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Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5500-77438 

September 2020

Integrating Electric Vehicle Charging 

Infrastructure into Commercial 

Buildings and Mixed-Use Communities: 

Design, Modeling, and Control 

Optimization Opportunities

Preprint

Shanti Pless, Amy Allen, Lissa Myers, David Goldwasser, 

Andrew Meintz, Ben Polly, and Stephen Frank

National Renewable Energy Laboratory

Presented at the 2020 ACEEE Summer Study on Energy Efficiency in 

Buildings 

August 17-21, 2020
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

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Conference Paper

NREL/CP-5500-77438 

September 2020

Integrating Electric Vehicle Charging 

Infrastructure into Commercial 

Buildings and Mixed-Use Communities: 

Design, Modeling, and Control 

Optimization Opportunities

Preprint

Shanti Pless, Amy Allen, Lissa Myers, David Goldwasser, 

Andrew Meintz, Ben Polly, and Stephen Frank

Suggested Citation

Pless, Shanti, Amy Allen, Lissa Myers, David Goldwasser, Andrew Meintz, Ben Polly, and 

Stephen Frank. 2020. Integrating Electric Vehicle Charging Infrastructure into Commercial 

Buildings and Mixed-Use Communities: Design, Modeling, and Control Optimization 

Opportunities; Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5500-77438. https://www.nrel.gov/docs/fy20osti/77438.pdf
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Integrating Electric Vehicle Charging Infrastructure into Commercial 

Buildings and Mixed-Use Communities: Design, Modeling, and Control 

Optimization Opportunities 

Shanti Pless, Amy Allen, Lissa Myers, David Goldwasser, Andrew Meintz, Ben Polly, and 

Stephen Frank, National Renewable Energy Laboratory 

 ABSTRACT

Many cities and states have aggressive goals for personal and fleet vehicle electrification.

For example, the City and County of Denver set a goal of 30% of vehicles being electric by 

2030, growing to 100% in 2050. Reaching ambitious goals like this will require significantly 

more charging stations at workplaces, homes, apartments, and public fast charging stations. 

Charging infrastructure build-out is directly linked with electric vehicle (EV) uptake, and smart 

EV charging infrastructure planning, electrical system integration, new building electrical codes, 

and demand management are presenting new integration concepts and challenges for building 

owners, utilities, and designers. 

This paper discusses modeling and field studies of controlled EV charging that have been 

performed with the goal of minimizing requirements for infrastructure upgrades, minimizing 

building peak demand charges, and maximizing the use of on-site generation. We present a

large-scale workplace charging pilot of a demand-controlled scheduled EV charging system with 

over 250 active daily commuters, successfully demonstrating management of aggregate charging 

power to avoid new infrastructure investments, mitigate peak demand charges, and provide costeffective workplace charging to users. In addition to understanding opportunities for demand 

management, integrating these controllable loads into the energy modeling process for new 

buildings will also be necessary. This paper then presents an example energy modeling process 

that evaluates the potential effects of EV charging on building load profiles and infrastructure 

requirements for a mixed-use community. Finally, we discuss an illustration of how EV charging 

can be controlled to be synergistic with other building loads and distributed generation. 

Background

Many cities and states have aggressive goals for personal and fleet vehicle electrification.

Worldwide, electric vehicle (EV) penetration goals have resulted in an average growth of 59% 

year over year, with 2.1 million light vehicle EV sales in 2018 (McKinsey 2019). Reaching 

ambitious EV goals will require significantly more charging stations for residents and drivers, 

including at workplaces, homes, apartments, and public fast charging stations. With charging 

infrastructure build-out directly linked with EV uptake, smart EV charging infrastructure 

planning is an emerging commercial building and community energy planning promising 

practice. To understand the emerging scope and speed of deployment of the needed EV charging 

infrastructure, the following provides an investigation into the City of Denver’s goals, which are 

illustrative of many cities action plans in the United States (C40 2020). In the Denver mayor’s 

Climate Action Plan, Denver set the goal of reducing greenhouse gas emissions 80% by 2050 

