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Contract No. DE-AC36-08GO28308

Hardware-in-the-Loop (HIL) 

Simulations for Smart Grid 

Impact Studies

Preprint

Bethany Sparn, Dheepak Krishnamurthy, 

Annabelle Pratt, and Mark Ruth

National Renewable Energy Laboratory

Hongyu Wu

Kansas State University

Presented at the IEEE Power and Energy Society General Meeting

Portland, Oregon

August 5-9, 2018

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Suggested Citation

Sparn, Bethany, Dheepak Krishnamurthy, Annabelle Pratt, Mark Ruth, and 

Hongyu Wu. 2018. “Hardware-in-the-Loop (HIL) simulations for Smart Grid 

Impact Studies.” Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5500-70409. https://www.nrel.gov/docs/fy18osti/70409.pdf

Conference Paper

NREL/CP-5500-70409

August 2018
NOTICE

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. This work

was supported by the Laboratory Directed Research and Development (LDRD) Program at NREL. The views 

expressed in the article 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.

This report is available at no cost from the National Renewable 

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

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NREL prints on paper that contains recycled content.
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Hardware-in-the-Loop (HIL) Simulations for Smart 

Grid Impact Studies

Bethany Sparn, Dheepak Krishnamurthy,

Annabelle Pratt, Mark Ruth

National Renewable Energy Laboratory

Golden, CO USA

bethany.sparn@nrel.gov

Hongyu Wu

Kansas State University

Manhattan, KS USA

Abstract— Hardware-in-the-loop (HIL) simulations are

increasingly employed in power engineering as more inverterbased generation and smart appliances are connected to the 

electric grid. HIL techniques allow for the co-simulation of 

analytical models with actual devices whose complex behavior is 

computationally inefficient or difficult to model. They also allow 

for testing the behavior of these devices under adverse conditions 

that rarely occur in the field but are important to evaluate. This 

paper provides an overview of how HIL simulations have been 

used to date and proposes that HIL simulations should play an 

important role in evaluating new control strategies, especially at 

the distribution level, that are being proposed to allow for 

continued affordable and reliable operation of the electric grid. 

We present a new capability that was developed to evaluate the

interactions between residential loads and the smart grid: smart 

home hardware-in-the-loop. The paper includes results from an 

HIL experiment that incorporates multiple technologies and 

controls.

Index Terms-- Demand response, hardware-in-the-loop (HIL) 

simulation, home energy management system (HEMS), power 

system simulation, smart grids.

I. INTRODUCTION

Hardware-in-the-loop (HIL) simulations have a long 

history, with applications extending to a number of disciplines. 

An HIL simulation is a method for including pieces of hardware 

in a real-time simulation to improve the fidelity of the

simulation results. It is especially valuable when there are 

system components that are difficult to model. HIL simulations

are also used to evaluate the performance of hardware, 

especially when it is important to test the physical devices

against rare conditions or when it is impractical to reproduce

the full physical system in a laboratory.

Early applications of HIL simulations were in the aerospace 

industry, when flight simulators were developed to train pilots 

on cockpit hardware without incurring any of the risks

associated with flying [1]. The automotive industry has used 

HIL simulations extensively in the development of antilock

braking systems, traction control, and electronic control units 

[2].

Recently, HIL simulations have come into use in the field 

of power systems [3]. HIL simulations are commonly used to 

study renewable energy integration as inverter-driven 

generation and smart appliances have become more common

[4]-[6]. The algorithms required to control these devices need 

to be developed and tested under realistic conditions but in a 

repeatable, controlled environment. HIL simulations enable 

algorithm development by simulating some components (such 

as solar insolation or grid conditions) while allowing the power 

hardware to respond and repeating the process to test the impact 

of changes in the controls. This method is also applicable to the 

development of controls for smart appliances [7]. Residential 

appliances can be controlled in a variety of ways to achieve cost 

savings, to consume more local renewable energy, or to 

mitigate congestion on the grid, and HIL simulations could 

allow these control strategies to be refined and their impact on 

the grid to be evaluated [8].

