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Original filename: Evaluation of a 1 MW, 250 kW-hr Battery Energy Storage System for Grid Services for the Island of Hawaii.pdf
Title: Evaluation of a 1 MW, 250 kW-hr Battery Energy Storage System for Grid Services for the Island of Hawaii
Author: Karl Stein, Moe Tun, Keith Musser and Richard Rocheleau

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energies
Article

Evaluation of a 1 MW, 250 kW-hr Battery Energy
Storage System for Grid Services for the Island
of Hawaii
Karl Stein 1 , Moe Tun 2 , Keith Musser 3 and Richard Rocheleau 4, *
1
2
3
4

*

Center for Climate Physics, Institute for Basic Science (IBS), Busan 46241, Korea; kjstein@gmail.com
HNU Photonics LLC, Kahului, HI 96732, USA; moetunhawaii@gmail.com
Integrated Dynamics, Inc., Fishers, IN 46037, USA; kmusser@idi-software.com
Hawai‘i Natural Energy Institute, SOEST, University of Hawaii at M¯anoa, Honolulu, HI 96822, USA
Correspondence: rochelea@hawaii.edu

Received: 27 October 2018; Accepted: 22 November 2018; Published: 1 December 2018




Abstract: Battery energy storage systems (BESSs) are being deployed on electrical grids in significant
numbers to provide fast-response services. These systems are normally procured by the end user,
such as a utility grid owner or independent power producer. This paper introduces a novel research
project in which a research institution has purchased a 1 MW BESS and turned ownership over to a
utility company under an agreement that allowed the institution to perform experimentation and
data collection on the grid for a multi-year period. This arrangement, along with protocols governing
experimentation, has created a unique research opportunity to actively and systematically test the
impact of a BESS on a live island grid. The 2012 installation and commissioning of the BESS was
facilitated by a partnership between the Hawaii Natural Energy Institute (HNEI) and the utility
owner, the Hawaiian Electric and Light Company (HELCO). After the test period ended, HELCO
continued to allow data collection (including health testing). In 2018, after 8500 equivalent cycles, the
BESS continues to operate within specifications. HNEI continues to provide HELCO with expertise
to aid with diagnostics as needed. Details about the BESS design, installation, experimental protocols,
initial results, and lessons learned are presented in this paper.
Keywords: battery energy storage system; field evaluation; wind smoothing; frequency regulation;
grid-scale; lithium-titanate

1. Introduction
At high penetration levels, the integration of intermittent renewable energy sources poses several
challenges for grid operations, due, in part, to the variability of renewable energy sources, but also
due to the reduction of system inertia via the displacement of traditional dispatchable generation.
These factors can result in a reduction in grid stability and reliability, manifesting in such ways as
increased frequency variability, voltage transients, and power quality reduction [1–3]. Such effects are
magnified on small island grids such as those on the Hawaiian islands [4]. The geographic isolation of
the islands’ electricity grids and the rapid growth of renewable generation make the Hawaiian grids
particularly susceptible to the adverse effects of variable renewable energy sources, but also an ideal
test-bed for various emerging grid solutions, including energy storage. With already high penetration
of wind and solar generation, recently passed legislation set a goal of 100% renewable energy by the
year 2045 for the state [5] and recent utility planning ensures that the islands’ grids will see increasing
renewable energy penetration in the coming years. Similar challenges and solutions are also being
addressed on other island grids [6–10].