(Denver Climate Action Plan 2018). The transportation sector is the second largest source of 
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greenhouse gas emissions in Denver, and the Climate Action Plan identifies EVs as a key way to 

reduce greenhouse gas emissions from vehicles. To achieve these goals, Denver set a goal that 

by 2030, 30% of vehicles would be electric, growing to 100% of vehicles in 2050. Specific 

observations include (Denver Climate Action Plan 2018):

• Denver will require significantly more public charging stations—approximately 10,000

by 2030 and 25,000 by 2050

• Denver only has 350 public charging stations as of 2019

• Between 2017 and 2018, EV sales in Colorado increased by 70% (up to 5.36% of sales)

• New buildings will need to be equipped to charge EVs to help increase EV adoption 

• Specific focus is needed on residential properties, in particular multifamily properties 

where more than 40% of Denver residents live and where there is greatest opportunity for 

recharging vehicles during idle time.

There are many factors involved in electrifying a fleet of vehicles and/or providing 

workplace or residential EV charging. Adequate planning requires a deep knowledge of vehicle 

routes, locations where charging will take place, available electrical distribution infrastructure, 

existing facility electricity usage, utility rates, insurance, federal and state policies, and vehicle 

costs. Key design and building infrastructure strategies to consider during building planning and 

design to enable these EV goals include: 

• Determining the types of charging infrastructure currently available and to be provided

• Assessing and modeling how new EV loads impact zero energy goals as well as energy 

accounting, building peak demand, building electrical infrastructure, utility costs, and 

adaptive management opportunities

• Considering locations for EV charging stations based on alignment with distributed 

renewables and local building and utility electrical infrastructure constraints (and 

availability)

• Aligning with local proposed new construction codes by building type, with an eye to 

requiring charging infrastructure in new construction 

• Enabling the future use of EVs as distributed energy resources for energy shifting and 

adaptive load management, including enabling future control and communications 

capabilities to minimize peak demand and maximize grid coordination opportunities.

Available Charging Infrastructure

A building development and design process takes time, so it is important to ensure 

adaptability in planning. This is particularly important in determining EV charging infrastructure 

needs, which are evolving rapidly. A building owner may deploy multiple kinds of EV charging 

systems based on the building type and occupant needs and connection to the energy system. The 

plan should determine how many charges are needed (including total number as well as single 

versus multiple vehicle chargers), where they should be located, and the type of charger required 

(e.g., Level 1, Level 2, or fast). Understanding the existing definitions of EVs and charging 

infrastructure is the first step in this process. The Southwest Energy Efficiency Project 

definitions include (SWEEP 2020a):

• Battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV). A 

motorized vehicle registered for on-road use, powered by an electric motor that draws 
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current from rechargeable storage that is charged by being plugged into an electrical 

source.

• Electric vehicle supply equipment (EVSE). The electrical conductors and equipment 

external to the EV that provide a connection between an EV and a power source to 

provide charging, with varying levels of power delivery available (i.e., a charging 

station).

• Level 1 EVSE. Level 1 charging is provided by a standard electrical outlet and can be 

convenient for home use or extended parking sessions; it charges slowly, however, 

offering about 5 miles of range per hour and a power output less than 1.5 kW at 120 

volts.

• Level 2 EVSE. Some home chargers and most public charging stations are Level 2, 

which can add 12 to 25 miles of range per hour, depending on the type of EV and its 

onboard charger at 3 kW to 7 kW at 208/240 volts; Level 2 EVSEs are ideal for times 

when the vehicle will be parked for at least an hour, such as at work, restaurants, movie 

theaters, sporting events, or longer shopping trips.

• EV fast charger. EV supply equipment with a minimum power output of 20 kW, often 

referred to as a DC fast charger; this device enables a quicker charge and is typically 

located in public areas with access for long distance trips (note that not all EVs have DC 

fast charge capabilities). 

• EV load management system. A system designed to allocate charging capacity among 

multiple EVSEs.

• EV-capable space. A designated parking space with conduit sized for a 40-amp, 

208/240-volt dedicated branch circuit from a building electrical service panel to the 

parking space and sufficient physical space in the same building electrical service panel 

to accommodate a 40-amp dual-pole circuit breaker.