In this paper, we present different HIL configurations used 

in power systems research and a new application of HIL 

developed at NREL to investigate the interaction between 

residential loads and the smart grid. Section II describes the HIL 

configurations, section III details the experimental approach, 

section IV presents results, and section V covers conclusions 

and future work.

II. HIL SIMULATION CONFIGURATIONS

The basic concept for HIL simulations can be applied in 

different ways to study different systems. Some common 

applications of HIL simulations in power engineering are 

presented in this section, listed in order of increasing 

complexity, building to a new capability designed to evaluate

interactions between residential loads and the smart grid: smart 

home hardware-in-the-loop (SHIL).

A. Controller Hardware-in-the-Loop

The simplest form of HIL is called controller hardware-inthe-loop (CHIL), and it allows a physical controller to be 

evaluated with simulated inputs, as shown in Figure 1a. Many 

types of controllers can be evaluated in this way, including 

embedded controllers for inverters, capacitor banks or electric 

vehicles, and stand-alone controllers such as smart thermostats.

A power system simulation, running on a digital real-time 

simulator (DRTS), is configured using grid and weather 

conditions to provide realistic simulated input data to the 

controller hardware on a timescale from microseconds (for 

electromagnetic transient simulations) to milliseconds (for 

dynamic phasor simulations). The output signals from the 

This work was supported by the U.S. Department of Energy under Contract 

No. DE-AC36-08-GO28308 with Alliance for Sustainable Energy, the 

Manager and Operator of the National Renewable Energy Laboratory (NREL) 

and by a Laboratory Directed Research and Development Program at NREL.

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.
2

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

controller hardware are fed back to the power system 

simulation in real time.

The simulation is updated based on feedback from the 

controller hardware, which in turn updates the signals sent back 

to the control hardware [6]. The input and output signals for the 

controller can be analog or digital. For a controller that outputs 

analog signals, an analog-to-digital converter (ADC) internal to 

the DRTS converts the analog signal to a digital input for the 

simulation, and a digital-to-analog converter (DAC) converts 

the digital signal from the simulation to an analog signal that is 

provided to the controller hardware [4].

B. Power Hardware-in-the-Loop

For power hardware-in-the-loop (PHIL) simulations, power 

hardware is interfaced with a power system simulation executed 

on a DRTS, as shown in Figure 1b. This allows the power 

hardware, including its embedded control, to be tested under 

realistic but simulated conditions [3], [4], [6]. The power 

hardware needs to be supplied with operational power (AC 

power, DC power, or both) that is consistent with conditions in 

the power system simulation. For example, a bidirectional AC 

power amplifier, also referred to as a grid simulator, is used to 

supply the AC power required by the power hardware, based on 

a voltage signal from the power system simulation. Sensors 

measure the AC current supplied by the grid simulator and 

return that data to the power system simulation as feedback, 

thereby closing the loop. To ensure a stable and accurate PHIL 

simulation, compensation needs to be added to the loop, as 

described in [9]. Similarly, a DC power amplifier can be 

controlled to deliver DC power consistent with the power 

system simulation when necessary.

An experiment with a photovoltaic (PV) inverter is an 

example of a PHIL configuration with both AC and DC power 

connections. A PV inverter is connected to a DC amplifier, also 

known as a PV simulator, and to a grid simulator. The PV 

simulator is configured with models for PV panels and can be 

controlled with solar insolation data stored in the PV simulator

or it can be controlled from the DRTS. The latter option is 

preferred to synchronize the simulation. The inverter operates

as if it were installed with solar panels and connected to a utility

grid, while allowing researchers to explore its behavior under a 

wide range of conditions. This technique is more realistic than 

simulation only because models that capture the full range of 

realistic behavior may not exist, and it allows proprietary 

systems to be tested.

Figure 1. Different configurations of HIL: (a) CHIL and (b) PHIL.

C. PHIL with Multi-Timescale Co-Simulation

For PHIL simulations of large power systems that exceed 

the capacity of the DRTS, a quasi-steady-state time-series 

(QSTS) simulation tool, such as GridLAB-D or OpenDSS, can 

be used to simulate the power system. The QSTS power system 

simulation can generally run on a PC or workstation. The 

DRTS is used to convert the voltage phasor data to timedomain waveforms required to control the grid simulator, as 

shown in Figure 2a. For example, testing two residential PV

inverters at separate points of common coupling (PCCs) to the

IEEE 8500-node test distribution feeder simulated in

GridLAB-D PHIL with multi-timescale co-simulation [10]

Figure 2. More complex HIL configurations: (a) PHIL with multi-timescale 

simulation (b) PHIL + thermal HIL.