Energies 2018, 11, 3367; doi:10.3390/en11123367

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Fast-acting battery energy storage systems (BESSs) show promise in mitigating many of the effects
of high renewable energy penetration levels [11–15]. Despite substantial numbers of deployments
worldwide, few studies have reported results of grid-connected systems [16–19]. This paper presents
a unique field test of a BESS operating on an island electric grid which was made possible by a
partnership between the Hawaii Natural Energy Institute (HNEI), at the University of Hawaii at
M¯anoa, and the utility grid operator, the Hawaii Electric Light Company (HELCO, Hilo, HI, USA).
In accordance with the agreement, HNEI purchased the BESS, then relinquished ownership to HELCO,
but was allowed to perform experiments with the BESS while it was operating on the electric grid.
The experiments involved switching the BESS on and off every 20 min. This switching forms both an
independent and dependent variable over a relatively short time (minimizing changes in background
conditions). This allowed for much more direct measurements of the effect of the BESS on the island
grid than would be possible with data from long-term monitoring of the grid before and after BESS
installation. To the best of our knowledge, no other BESS research projects involve a multi-year
agreement that allows for robust scientific experimentation.
The work presented here is part of a larger research program at HNEI, which includes assessment
of battery performance on the grid, optimization of control algorithms to maximize grid support while
minimizing battery cycling [20], and field and laboratory testing of cells to better understand cell
aging and degradation [21,22]. This paper focuses on the grid support performance aspect, along with
initial testing and methodology. The paper describes Hawaii Island BESS project including project
development and installation (Section 2), development of the control algorithms, acceptance test
results, and initial performance tests (Section 3) followed by concluding remarks and the plans for
future work (Section 4).
2. BESS Development and Installation
The investigation into use of a BESS system for the island of Hawaii was initiated as a result
of a 2009 analysis of that electrical grid performed by General Electric (GE, Schenectady, NY, USA)
under contract to HNEI. The study utilized the Positive Sequence Load Flow (PSLF) power system
analysis software and historical data to model the frequency response of the Hawaii island grid to
rapid changes in wind generation and other contingency events. At the time of the study, the electric
grid, operated by the Hawaii Electric Light Company (HELCO), had a peak load of approximately
180 MW and a wind capacity of approximately 32 MW. While there has been rapid growth since, there
was negligible photovoltaics on the system at that time. The models indicated that as little as 1 MW
of short-duration fast-acting energy could significantly reduce the severity and duration of the grid
frequency events.
These results prompted HNEI to develop a research project to procure and evaluate a 1 MW
grid-connected BESS. HNEI and the grid owner, HELCO, reached a mutually beneficial memorandum
of understanding that would facilitate the realization of this research project (and address any liability
issue). In accordance with the agreement, HNEI procured the BESS, and transferred ownership over
to HELCO immediately after commissioning. The agreement further stipulated that HELCO would
provide HNEI with the ability to perform experiments on the grid (provided adherence to protocol
that was agreed upon by both parties). A lithium ion titanate battery chemistry was chosen due to
the desire for an extended cycling lifetime and faster charge/discharge rates compared to the more
common carbon anode electrochemistry [23].
HNEI assembled a public-private partnership (Table 1) for BESS development, installation,
and testing. The key tasks included: site preparation, material procurement, electrical design,
communications design, and algorithm development. Procurement of balance of plant (BOP) materials
(e.g., transformers, meters, and compatible communications devices) was undertaken by HELCO,
Altairnano (Anderson, IN, USA), and HNEI. The site preparation was conducted jointly by Altairnano
and the installation site land owner, Haw’i Renewable Development (HRD, Upolu Point, HI, USA),
which owns a collocated 10.6 MW wind farm. The electrical and communications systems were

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Dynamics Inc. (IDI, Fishers, IN, USA) and SCADA Solutions Inc. (Irvine, CA, USA). HNEI provided
designed management
by HELCO and
Altairnano.
Algorithmindevelopment
wasdevelopment
performed byeffort.
Integrated Dynamics
program
and
technical oversight
all areas of the
Inc. (IDI,
Fishers,
IN,
USA)
and
SCADA
Solutions
Inc.
(Irvine,
CA,
USA).
HNEI
program
The BESS was installed at a transmission level site on the grid, at the point of provided
common coupling
management
and
oversight
in all
areas
of the
development
effort.
(PCC)
between
thetechnical
HRD wind
farm and
the
nearby
Waimea
substation.
Site construction started in
The
BESS
was
installed
at
a
transmission
level
site
on
the
grid,
at
the
point
of common
coupling
April of 2012 and the BESS was commissioned in December of 2012. The
BESS
was designed
for
(PCC)
between
the
HRD
wind
farm
and
the
nearby
Waimea
substation.
Site
construction
started
interconnection to electrical power systems (as defined by ANSI C84.12006), and to be compliant
with
in April ofstandards
2012 and the
BESS
was commissioned
in December
of 2012. The BESS
was
designed
for
applicable
during
discharge
(IEEE 15472003)
and standby/charging
modes
(IEEE
519) [24].
interconnection to electrical power systems (as defined by ANSI C84.12006), and to be compliant with
applicable
during
discharge
(IEEEfor
15472003)
and standby/charging
modes
(IEEE
519) [24].
Table 1.standards
List of funding
sources
and partners
Hawaii Battery
Energy Storage Systems
(BESS)
project.
Project
Management
Table 1. List of funding sources and partners for Hawaii
Battery
Energy Storage Systems (BESS) project.
Hawaii Natural Energy Institute (HNEI)
Algorithm Development
Technical
Oversight
Project Management
Hawaii Natural Energy Institute (HNEI)
Algorithm
Development
Primary
Funding
Source
Office of Naval Research (ONR)
Technical
Oversight
Program
Oversight
Primary Funding Source
Office ofof
Naval
Research
Department
Energy
(DOE) (ONR)
Partial
Funding
for Algorithm Development
Program
Oversight
Infrastructure
Development
Department of Energy (DOE)
Partial Funding for Algorithm Development
Hawaii Electric Light Company (HELCO)
Planning
Infrastructure Development
Hawaii Electric Light Company (HELCO)
Planning
Grid
Management
Grid
Management
Host Site
Owner
Haw’i Renewable Development (HRD)
Host Site Owner
Site Preparation
Haw’i Renewable Development (HRD)
Site Preparation
Battery
BatterySystem
SystemManufacturer
Manufacturer
Altairnano
Altairnano
Systems
Integration
Systems Integration
FrequencyAlgorithm
AlgorithmDevelopment
Development
Frequency
Integrated
Dynamics,
Inc. (IDI)
Integrated
Dynamics,
Inc. (IDI)
SoftwareDevelopment
Development
Software
SCADA Solutions
Wind Algorithm Development
SCADA Solutions
Wind Algorithm Development
Parker–Hannifin Company
Power Conversion System Supplier
Parker–Hannifin Company
Power Conversion System Supplier