• EV-ready space. A parking space that is provided with one 40-amp, 208/240-volt 

dedicated branch circuit for EV supply equipment that is terminated at a receptacle, 

junction box, or Level 2 EVSE within the parking space

• EVSE installed space. A parking space with EVSE capable of supplying Level 2 with 

current at 40 amps at 208/240 V.

Based on the possible locations for EV charging infrastructure, building designers can 

consider the following deployment scenarios to maximize alignment of this new electrical load 

with renewables and reduce infrastructure and peak demand costs:

• Charging at work in office buildings. Daytime loads with Level 2 EVSEs align well 

with the lowest-cost renewables. Utility demand charges can be mitigated with smart 

charging controls.

• Charging at single-family homes. Lower-power Level 1 EVSEs and overnight charging 

reduces overall peak loads and can be aligned with off-peak periods for time-of-use rates 

to minimize charging costs.

• Charging in apartment and multifamily buildings. Lower-power Level 1 EVSEs and 

overnight charging reduces overall peak loads and can be aligned with off-peak periods 

for time-of-use rates to minimize charging costs.

• Strategic destination charging (hotels, park-and-rides, gyms, schools, etc.). Provides 

charging infrastructure anywhere vehicles are parked for extended periods of time.
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• Fast charging (long distance travel and fleets). Additional charging flexibility and 

demand management is possible with demand-management controls on EV charging 

infrastructure.

New Construction Codes for Charging Infrastructure

According to the Southwest Energy Efficiency Project (SWEEP 2020b), approximately 

half of all vehicles in the United States belong to residents of single-family or duplex homes with 

access to a dedicated off-street parking space, such as a garage or driveway that could be used 

for overnight EV charging. The other half do not have reliable access to a dedicated off-street 

parking space at an owned residence, so there is a need to expand charging access to multifamily 

unit dwellings, workplaces, and commercial properties. In addition, the installation of an EV 

charging station is 64%–75% less expensive when the infrastructure is installed during initial 

construction rather than retrofitted in existing buildings (Pike et al. 2016). Therefore, new 

construction codes for EVSE charging infrastructure are being developed to ensure these EV 

charging capabilities become available as buildings and communities are built out over time. For 

example, Table 1 indicates a local city jurisdiction’s 2019 proposed new construction code 

requirements for charging stations to be installed by building type (Denver Community Planning 

and Development 2019).

Table 1. Electric vehicle infrastructure Denver proposed code requirements (proposed in 2019)

Number of EV-ready 

spaces

Number of EVcapable spacesNumber of EVSE 

installed spaces

Single-family homes and townhomes

Parking space in a garage 1 in every parking garage None None

Multifamily EV infrastructure

1 space 1 None None

2–9 spaces 1 20% of space None

10 or more spaces 15% of spaces Remainder of 

spaces 5% of spaces

Commercial and parking garage EV charging infrastructure

1 space 1 None None

2–9 spaces 1 1 None

10 or more spaces 10% of spaces 10% of spaces 5% of spaces

Aligning EV Charging with Distributed Renewables

In a recent Rocky Mountain Institute evaluation of the future of buildings, EV charging,

and the grid, researchers identified key strategies for building systems and loads that could 

enable more cost-effective grid integration and the addition of renewables to the grid (Goldberg 

et al. 2018). The authors modeled the use of demand flexibility strategies to shift electricity 

consumption from times of the day with high demand but low renewable supply to times with 

high renewable supply. As shown in Figure 1, the authors identified EV charging loads as the 

largest opportunity utility system-wide to maximize the benefits of demand flexibility, 

suggesting that developing a grid-coordinated EV charging infrastructure could be a key strategy 

to reduce utility system costs and maximize alignment of loads with renewable generation. 
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Figure 1. Opportunity for flexible EV loads to reduce utility system peaks and flatten loads (Goldberg et al. 2018)

Building Energy Models and EV Charging Loads 

The U.S. Department of Energy (DOE) Zero Energy Building and Campuses Common 

Definition, as well as the U.S. Green Building Council’s LEED Zero Building certification 

program, provide specific guidance for how to treat EV charging loads in the zero energy 

building accounting (DOE 2015): 

Zero Energy Building (ZEB) energy accounting would include energy used for heating, cooling, 

ventilation, domestic hot water (DHW), indoor and outdoor lighting, plug loads, process energy, 

and transportation within the building. Vehicle charging energy for transportation inside the

building would be included in the energy accounting. On-site renewable energy may be exported 

through transmission means other than the electricity grid such as charging of electric vehicles 

used outside the building.