D. PHIL + Thermal HIL

This HIL configuration combines PHIL with a thermal HIL

component, as shown in Figure 2b. The power hardware is an 

air conditioner (A/C) with a thermostat as the controller. To recreate realistic operation of the A/C, a thermal HIL system was 

designed to pair a building simulation with the thermostat and 

A/C hardware. The building simulation uses EnergyPlus 

software to determine the indoor temperature based on 

weather, building construction, internal loads, and operation of 

the A/C [8]. The outdoor component of the A/C is located in 

an environmental chamber (EC) that is controlled to match the 

outdoor temperature from the weather file. The indoor 

component of the A/C is part of a duct loop that imposes the 

correct heat load on the A/C based on the modeled return air 

temperature in the building simulation. The thermostat is 

located inside a second EC controlled to match the indoor 

temperature from the building simulation. When cooling is 

needed, the thermostat turns the A/C on, and cooling capacity 

data is passed to the building simulation to inform the indoor 

temperature calculation.

The measured electrical load is applied to the power system 

simulation, either within the DRTS or a QSTS simulation. A

co-simulation coordinator (CSC) can be employed to manage

the flow of signals between the DRTS, power system and 
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building simulations [11]. In [8], results are presented from an 

experiment in which the power system simulation was a 

GridLAB-D simulation of a feeder with multiple houses. The 

A/C load for one house in the GridLAB-D simulation was 

replaced with measurements taken from the A/C hardware. In 

turn, the grid simulator supplying power to the A/C hardware 

was controlled so that the voltage supplied to the hardware 

matched the voltage calculated by the GridLAB-D simulation. 

The DRTS was used to convert the voltage phasor data to a 

time-domain waveform signal.

E. Smart Home Hardware-in-the-Loop

We have designed and implemented a setup that 

incorporates PHIL, CHIL, and thermal HIL with multitimescale simulations, as shown in Figure 3. This capability 

was developed to evaluate smart appliances and smart home 

control strategies under multiple utility tariff structures and to

quantify the impact of those controls on the power distribution 

system. This HIL configuration intends to represent all 

relevant components of a home in hardware, so this 

configuration is called Smart Home Hardware-in-the-Loop 

(SHIL). The power system simulation is a GridLAB-D model 

that includes 20 homes on the IEEE 13-node feeder, with the 

load for one of those homes replaced by the residential 

hardware in the laboratory. Each home has multiple 

controllable appliances and a dedicated home energy 

management system (HEMS) controller that determines the

optimal operation of each controllable appliance.

In this instantiation, the controllable appliances in the 

homes are an air conditioner, an electric water heater (EWH),

and electric vehicle supply equipment (EVSE). The current 

version of this system also includes an uncontrolled PV 

inverter, but future iterations will include a controllable PV 

inverter. Uncontrolled appliances, such as a refrigerator and 

lights, are also included in the laboratory home. Each home has 

a separate HEMS controller that executes a multi-objective 

optimization within a model predictive control (MPC) 

framework [12] to optimize appliance operations. It sends

control signals to either simulated appliances within GridLABD or to physical appliances via Wi-Fi or physical signal wire 

in the laboratory home. The A/C and EWH are controlled by 

changing the associated set point temperature for the physical 

appliances. Control of the A/C hardware is implemented via an 

internet-connected thermostat, and the water heater has a 

custom controller that was also internet-connected. The 

charging rate for the electric vehicle (EV) was controlled by 

changing the duty cycle of the pulse-width modulation signal 

from the EVSE, a feature enabled by the supported CTA-2045 

protocol. The SHIL setup therefore includes a variety of 

appliances that we can operate either with their built-in 

controls, or with advanced controls implemented at the 

appliance level or by a supervisory controller such as a HEMS. 

This enables us to evaluate the impact of different control 

strategies.