The system
system is
is housed
housed in
in two
two containers,
containers, one
one for
for the
the power
power module
module (PM,
(PM, Figure
Figure 1a)
1a) and
and aa second
second
The
for the
the power
power conversion
conversion system
system(PCS,
(PCS,Figure
Figure1b).
1b).The
ThePM
PMcontains
containsthe
thebattery
batterystack,
stack,along
along
with
for
with
a
a
ground
fault
detection
system,
a
fire
suppression
system,
HVAC
temperature
control
system,
the
ground fault detection system, a fire suppression system, HVAC temperature control system, the
battery management
(SDC).
The
BMS
is responsible
for
battery
management system
system(BMS),
(BMS),and
andthe
thesite
sitedispatch
dispatchcontroller
controller
(SDC).
The
BMS
is responsible
monitoring,
controlling,
and and
protecting
the battery
cells, including
monitoring
state-of-charge
(SOC),
for
monitoring,
controlling,
protecting
the battery
cells, including
monitoring
state-of-charge
preventing
over-charge/under-charge,
and
protecting
against
thermal
damage.
The
PCS
contains
(SOC), preventing over-charge/under-charge, and protecting against thermal damage. The PCSa
four-quadrant
Parker-Hannifin
inverter (+/−inverter
real and(+/−
reactive
cooling
system,cooling
metering
units,
contains
a four-quadrant
Parker-Hannifin
real power),
and reactive
power),
system,
processing
units,
and
associated
protection.
metering units, processing units, and associated protection.

(a)

(b)

Figure
Figure 1.
1. Containers
Containers for
for the
the BESS
BESS (a)
(a) power
power module
module (PM);
(PM); and
and (b)
(b) power
power conversion
conversion system
system (PCS).
(PCS).

Energies 2018, 11, 3367

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The control center of the BESS is the site-dispatch controller housed in the power module, which
performs
the
following
tasks:
Energies
2018,
11, x FOR PEER
REVIEW
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1.
2.

Collects
and stores
allofdata
on the
cloud
server controller housed in the power module, which
The control
center
the BESS
is the
site-dispatch
Utilizes data
from thetasks:
BMS (e.g., SOC and cell temperatures) and the inverter system in the PCS
performs
the following

3.
4.

to determine
any
limits
available
power
1.
Collects and
stores
allon
data
on the cloud
server
Processes
the
measured
data
to
develop
power
commands using
controlinalgorithms
2. Utilizes data from the BMS (e.g., SOC and
cell temperatures)
and thereal-time
inverter system
the PCS
to determine
limits on available
Executes
manualany
commands
inputtedpower
by a user
3. Processes the measured data to develop power commands using real-time control algorithms
The
SDC coordinates
the PM and
PCSby
toaprovide
the desired amount of real and/or reactive
4. Executes
manual commands
inputted
user

