes the charging efficiency.

Strategy 2 (immediate charging):

Charging is inserted after every service task, as shown in Eqs. (10)-(11):

$$C_{m,k,h}^{start}=S_{m,k,h}^{end}+{\tau }_{m,k,h}^{charge},\forall h\in {\mathcal{H}}_{m,k},m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 10

$$C_{m,k,h}^{end}=\min \left\{S_{m,k,h+1}^{start},C_{m,k,h}^{start}+\left({\pi }_k-{\pi }_k\cdot {SOC}_{m,k}^h\right)/\theta p\right\},\forall h\in {\mathcal{H}}_{m,k},m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 11

Strategy 3 (overnight charging):

Under this strategy, charging is restricted to a predefined off-peak time window $\left[\alpha ,\beta \right]$. If ${SOC}_{m,k}^h-\frac{e_{m,k,h+1}}{{\pi }_k}<{SOC}_{m,k}^{low}$ and the end time of the service task $S_{m,k,h}^{end}$ falls outside the off-peak window $\left[\alpha ,\beta \right]$, a charging event is scheduled to begin at $\alpha$. The corresponding charging start and end times are calculated by Eqs. (12)-(13):

$$C_{m,k,h}^{start}=\alpha ,\forall m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 12

$$C_{m,k,h}^{end}=C_{m,k,h}^{start}+\left({\pi }_k-{\pi }_k\cdot {SOC}_{m,k}^h\right)/\theta p,m\in {\mathcal{M}}_k,k\in \mathcal{K}$$ 13

For service tasks that end within the off-peak window $\left[\alpha ,\beta \right]$, charging is assumed to begin immediately after each service, as it naturally aligns with the allowed charging period.

Note that scheduling all charging events to begin at $\alpha$ may lead to a sharp peak in power demand at the start of the off-peak window, particularly at large airports with extensive GSE operations. This could also necessitate an extremely high number of chargers, making the strategy economically impractical. To address this, a post-processing step was further applied to redistribute the charging loads across the full off-peak interval $\left[\alpha ,\beta \right]$ to minimize the number of chargers required and smooth the overall power demand profile. The charging start and end times, $C_{m,k,h}^{start}$ and $C_{m,k,h}^{end}$, were updated accordingly during this post-processing step.

Step 4. After inserting charging events, task feasibility is verified. A task $h+1$ remains valid on the same vehicle if its start time is greater than or equal to the later of the previous task’s end time and the corresponding charging completion time, as defined in Eq. (14):

$$S_{m,k,h+1}^{start}\ge \max \left(S_{m,k,h}^{end},C_{m,k,h}^{end}\right),\forall m\in {\mathcal{M}}_k,k\in \mathcal{K},h\in {\mathcal{H}}_{m,k}$$ 14

If this condition fails, the task is reassigned to another vehicle if feasible or assigned to a newly added vehicle $m^{\prime }\in {\mathcal{M}}_k\leftarrow {\mathcal{M}}_k\cup \left\{m^{\prime }\right\}$.

Step 5: The model iterates the steps 1-4 until all task chains are feasible in time and energy, and SOC is tracked consistently throughout.

Estimation of ground support equipment fleet requirements, charging infrastructure, and energy load profiles

Using the proposed five-step approach, the simulation model can determine the required number of vehicles for each GSE type as well as the number of chargers needed at each analyzed airport. Since the start and end times of all charging events across vehicles are explicitly tracked, the model can generate detailed energy load profiles by aggregating the power consumption of concurrent charging events at each time interval, resulting in minute-level load estimates.

Optimal design of battery storage and photovoltaic size

NLR’s tool EVI-EDGES was used for determining the optimal design of battery storage and PV size for each airport. Taking eGSE load profiles as key input, EVI-EDGES uses a two-tier optimization approach to identify the optimal design and operation of behind-the-meter energy systems for eGSE. The inner loop performs high-resolution, daily operational optimization using the physics-based simulation tool System Advisor Model (SAM) to capture real-time dynamics combined with a model predictive control (MPC) algorithm. The outer loop uses a derivative-free optimization method to determine the combination of battery size and PV capacity that minimizes the system’s net present charging cost. This cost is calculated through a discounted cash-flow analysis over the project’s lifetime, ensuring the most cost-effective infrastructure and energy solution. Note that the cost includes the cost of chargers, battery storage, PV systems, and charging expenses, but excludes the cost of replacing the GSE fleet.

Author contributions

Y.H., K.K. and M.L. designed the research and conceived the paper; Y.H., K.K. and M.J. collected data; R.V. and Y.H. conducted the BTMS and on-site generation analysis; Y.H. developed the GSE simulation model and conducted the analysis; Y.H., M.J., and Y.G. validated the GSE simulation model; Y.H. wrote the paper.

Peer review

Peer review information

Nature Communications thanks Christoph Klingenberg, Melih Yıldız and Stuart Hillmansen for their contribution to the peer review of this work. A peer review file is available.

Data availability

The BTS On-Demand flight data and T-100 data used in this study, as well as the GSE charging load profiles, airport-level energy consumption data, and the fleet and charger numbers generated in this study, have been deposited in the NLR Data Catalog under accession code 298. The data are publicly available without restriction.

Code availability

The code used in this study for estimating GSE energy and power demand, fleet and charging infrastructure requirement, as well as data visualization is publicly available and archived on Zenodo to ensure reproducibility. The GitHub repository containing the code can be accessed at https://github.com/NatLabRockies/AthenaGSEPaper, and the specific version used for this study has been assigned the DOI 10.5281/zenodo.18854761.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-026-71125-4.