In order to run multiple HEMS optimizations, 

parallelization is required; thus they are executed on a highperformance computer (HPC). The CSC used with the SHIL 

simulation is an NREL-developed framework called the 

Integrated Energy System Model (IESM), which was designed 

to manage timing and data exchange among the GridLAB-D 

model, the HEMS optimizations, the control signals for the 

power hardware, and the phasor-to-waveform conversion 

running on a DRTS [8], [11]. In addition to sending a control 

signal to the grid simulator, the DRTS also sends insolation 

and temperature signals to the PV simulator to drive the PV 

inverter throughout the experiment.

Figure 3. SHIL includes PHIL, CHIL and thermal HIL.

Voltage variations can impact power consumption or can 

impact the current flow if the device is designed to maintain a 

constant power draw. In both cases, providing power to the 

appliances at the voltage determined by the power system 

simulation (closing the loop on the HIL simulation) is 

important for evaluating the full impact of HEMS.

III. SHIL SIMULATION APPROACH

We present results from two SHIL experiments that were 

performed to compare behavior with and without HEMS, each 

lasting two days. These results demonstrate both realistic 

operation of individual hardware appliances and the ability to 

perform analyses based on results from both simulated and 

hardware appliances.

A. Simulation Parameters

The experiments simulated two days in early June using

typical meteorological year (TMY) weather data from North 

Carolina to inform the outdoor air temperature and solar 

insolation for the GridLAB-D and EnergyPlus simulations. A 

time-of-use (TOU) tariff schedule from Duke Energy in North 

Carolina was used to calculate the cost of electricity. The TOU 

tariff had a peak price between 1 p.m. - 6 p.m. and shoulder 

rates for two hours before and after the peak [8].
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The baseline experiment did not use the HEMS to 

dynamically change the operation of the controlled devices; 

the operation of the appliances was determined by a fixed 

schedule. The set points for the water heaters were constant at

119°F. The air conditioners were controlled with a simple 

setback schedule: 76°F during the day (8 a.m.–5 p.m.) and 

72°F at other times. The EVs had a charging window between 

6 p.m. and 8 a.m. to simulate a typical daily schedule, which 

was imposed by using a charging schedule in the vehicle.

When the HEMS control was applied to the homes, the 

operating schedules were adjusted to find an optimal trade-off

between electricity cost savings and occupant discomfort

(within prescribed comfort bounds) [12]. For this experiment,

optimizations were performed and new control signals sent to 

the appliances every 15 minutes. The A/C and water heater set 

points were allowed to vary up to ±5°F from the base schedule, 

and the EV was allowed to charge anytime between 6 p.m. and 

8 a.m., as long as it reached full charge by 8 a.m.

For both cases, the appliances were subjected to simulated 

use to ensure that their operation was consistent with 

appliances in an occupied home. The air conditioner was 

driven with the thermal HIL simulation described in section

II.D and [8]. The building simulation incorporated the impact 

of weather, solar insolation, and internal gains on the interior 

air temperature, which ensured proper cycling of the air 

conditioner. A water draw schedule was imposed on the 50-

gallon water heater using a solenoid valve—which was

controlled by the laboratory data acquisition and control 

system—connected to the hot water line on the kitchen sink. 

This schedule used an average of 51 gallons per day and was 

developed using a hot water draw generation tool developed at 

NREL [13]. A Nissan Leaf EV was charged using the 

controllable EVSE during the night, but it was not feasible to 

drive the Leaf during the day to drain the battery. Instead, the 

A/C for the car was turned on at its highest level and all the 

seat heaters were turned on for several hours each day. This 

drained the battery an amount equal to driving approximately 

35 miles per day, which is consistent with the average daily 

driving distance of 36 miles for Americans, per [14].

IV. RESULTS

The following plots show the behavior that was observed. 

In all figures, the purple lines correspond to the baseline 

results, and the green lines correspond to results from when the 

HEMS controls were applied. The vertical pink bands indicate 

the price according to the TOU schedule: the darker band 

indicates the peak price period and lighter bands show the 

duration of the shoulder prices.