power at the point of common connection (PCC). It can accept automatic generation control (While the
The SDC coordinates the PM and PCS to provide the desired amount of real and/or reactive
AGCpower
capability
exists, it was never connected or utilized.) (AGC) commands or operate in manual or
at the point of common connection (PCC). It can accept automatic generation control (While
automatic
modes.
In manual
the connected
operator specifies
the(AGC)
amount
of real/reactive
desired,
the AGC capability
exists, itmode,
was never
or utilized.)
commands
or operatepower
in manual
whileorinautomatic
automaticmodes.
mode In
themanual
powermode,
command
is calculated
according
one of
control algorithms
the operator
specifies
the amount
of two
real/reactive
power
(Section
3). Operators
interact mode
with the
a PC-based
Humanaccording
Machineone
Interface
desired,
while in automatic
the SDC
powervia
command
is calculated
of two (HMI)
control over
algorithms
(Section
3). Operators
with the
SDC via include:
a PC-based
Human of
Machine
a secure
network
connection.
Theseinteract
interactions
typically
selection
controlInterface
algorithms,
(HMI)parameter
over a secure
network
connection.orThese
interactions
typically
include: Manual
selection commands
of control are
algorithm
settings,
diagnostics,
inputting
of manual
commands.
algorithm
parameter settings,
diagnostics,
or inputting
of manualscheduled
commands.intervals.
Manual
oftenalgorithms,
used to execute
State-of-Health
tests which
are performed
at regularly
commands are often used to execute State-of-Health tests which are performed at regularly
Figure 2 shows a simplified diagram of the system, indicating the location of the four meters
scheduled intervals.
used for data collection. There are three Schweitzer SEL-735 m which sample current, voltage, and
Figure 2 shows a simplified diagram of the system, indicating the location of the four meters
frequency;
one each for the BESS (“Battery”), the wind farm (“Wind Farm”), and combined signals
used for data collection. There are three Schweitzer SEL-735 m which sample current, voltage, and
(“Totalizer”).
There
is also
Shark
m that can
usedfarm
to determine
the amount
of auxiliary
power
frequency;
one each
forathe
BESS200
(“Battery”),
thebe
wind
(“Wind Farm”),
and combined
signals
that the
BESS
consumes.
The
meters
sample
at
10
Hz
for
control
purposes.
Data
is
recorded
(“Totalizer”). There is also a Shark 200 m that can be used to determine the amount of auxiliary powerto the
cloudthat
server
at a rate
of 5 HzThe
for meters
analysis.
Within
theHz
PM,
are organized
as follows:
the BESS
consumes.
sample
at 10
forbattery
control cells
purposes.
Data is recorded
to the7 cells
cloudgroup”,
server at
a rate
of 5 Hz
analysis.and
Within
PM, battery
cellsreplaceable
are organized
as(LRU,
follows:
7
to a “cell
2 cell
groups
to for
a module,
fourthe
modules
to a line
unit
Figure
2,
to a “cell
2 cell groups
a module,
and
to and
a linetemperatures
replaceable unit
inset).cells
Voltages
forgroup”,
each group
(7 cells)toare
sampled
atfour
10 s modules
intervals,
for(LRU,
groups of
Figure
2, inset). consisting
Voltages forofeach
group
(7 cells)
sampled
at 10
s intervals,
and
temperatures
for with
28 cells
(2 modules
2 cell
groups
each)are
are
recorded
every
minute.
These
data, along
groups of 28 cells (2 modules consisting of 2 cell groups each) are recorded every minute. These data,
diagnostics and environmental data within the container, are stored in a cloud server for later analysis.
along with diagnostics and environmental data within the container, are stored in a cloud server for
7 cells are connected in parallel. 384 of these parallel groups are connected in series.
later analysis. 7 cells are connected in parallel. 384 of these parallel groups are connected in series.

Figure
2. Schematic
of the
Hawaiiisland
islandBESS
BESS highlighting
highlighting metering
units.
TheThe
pictures
insets
showshow
Figure
2. Schematic
of the
Hawaii
metering
units.
pictures
insets
the
installed
PM
and
PCS
units
on
site
(top),
and
the
line
replaceable
units
(LRUs)
which
house
the the
the installed PM and PCS units on site (top), and the line replaceable units (LRUs) which house
battery
cells
within
the
PM
(below).
battery cells within the PM (below).

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5 of 17

3. Control Algorithms
3. Control Algorithms
Most of the time, the Hawaii Island BESS operates in the automatic mode. In this mode, one
Most of the time, the Hawaii Island BESS operates in the automatic mode. In this mode, one of
of two algorithms can be selected, a wind smoothing algorithm or a frequency response algorithm.
two algorithms can be selected, a wind smoothing algorithm or a frequency response algorithm. The
The algorithms were developed by SCADA Solutions, LLC and Integrated Dynamics, Inc. (IDI) on
algorithms were developed by SCADA Solutions, LLC and Integrated Dynamics, Inc. (IDI) on behalf
behalf of the battery manufacturer, Altairnano. These algorithms were developed in close consultation
of the battery manufacturer, Altairnano. These algorithms were developed in close consultation with
with the utility (HELCO) and the HRD wind farm. Both algorithms were developed through a similar
the utility (HELCO) and the HRD wind farm. Both algorithms were developed through a similar
process required by HNEI (Figure 3) which is described below. Process ensures that the algorithms
process required by HNEI (Figure 3) which is described below. Process ensures that the algorithms
were meeting the design and performance objectives.
were meeting the design and performance objectives.
The development process began by defining a set of requirements and specifications for each
The development process began by defining a set of requirements and specifications for each
algorithm. Once these were agreed upon by all stakeholders, Altairnano and IDI designed the
algorithm. Once these were agreed upon by all stakeholders, Altairnano and IDI designed the
algorithms and HMI user interface to meet the specifications. Next, simple modeling studies were
algorithms and HMI user interface to meet the specifications. Next, simple modeling studies were
performed to optimize control algorithm parameters and to ensure the predicted behavior of the
performed to optimize control algorithm parameters and to ensure the predicted behavior of the BESS
BESS under each algorithm met the design objectives. After the algorithms passed an acceptance
under each algorithm met the design objectives. After the algorithms passed an acceptance test based
test based on the modeling studies, laboratory testing with computational hardware in-the-loop
on the modeling studies, laboratory testing with computational hardware in-the-loop was executed.
was executed. This step determined whether the algorithms, running on the embedded computers
This step determined whether the algorithms, running on the embedded computers performed as
performed as intended. Lastly, a site acceptance test (SAT) plan was developed and executed during
intended. Lastly, a site acceptance test (SAT) plan was developed and executed during BESS
BESS commissioning
to ensure
thatalgorithms
the algorithms
performed
as intended
in field.
the field.
Descriptions
of
commissioning
to ensure
that the
performed
as intended
in the
Descriptions
of the
the two
algorithms
results
acceptance
andinitial
initialtesting
testingofofthe
thealgorithms
algorithmsare
are presented
presented
two
algorithms
andand
thethe
results
of of
sitesite
acceptance
and
in
the
following
sections.
in the following sections.