For a building or district that provides electricity to charge EVs used for transportation to 

and from the district, this load, if separately metered, is considered an export option rather than 

an additional load to offset with renewable energy. However, the interaction with the local 

electrical distribution system as well as the impacts on building’s peak demand or time-of-use 

rate costs also need to be considered. As such, there is increasing interest in grid-interactive 

efficient buildings and the role that building demand flexibility can play in providing value to 

building owners while also providing benefits to the grid, such as enhanced resilience, deferring 

capital expenditures, and supporting the balance of renewable energy generation supply (DOE 

2020). As previously identified, a major potential future source of demand flexibility in buildings 

Move evening EV 

charging loads to 

daytime hours to 

flatten load
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and communities is EV charging. As a first step, it is critical to understand the potential impact 

that uncontrolled EV charging can have on the typical load profiles of buildings and 

communities. With that knowledge, EV charging can be controlled and optimized, along with 

other building demand flexibility technologies, to manage building-scale and community-scale 

loads to achieve targeted benefits. Physics-based, predictive modeling helps facilitate the 

estimation of EV charging impacts and the optimization of EV charging controls from a wholebuilding or whole-community integration perspective.

A Zero Energy District Modeling Example

The National Renewable Energy Laboratory (NREL) partnered with Panasonic on the 

design of the Peña Station NEXT (PSN) development. PSN will occupy a 400-acre site, where 

103 buildings are planned. The development is anchored by a Panasonic office. The planned 

development includes residential, hotel, office, and retail buildings. The goal of the development 

is to be net zero energy, with much greater than 50% penetration of distributed energy resources 

(Doubleday et al. 2019). NREL led the analysis that will support the design of energy systems 

for PSN in the context of the net zero energy goal. As part of the project, NREL assessed the 

expected impact of EV loads on PSN’s overall electric load profile and the local distribution 

grid. NREL constructed energy models of the 103 planned buildings at PSN, with the integration 

of EV charging loads, using URBANopt and OpenStudio®. URBANopt is a district- and 

community-scale modeling platform originally developed by NREL and now being developed as 

a collaborative, open-source project. According to its developers, “the URBANopt platform uses 

the OpenStudio platform to perform detailed energy modeling at the individual building level 

using EnergyPlus” (Polly et al. 2016). OpenStudio is a “cross-platform collection of software 

tools to support whole building energy modeling using EnergyPlus,” and is developed in 

collaboration by five national laboratories (NREL, Argonne National Laboratory, Lawrence 

Berkeley National Laboratory, Oak Ridge National Laboratory, and Pacific Northwest National 

Laboratory) (NREL 2020a).

Within URBANopt, OpenStudio models are configured programmatically through a 

workflow consisting of a series of Ruby scripts, known as “OpenStudio measures.” OpenStudio 

measures modify components or values in an OpenStudio model. OpenStudio measures can be

leveraged for many additions or modifications to OpenStudio models, including creating or 

altering model geometry, systems, or loads, and reporting of output values (NREL N.D.). NREL 

maintains the Building Component Library (BCL), which is a repository of model components 

and OpenStudio measures available for use by the public and is accessible online and in the 

OpenStudio application (NREL 2019). Because OpenStudio does not yet have the capability to 

access all components of the underlying EnergyPlus model structure, it is sometimes necessary 

to leverage “EnergyPlus measures,” which are analogous to OpenStudio measures, but operate 

on the EnergyPlus model directly (NREL N.D.). In this work, an OpenStudio measure was 

written to add an EV charging load to a building energy model, and this measure was integrated 

into a workflow for URBANopt. Additionally, an EnergyPlus measure was written to control an 