To avoid running the A/C during the peak price period, the 

HEMS raised the set point toward the end of the high price 

periods (as indicated in Figure 4 by the higher indoor 

temperature under HEMS control) and then lowered the set 

point once the price dropped (indoor temperature drops after 

price drops). Similarly, the HEMS sent a lower A/C set point 

to precool the house before the price increased (A/C runs and 

temperature drops before peak price period). However, the 

A/C needed to operate for a minimal amount of time during the 

peak price period to meet the comfort requirements. The water 

heater set point was mostly set to the lowest allowable 

temperature, as can be seen in Figure 5. The tank temperature 

in the HEMS case is generally lower than the baseline. 

However, the EWH set point was raised before the electricity 

price increased to ensure that hot water was available through 

the peak period. The charge rate signal, which sets the 

maximum charge rate for the EVSE, and the actual power 

consumption of the EVSE are shown in Figure 6.

Figure 4. Comparison of temperature and power data from the air conditioner

hardware during two days under baseline control and HEMS control. The 

dashed blue lines indicate the bounds on the set point temperature.

Figure 5. Comparison of tank temperature and power data from the water 

heater hardware during two days under baseline control and HEMS control.

Figure 6. Comparison of charge rate set point and power data for the EVSE

hardware during two days under baseline control and HEMS control.
5

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Under HEMS control, charging of the EVSE was delayed 

until after the utility rate dropped to its lowest price. The 

charge rate for the EVSE under HEMS exhibited pulsing 

behavior because battery degradation was not taken into 

account in the HEMS optimization; future work will include 

battery degradation in the optimization, based on a 

linearization of the approach described in [15].

For the house represented in hardware, these controls 

resulted in 4.6 kWh or 8% energy savings during two days and 

14% cost savings; however, there was an adverse aggregate 

impact on feeder voltage, with the GridLAB-D simulation 

showing slightly more variation from the nominal voltage 

when the HEMS controls were applied. In the baseline case, 

the largest voltage deviation from nominal (120 V) was 0.75 

V, or 0.6%; whereas the HEMS controls resulted in 2.0 V, or 

1.6% deviation from nominal.

Another way to view the aggregated impact of the HEMS 

is shown in Figure 7. The top plot shows the average indoor 

temperature from all the homes (simulated and hardware) for 

the HEMS and baseline cases. The blue line indicates the 

desired set point. The lower plot shows the total power from 

all the homes for both cases. The baseline case shows a higher 

indoor temperature at the beginning of the peak period because 

no precooling took place, which causes significant cooling at 

the end of the peak period after the set point decreases. The 

HEMS case shows a lower temperature earlier in the day 

because precooling was used to avoid the need for cooling 

during the peak price period.

V. CONCLUSIONS AND FUTURE WORK

Building on established CHIL and PHIL techniques, the 

SHIL system is a robust, powerful tool for evaluating smart 

home technologies and how they impact the grid and its 

consumers. The ability to customize the feeder model, utility 

tariff structure, building models and weather data means that 

this test bed can be used to simulate smart grid systems from 

different regions. In this paper, we presented results from 

simulations that compare behavior with and without HEMS;

however, the SHIL capability will accommodate a wide range 

of experiments that could help inform utility rate designs and 

policies, customization of HEMS algorithms, and research and 

development related to smart appliances and other DERs.

Figure 7. Summary of indoor air temperature and real power consumption for 

all 20 homes on the simulated GridLAB-D feeder.

In future work we will incorporate new HEMS algorithms 

and apply this framework to homes in different climate regions 

with different feeders, including some that exhibit more 

voltage variability. We will develop more complex residential 

appliance and building models for GridLAB-D to improve the 

accuracy of HIL simulations and larger scale simulation-only 

studies. We also plan to add an aggregator module that can 

coordinate HEMS through transactive energy and other 

techniques to flatten the aggregated load shape and avoid

unintended coincident operation.

ACKNOWLEDGMENTS

The authors gratefully acknowledge the contributions from 

Monte Lunacek, Wesley Jones, Saurabh Mittal, Jon Winkler, 

Jason Woods, Paulo Tabares-Velasco, Matt Eash, Kristen 

Munch, Jesse Marks, and Paul Vaynshenk.

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