Figure
control algorithm.
algorithm.
Figure 3.
3. Flowchart
Flowchart of
of the
the development
development process
process for
for the
the frequency
frequency response
response control

3.1. Frequency
FrequencyResponse:
Response:Algorithm
AlgorithmDevelopment
Developmentand
andTesting
Testing
Some of the most important results in this section were made possible by the
the agreement
agreement between
HNEI and HELCO. As the section progresses, it will become apparent that quantifying the impact of
a frequency response algorithm would be limited without the unique ability of experimentalists to
actively engage in control of the BESS, and hence, the response of the grid. Unlike power smoothing
(including wind smoothing), it is not possible to directly determine how the grid frequency ‘would
have behaved’ if the BESS (running a frequency
frequency response
response algorithm)
algorithm) was not present.
present. This would
otherwise complicate the assessment of performance.

The electric grid on the island of Hawaii operates with a relatively high penetration of nondispatchable renewable resources (70 MW of wind and solar on a 180 MW grid in 2013), which can
result in increased frequency fluctuations due to mismatch of load and generation and reduced

Energies 2018, 11, 3367

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The electric grid on the island of Hawaii operates with a relatively high penetration of
non-dispatchable renewable resources (70 MW of wind and solar on a 180 MW grid in 2013), which can
Energies 2018, 11, x FOR PEER REVIEW
6 of 17
result in increased frequency fluctuations due to mismatch of load and generation and reduced
rotational inertia.
inertia. The
The frequency
frequency response
response algorithm
algorithm was
was designed
designed to
to mitigate
mitigate such
such frequency
frequency
rotational
deviations
by
sourcing
and
sinking
real
power
to/from
the
grid
as
needed.
deviations by sourcing and sinking real power to/from the grid as needed.
The grid
200
m,m,
and
(b)(b)
SEL-735
m (Figure
2, top
The
grid frequency
frequency isismeasured
measuredby
bytwo
twometers:
meters:(a)(a)Shark
Shark
200
and
SEL-735
m (Figure
2,
left
of
Figure
4).
The
Shark
meter
proved
to
have
a
significant
lag
due
to
filtering,
so
the
frequency
top left of Figure 4). The Shark meter proved to have a significant lag due to filtering, so the frequency
signal is
is obtained
obtained from
from the
the SEL-735
SEL-735 m
m in
in the
the field
field for
for control
control purposes.
purposes. The
The frequency
frequency signal
signal is
is passed
passed
signal
through
an
outlier
rejection
filter
to
prevent
overly
large
ramp
rates
and
a
low
pass
filter
to
prevent
through an outlier rejection filter to prevent overly large ramp rates and a low pass filter to prevent
potential oscillations.
oscillations.
potential

Figure 4.
4. Schematic
Schematic of
of the
the frequency
frequency response
response control
control algorithm.
algorithm.
Figure