EV charging load in a building energy model. The OpenStudio measure is available to be 

publicly used through the BCL.
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Adding an EV Charging Load to a Building Energy Model 

The OpenStudio measure is based on static profiles of power draw for EV charging. The 

profiles were created leveraging output from the EVI-Pro Tool, generated as part of a study of 

projected future EV adoption for the state of California (Bedir et al. 2018). These data are also 

accessible through the California Energy Commission EV Infrastructure Projection Tool (NREL

2018). The work of Bedir et al. leverages results from the 2010–2012 California Household 

Travel Survey. NREL developed the EVI-Pro Tool in collaboration with the California Energy 

Commission. According to the tool’s developers (Wood et al. 2018), “The fundamental 

assumption in EVI-Pro is that consumers prefer charging infrastructure that enables them to 

complete all their travels (based on current vehicle use) while minimizing operating cost.” The 

initial EV analysis focused on light-duty vehicles only. Future analysis could consider 

autonomous electric shuttle vehicles, an “electric roadway” for wireless charging of hotel and 

parking shuttle buses, and high-powered fast charging of mass transit vehicles and other fleets.

EVI-Pro analysis considers both plug-in hybrid electric vehicles (PHEVs) and battery 

electric vehicles (BEVs). EVI-Pro assumes that drivers of PHEVs seek to maximize the portion 

of their vehicle miles traveled that are powered by electricity (eVMT). The charging profiles are 

divided into residential, public, and workplace charging stations. Residential charging represents 

charging at homes or apartment buildings. Public charging represents EV charging at parking for 

retail stores or public buildings. Workplace charging represents charging by employees at a 

workplace. Residential and workplace charging stations were assumed to be Level 1 and Level 2. 

Public building charging stations were assumed to be DC fast chargers. Each of the 103 

buildings modeled for the PSN site was assigned one of these types of charging stations.

Charging Profiles 

The process for generating EV charging profiles is depicted in Figure 2, as well as an 

alternative process for generating inputs to the analysis based on the recently released EVI-Pro 

Lite web tool. Inputs that can be generated from the EVI Pro Lite web tool are shown in orange. 

In this study, separate EV charging profiles were developed for three different day types 

(weekdays, Saturdays, and Sundays) for each of the three locational charging types (residential, 

public, and workplace) at PSN. Building occupancy was used as a proxy for vehicle population. 

Building occupancy was determined based on occupancy schedules from OpenStudio Standards 

used in the building energy models. OpenStudio Standards aggregates information from building 

energy performance standards such as ASHRAE 90.1, as well as standardized modeling inputs 

from sources such as ASHRAE 90.1 Appendix G, for use in OpenStudio energy models, and 

generation of code-compliant baseline models (NREL 2020b). 

The EV charging profiles generated for Peña Station assumed that 50% of the light-duty 

vehicles on the site were plug-in EVs, which is about 10,000 plug-in EVs throughout a typical 

weekday. The profiles assume that PSN residents use residential charging, but not workplace or 

public charging, and that commuters to the site use workplace or public charging, but not 

residential charging. A series of EV charging events on a given day type were generated using 

the EVI-Pro model with travel data from California, and off-site charging events were then 
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discarded. EVI-Pro Lite is a simplified version of EVI-Pro and is available publicly online. EVIPro Lite allows users to estimate the number and type of charging stations required to support a 

given number of EVs in a metropolitan area or state. The data supporting EVI-Pro and EVI-Pro 

Lite enable extensibility of this analysis (NREL 2020c). 

Figure 2. Flow chart depicting process of generating EV charging profiles 

Charging events were assigned to categories of building types (public, workplace, and 

residential), and disaggregated to the individual building level based on building occupancy. EV 

charging profiles from EVI-Pro reflect a given population of EVs of each of several battery 

capacities. The EV charging profiles for PSN reflect the disaggregation by battery capacity 

shown in Table 2. In the abbreviations, the numeric suffix indicates the electric range, in miles.