The primary
primary component
component for
for adjusting
adjusting the
the frequency
frequency response
response algorithm
algorithm is
is the
the frequency-Watt
frequency-Watt
The
(f-W)
curve.
The
curve
can
be
configured
with
a
deadband
of
various
widths
and
with various
various
(f-W) curve. The curve can be configured with a deadband of various widths and with
proportional
gains,
i.e.,
the
slope
of
the
f-W
curve
in
MW/Hz
(Figure
4,
inset).
The
result
of
the f-W
f-W
proportional gains, i.e., the slope of the f-W curve in MW/Hz (Figure 4, inset). The result of the
curve is
is summed
summed with
This
sum
is
curve
with aa real
real power
powercomponent
componenttotomaintain
maintainaatarget
targetstate-of-charge
state-of-charge(SOC).
(SOC).
This
sum
then
limited
by
total
available
power,
with
a
further
limitation
based
on
BMS
and
inverter
data
before
is then limited by total available power, with a further limitation based on BMS and inverter data
sending
a real power
command
to the inverter.
before
sending
a real power
command
to the inverter.
The
frequency
response
algorithm
wasfirst
firsttested
tested
within
a simple
grid
model
developed
on
The frequency response algorithm was
within
a simple
grid
model
developed
on the
the
MATLAB/Simulink
platform.
A
model
of
the
BESS
was
incorporated
into
a
simple
model
of
MATLAB/Simulink platform. A model of the BESS was incorporated into a simple model of the grid
the grid dynamics
[25], inawhich
single generator
represented
the cumulative
all
dynamics
[25], in which
singlea generator
model model
represented
the cumulative
effecteffect
of allofthe
the
generators
on
the
grid,
and
which
included
a
wind
generation
input
(Figure
5).
The
unknown
generators on the grid, and which included a wind generation input (Figure 5). The unknown
parameters of
constant
(M),
damping
constant
(D),(D),
governor
gains
(Kp (Kp
and
parameters
of the
thegrid
gridmodel
modelwere
wereinertia
inertia
constant
(M),
damping
constant
governor
gains
Ki), Ki),
and and
generator
response
time.
These
were
estimated
based
on historical
telemetry
provided
by
and
generator
response
time.
These
were
estimated
based
on historical
telemetry
provided
HELCO.
The
consequence
of
a
large
number
of
unknowns
is
the
possibility
of
parameter
trades
by HELCO. The consequence of a large number of unknowns is the possibility of parameter trades
(i.e., aa particular
particular set
set of
of parameters
parameters causes
causes the
the model
model to
to “fit”
“fit” the
the grid
grid frequency
frequency data,
data, but
but this
this may
may not
not
(i.e.,
be
the
physically
correct
set
of
parameters).
For
this
reason,
a
variety
of
reasonable
parameter
sets
were
be the physically correct set of parameters). For this reason, a variety of reasonable parameter sets
used during
testing.
This, in
turn,inprovided
a variety
of possible
predicted
behaviors.
The full range
of
were
used during
testing.
This,
turn, provided
a variety
of possible
predicted
behaviors.
The full
resultsofindicated
that the that
BESSthe
would
worsen
the gridthe
frequency.
Model runs
using
setsthe
of
range
results indicated
BESSnot
would
not worsen
grid frequency.
Model
runsthe
using
telemetry-estimated
parameters
showed
that
the
frequency
response
algorithm
would
improve
both
sets of telemetry-estimated parameters showed that the frequency response algorithm would
the transient
of theresponse
grid to disturbances
the steady-state
in gridvariability
frequency.in
improve
bothresponse
the transient
of the grid and
to disturbances
and variability
the steady-state

grid frequency.
During the site acceptance test, the grid frequency was observed for several 6–8 Hr periods with
the BESS off, and several 6–8 Hr periods with the BESS on. The results of the test showed that the
BESS did not degrade frequency variability, but there was also no statistically significant evidence
that the BESS reduced frequency variability with the proportional gain at 10 MW/Hz. Subsequent

testing by HNEI revealed that a statistically significant reduction in grid frequency variability could
be achieved by increasing the proportional gain (slope of the f-W curve) to 30 MW/Hz or more. Also,
because grid conditions and generator dispatch change over a shorter time frame, it was preferable
to make observations over shorter time periods rather than 6–8 Hr windows. A new protocol was
therefore
developed for testing the BESS under the frequency response control algorithm,
as
Energies 2018, 11, 3367
7 of 17
described in the following section.

Figure 5.
5. Schematic
Schematic of
of the
the simple
simple model
model of
of the
the Hawaii
Hawaii island
island grid,
grid, which
which was
was used
used to
to develop
develop the
the
Figure
frequency
response
algorithm.
frequency response algorithm.

During the site acceptance test, the grid frequency was observed for several 6–8 Hr periods with
In order to test the effect of the BESS on grid frequency during field operations, HNEI designed,
the BESS off, and several 6–8 Hr periods with the BESS on. The results of the test showed that the BESS
and HELCO approved a test protocol to mitigate the effects of time varying background grid
did not degrade frequency variability, but there was also no statistically significant evidence that the
conditions. The protocol called for switching the BESS ON and OFF every 20 min allowing the
BESS reduced frequency variability with the proportional gain at 10 MW/Hz. Subsequent testing by
frequency variability of consecutive OFF/ON periods to be compared to assess the impact of the BESS.
HNEI revealed that a statistically significant reduction in grid frequency variability could be achieved
A time period of 20 min was chosen for each OFF and ON period in order to balance sufficient data
by increasing the proportional gain (slope of the f-W curve) to 30 MW/Hz or more. Also, because
collection against the changing background grid conditions.
grid conditions and generator dispatch change over a shorter time frame, it was preferable to make
The “switching experiments”, typically 200 min in duration, were initially performed only in the
observations over shorter time periods rather than 6–8 Hr windows. A new protocol was therefore
evening hours to isolate the frequency variability due to wind fluctuations. Examples of two evening
developed for testing the BESS under the frequency response control algorithm, as described in the
experiments are shown in Figure 6. One experiment was conducted during an evening with light
following section.
winds (Figure 6a) and another was conducted during an evening with medium winds (Figure 6b). In
In order to test the effect of the BESS on grid frequency during field operations, HNEI designed,
both, the red oscillating lines in Figure 6 (top) indicate when the battery was on showing real power
and HELCO approved a test protocol to mitigate the effects of time varying background grid conditions.
output of the BESS. The center plots show wind power output at the 10.6 MW Haw’i wind farm. The
The protocol called for switching the BESS ON and OFF every 20 min allowing the frequency variability
bottom plots display the grid frequency during the experiments. Comparing contiguous red and
of consecutive OFF/ON periods to be compared to assess the impact of the BESS. A time period of
black sections in the bottom plots, it is apparent that there was a reduction in grid frequency
20 min was chosen for each OFF and ON period in order to balance sufficient data collection against
variability when the BESS was ON (red) compared to when the BESS was OFF (black).
the changing background grid conditions.
The “switching experiments”, typically 200 min in duration, were initially performed only in the
evening hours to isolate the frequency variability due to wind fluctuations. Examples of two evening
experiments are shown in Figure 6. One experiment was conducted during an evening with light
winds (Figure 6a) and another was conducted during an evening with medium winds (Figure 6b).
In both, the red oscillating lines in Figure 6 (top) indicate when the battery was on showing real power
output of the BESS. The center plots show wind power output at the 10.6 MW Haw’i wind farm. The
bottom plots display the grid frequency during the experiments. Comparing contiguous red and black
sections in the bottom plots, it is apparent that there was a reduction in grid frequency variability
when the BESS was ON (red) compared to when the BESS was OFF (black).

Energies
2018,
11,11,
3367
Energies
2018,
x FOR PEER REVIEW
Energies 2018, 11, x FOR PEER REVIEW

(a)
(a)

8 8ofof
17 17
8 of 17

(b)
(b)

Figure 6. Top: BESS power output with a gain of 30MW/Hz; Middle: wind power generation; Bottom:
Figure6.6.Top:
Top:BESS
BESS power
power output
output with
with aa gain
gain of 30MW/Hz;
Middle: wind
power
generation;
Bottom:
Figure
30MW/Hz;
wind
power
generation;
and grid frequency
on an evening
with lightof
winds
(a) andMiddle:
an evening
with
medium
winds Bottom:
(b). Black
and
grid
frequency
on
an
evening
with
light
winds
(a)
and
an
evening
with
medium
winds
(b).
Black
and
grid
frequency
on
an
evening
with
light
winds
(a)
and
an
evening
with
medium
winds
(b).
Black
lines indicate the BESS is OFF and red lines indicate BESS is ON. The BESS was switched between
lines
indicate
the
BESS
is
OFF
and
red
lines
indicate
BESS
is
ON.
The
BESS
was
switched
between
lines
indicate
the
BESS
is
OFF
and
red
lines
indicate
BESS
is
ON.
The
BESS
was
switched
between
OFF
OFF and ON every 20 min.
OFFON
andevery
ON every
20 min.
and
20 min.

Inorder
ordertotoquantify
quantifythe
thereduction
reductioninin
grid
frequency variability,
the standard
deviation of grid
Inorder
grid frequency
frequency variability,
In
to quantify the
reduction in
grid
variability, the
the standard
standarddeviation
deviationofofgrid
grid
frequencywas
wascalculated
calculatedfor
foreach
each20-min
20-min
period.
To verify
that this
metric captured
grid frequency
frequency
period.
To
frequency
was calculated
for each
20-min period.
To verify
verify that
that this
this metric
metriccaptured
capturedgrid
gridfrequency
frequency
variabilityinina abelievable
believableway,
way,5 5sample
sampletime
time
series
of grid
frequency were
ordered by
eye (left
to
variability
series
of
variability
in a believable way,
5 sample time
series
of grid
grid frequency
frequency were
were ordered
orderedby
byeye
eye(left
(lefttoto
right)
by
a
few
researchers
(Figure
7).
The
ordering
was
then
compared
to
the
values
of
the
metric.
right)by
byaafew
fewresearchers
researchers (Figure
(Figure 7).
7). The
right)
The ordering
ordering was
was then
then compared
comparedtotothe
thevalues
valuesofofthe
themetric.
metric.
The
standard
deviations
(in
mHz)
were
7.1,
11.2,
14.2,
17.6,
and
24.3
respectively
from
left
to right
in
The
standard
deviations
(in
mHz)
were
7.1,
11.2,
14.2,
17.6,
and
24.3
respectively
from
left
to
right
The standard deviations (in mHz) were 7.1, 11.2, 14.2, 17.6, and 24.3 respectively from left to rightinin
Figure
7,
corresponding
to
the
visual
ordering
of
the
frequency
variability.
Figure7,7,corresponding
correspondingto
tothe
thevisual
visual ordering
ordering of
of the
the frequency
frequency variability.
variability.
Figure

Figure
7.
Five
20-min
frequency
series
shown
inin
order
of of
increasing
by-eye
variability.
Figure
7.7.
Five
time
series
shown
in
order
of
increasing
by-eye
variability.
Figure
Five
20-min
frequency
time
series
shown
order
increasing
by-eye
variability.