Table 2. Disaggregation of plug-in electric vehicles at PSN by type

Vehicle type Fraction of EVs at PSN

PHEV20 15%

PHEV50 35%

BEV100 15%

BEV250 35%

EV charging profiles were developed for three different scenarios for charging behavior, and 

three different scenarios for charging flexibility. The charging behavior scenarios are as follows:

• “Business as usual” home dominant charging behavior: The majority of the electrical 

energy consumed by EV charging is attributable to charging of EVs at home during the 

evening hours and overnight. 

• Free workplace charging at PSN: The peak power draw from EV charging on weekdays 

occurs during morning hours, due to EV charging at workplaces. Overnight residential 

EV charging remains a significant share of the total electricity use for EV charging. 

• Free workplace charging across the Denver metro area: Home EV charging is reduced 

relative to the “free workplace charging at PSN” scenario, because residents of PSN who 

work elsewhere can charge their vehicles for free at those workplaces. 
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Plots of the EV charging load on a weekday under each of these scenarios are shown in 

Figure 3. The plots show disaggregation by home charging (Level 1 and Level 2), workplace and 

public charging (Level 1 and Level 2), and DC fast charging. The charging flexibility scenarios 

apply to workplace charging only, and are as follows:

• Minimum delay: EVs begin charging immediately upon arriving at work

• Maximum delay: EVs are plugged in immediately but do not begin charging until necessary

• Minimum power: EVs are charged at minimum rate over the parking event. 

Across the three charging flexibility scenarios, the same amount of energy is delivered 

per charging session. The “minimum power at work” scenario reduces the overall peak EV 

charging load, and the “maximum delay at work” scenario shifts the peak EV charging load to 

later in the day. Plots of the EV charging load on a weekday under the “free workplace charging 

at PSN” scenario, and each of the three charging flexibility scenarios, are shown in Figure 3. The 

vehicle load scenarios considered could be applicable to other campuses and developments. 

 

Figure 3. EV charging load under three charging behavior scenarios and three charging flexibility scenarios

OpenStudio Measure 

As configured for the PSN analysis, the OpenStudio measure selects an EV charging 

power draw profile for a given building and set of conditions (charging behavior and control) 

and adds the EV charging to the building electrical load. For future analysis (beyond the PSN 

project), the measure allows the user to linearly scale the level of EV penetration for a 

generalized profile for the building type (work, retail, or home), which is an average of the 

profiles generated for those building types at PSN. This scaling factor could be used to reflect 

building occupancy levels differing from those assumed in the prototype buildings. The user 

selects a charging flexibility option, a charging behavior option, a building type, and a 

penetration level of EVs (as a percentage) of light-duty vehicles associated with the building to 

configure the measure. The charging flexibility and behavior options are described previously.
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Controlling EV Charging in a Building Energy Model

The EnergyPlus EV charging control measure was written with a workplace charging 

application in mind and allows the user to control charging to better align it with expected solar 

photovoltaic (PV) power generation. This sort of control could be leveraged for demand response 

purposes. The measure uses the EnergyPlus Energy Management System feature and uses sitespecific solar irradiance as a proxy for PV power generation. The measure ensures that vehicles 

are fully charged by the end of the workday. The measure was written as a demonstration for a 

scenario in which EV charging loads are known a priori, with the “baseline” (uncontrolled) EV 

charging load taken as an input. 

For illustrative purposes, the EV charging control measure was applied to the prototype 

office building model available from DOE, with a modified version of the average EV charging 

loads from PSN added to it. DOE publishes “prototype” building models in OpenStudio format 

for a variety of building programs, which are intended to represent typical buildings in the 

United States, and are supported by extensive documentation (DOE 2018). The prototype office 

building model is configured with an occupied schedule of 7 a.m. to 6 p.m. on weekdays. The 

EV charging loads were scaled by a factor of 10 from the PSN load profiles to make the EV load 

a more significant fraction of the overall building load. Note that this prototype building has the 

same occupancy patterns assigned as in the prototype buildings used to generate the occupancy 

data for the PSN project that underly the EV charging loads embedded in the measure. Figure 4

shows the effects of load-shifting on the building’s overall load profile and on the EV load 

profile during a cloudy day in May. The EV charging load is reduced during the hours of 8 a.m. 

to 3 p.m., and is increased from 3:30 p.m. to 6 p.m., better aligning its consumption with solar 

irradiance, and thus, solar PV power generation.

The measure implements the following sequence of operations on weekdays, with 

PPEEEE_SSSSheeee = SSSSheeeeeeeeeeee pppppp pp dd oo EEEE h aaaaaaaa

PPEEEE = AA AA pppppp pp dd oo EEEE h aaaaaaaa

SS DDDD = Demand response signal, between 0 and 1

• If time is between 8 a.m. and 3 p.m.:

o If solar irradiance is below 100 W/m2, set demand response signal to 1.0. 

o If solar irradiance is between 100 W/m2 and 250 W/m2, set SS DDDD proportionately 

between 1 and 0. 

• Based on the demand response signal: 

Set PPEEEE =(1 − SS DDDD) ∗ 0.75 ∗ PPEEEE,SSSSheeee,

constrained by a minimum value of 100W for PPEEEE, based on the minimum turn-down 

capabilities of EV chargers.

• If the time is 3 p.m.:
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o Reset SS DDDD 0. Increase the EV charging load proportionately to the amount of 

energy curtailed, and the remaining hours until 6 p.m., to ensure that all vehicles 

are fully charged by 6 p.m.

The EV charging load present at PSN office buildings on weekends is minimal, and thus no 

control of EV charging load is performed on weekends. 

Figure 4. Effects of load shifting on EV charging profile and whole-building load profile 

For more general analysis for EVSE infrastructure planning in districts, this workflow 

allows energy planners to scale the level of EV user penetration for a generalized profile for the 

building type (work, retail, or home) across common demand management control strategies. 

This workflow, when integrated with the building energy modeling analysis for the district, 

enables EV infrastructure planning to be included in the full district energy analysis and 

feasibility studies.

Managed Charging at the Workplace—A Case Study

NREL’s living laboratory infrastructure enables its campuses to be utilized as a research 

instrument to study integration of renewable energy and energy efficiency and address a range of 

its own operational needs. The parking facilities are no exception. In addition to fulfilling their

primary function, the parking facilities at NREL’s South Table Mountain and Flatiron campuses 

provide the opportunity for NREL researchers and its partners to test the impact of various plugin EV charging scenarios on the electrical distribution network, to test vehicle-to-building energy 

control integration, and to gain understanding of charging behaviors for valuing and developing 

charge scheduling programs, all of interest to utilities, businesses, and agencies. In 2018, NREL 

partnered with PowerFlex Systems to evaluate the adaptive charging capabilities for a 50-kW 

DC fast charger integrated with 16 L2 demand-managed workplace charging stations. The 

adaptive charging approach was combined with building load and demand profile inputs to 

Load shifting from midday to end of day
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enable building demand charge mitigation and electrical infrastructure cost reductions due to 

managed EV-to-building integration. As documented recently by Bernal et al. (2020), the 

adaptive charging strategy for this initial deployment reduced the cost of the building’s electrical 

equipment by 48% and 42% for DCFC plus L2s and only L2s, respectively. In addition, building 

peak demand savings were documented, with varied savings ranges based on research constraints 

and magnitude of EV loads versus building loads. 

More recently, NREL adapted these research activities into its Golden, Colorado, main 

campus site operations by implementing a large-scale workplace charging system with load 

control strategies, utilizing the adaptive charging system to engage more than 250 daily EV 

commuters. All 124 of NREL’s 6.6-kW Level 2 EV charging stations available to staff and 

visitors for recharging personal EVs are networked and “smart.” The Adaptive Charging 

Network (ACN) system operates the EVSEs, providing NREL the ability to control access, 

collect payment, and manage electrical load as part of an intelligent campus energy strategy. A 

team of both researchers and operations staff manages the system, determining how to balance 

the load to mitigate peak demand fees for electricity and optimize campus energy consumption.

The layout for the EVSEs supports the design of ACN load management and control 

system and minimizes NREL’s electrical infrastructure costs by approximately 50%. This design 

required less electrical infrastructure, allowing for automatic sharing of the limited transformer 

capacity powering the EVSEs to be rated below the peak rating of the EVSEs (to as low as 3.3 

kW/EVSE in some locations). Figure 5 shows the scenarios that feature load management 

technology where labor and material savings were realized due to the smaller sizing of 

equipment, wiring ratings, and reduced installation time. Key capital costs savings included 

downsizing from a 150-kVA to a 75-kVa transformer, and utilizing existing space in the building 

main distribution panel (which enables not needing a building-wide electrical shutdown or 

upgrading the building electrical service and minimizing the distance of the electrical runs).

In addition to the transformer limitations, the EVSEs are managed to reduce total 

campus load during peak demand events on campus. The ACN system collects user inputs 

through use of an app at the initiation of each charge session. These inputs, including the number 

of miles of energy requested and the estimated time of departure, are aggregated and integrated 

within the load management control system to optimize delivery of the requested energy using 

real-time campus load data and the total energy requests from the EVSEs.
13 

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Figure 5. Electrical infrastructure sizing example with and without load management for a DC fast charger and a bay 

of Level 2 charging stations. Source: Bernal et al. 2020

NREL is required to recover costs for the electricity and annual operations and 

maintenance of the stations from users based on federal guidance contained in the Fixing 

America’s Surface Transportation Act. The ACN user app enables the communication between 

the driver, the EVSE, and the load management control system to meet this requirement, as 

shown in Figure 6. Users pay a per-kilowatt hour rate for their charge session. NREL created a 

variable pricing structure that aims to incentivize user behavior by setting rates for each kilowatt 

hour depending on the amount of flexibility the charge request provides to NREL. Higher rates 

are assigned for less flexible requests. The variable pricing structure allows NREL to more 

equitably manage the range of EV charging requests based on the potential risk the request 

presents to NREL of exceeding the capacity of the transformers and of incurring demand charges 

from the load of the EVSEs during peak campus demand events.
14 

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

 

Figure 6. User input example for workplace charging management

Figure 7. Workplace-managed charging peak demand savings 

Figure 7 demonstrates an example day of how this load flexibility across 100+ L2 

workplace charging stations can be redistributed to off-peak time windows and aligned with 

available PV generation. Using the NREL pricing structure, the ACN user app estimates total 

costs for the charge session. The ACN system then controls the EVSE and delivers the requested 

energy within the user requested timeframe and charges the user for that session once completed. 

Potential Future Work and Conclusions

The EV charging control approach could be refined in future work. The control logic 

could be configured to seek to control the EV charging load based on a deterministic schedule, or 

to achieve a desired “net load” profile for the building, where the net load is the difference 
15

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

between the building’s total load and on-site power generation. The approach could be modified 

for cases in which the EV charging load is not already known, though this would introduce 

uncertainty around whether all vehicles would be fully charged by the end of the workday. The 

modeling approach could also be extended to generate EV charging load profiles based on user 

inputs and building-specific parameters, instead of using static profiles generated based on 

higher-level characteristics, leveraging EVI-Pro or other sources. 

Across the United States, there is a growing demand for EV charging infrastructure, 

especially in office, retail, and multifamily buildings, and building designers are now being 

asked to consider the electrical infrastructure, peak demand implications, and the greater grid 

integration and control options. In this paper, we documented methods to design and understand 

peak demand and grid coordination EV charging control strategies by developing energy 

modeling approaches that enable the design optimization and building impacts of uncontrolled 

and demand-managed charge control approaches. In addition, a full-scale example of an ACN is 

presented, including the design approach, control approach, and user interface for a workplacemanaged charging solution. The case study presented demonstrates the opportunity to minimize 

office building peak demands and maximize self-consumption of a variable PV resource.

Acknowledgments

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for 

Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-

08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and 

Renewable Energy Building Technologies Office. The views expressed herein do not necessarily 

represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, 

by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, 

paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow 

others to do so, for U.S. Government purposes.

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