Table
22shows
the
standard
deviations
percent
changes
for
the
data
presented
Table
the
standard
deviations
and
percent
changes
for
the
data
presented
ininFigure
6.6.6.
Table
2shows
shows
the
standard
deviations
and
percent
changes
for
the
data
presented
inFigure
Figure
Each
OFF/ON
pair
is
referred
“interval”.
The
standard
deviations
for
the
5
intervals
for
the
Each
OFF/ON
to
as
an
“interval”.
The
standard
deviations
for
the
5
intervals
for
the
Each OFF/ON pair is referred to as an “interval”. The standard deviations for the 5 intervals for the
evening
with
low
wind
is
shown
in
the
upper
part
ofof
the
table,
and
the
intervals
forfor
thethe
evening
evening
with
low
wind
isis
shown
inin
the
table,
and
the
55 intervals
for
the
evening
evening
with
low
wind
shown
the
upper
part
the
table,
and
the
5 intervals
evening
with
medium
winds
is
shown
in
the
bottom.
The
mean
percent
change
for
the
light
wind
case
with
medium
winds
is
shown
in
the
bottom.
The
mean
percent
change
for
the
light
wind
case
isis is
with medium winds is shown in the bottom. The mean percent change for the light wind case
−−15.4%,
15.4%,
with
the
negative
indicating
a
reduction
of
frequency
variability
by
15.4%
when
the
BESS
indicating
a
reduction
of
frequency
variability
by
15.4%
when
the
BESS
isis is
−15.4%, with the negative indicating a reduction of frequency variability by 15.4% when the BESS

Energies 2018, 11, x FOR PEER REVIEW

9 of 17

ON. The mean percent change for the medium wind case is −37.1%, indicating that with increasing
grid frequency variability, the BESS has a stronger proportional effect.
Although
the above approach is a useful metric for assessing how the overall frequency
Energies
2018, 11, 3367
9 of 17
variability is reduced, it does not provide any information on how well the frequency variability is
reduced on short timescales, and therefore does not optimally measure the effect of using a fast-acting
ON. TheIf mean
percent of
change
forminutes
the medium
windwere
case is
−37.1%,
indicating
with increasing
battery.
a timescales
several
or longer
chosen,
then
the gridthat
frequency
control
grid
frequency
variability,
the
BESS
has
a
stronger
proportional
effect.
would be dominated by generator governors, and one is not measuring the effectiveness of the BESS.
Although
above
approach
a useful
metric
for assessing
the overall
frequency
variability
For this
reason,the
it was
decided
thatisthe
20-min
periods
would behow
divided
into several
smaller
nonis
reduced,
it
does
not
provide
any
information
on
how
well
the
frequency
variability
is
reduced
on
overlapping segments, and the standard deviation of each of the smaller segments taken. The mean
short
timescales,
and
therefore
does
not
optimally
measure
the
effect
of
using
a
fast-acting
battery.
of those standard deviations is then determined and used to provide a single metric characterizing
If awhole
timescales
of several
or longer
chosen,
then is
thereferred
grid frequency
would
be
the
20-min
period. minutes
The length
of the were
smaller
segments
to as thecontrol
timescale.
This
dominated
by
generator
governors,
and
one
is
not
measuring
the
effectiveness
of
the
BESS.
For
this
process is illustrated by example in Figure 8. The grid frequency data is separated into smaller 60 s
reason, it was
the 20-min
periods
be divided
into several
smaller
segments.
The decided
standardthat
deviations
of those
60would
s segments
are shown
just above
the non-overlapping
plot. The mean
segments,
and
the
standard
deviation
of
each
of
the
smaller
segments
taken.
The
meanwindow.
of those
of those values was calculated to be 7.5 mHz and represents the variability for the entire 20-min
standard deviations is then determined and used to provide a single metric characterizing the whole
20-min
period.
The length
of theofsmaller
segments
is with
referred
as versus
the timescale.
This process
is
Table
2. The standard
deviation
the 20-min
intervals
BESS to
OFF
20-min periods
with
illustrated
byalong
example
The grid
frequency
data
separated
smaller 60
s segments.
BESS ON,
with in
theFigure
percent8.change
between
intervals,
foristhe
switchinginto
experiments
shown
in
The Figure
standard
of those
60with
s segments
areB:shown
above
the with
plot.medium
The mean
of those
6. A:deviations
Results for the
evening
light winds;
Resultsjust
for an
evening
winds.
values was calculated to be 7.5 mHz and represents the variability for the entire 20-min window.
A: Frequency Variability: Evening with Light Wind

Interval
Percent Change
Standard Deviation of Standard Deviation of
Table 2. The standard deviation of the 20-min intervals with BESS OFF versus 20-min periods with
Frequency
w/
BESS
OFF
Frequency
w/
BESS
ON
BESS ON, along with the percent change between intervals, for the switching experiments shown in
[mHz]
[mHz]
Figure 6. A: Results for the evening
with light winds; B: Results
for an evening with medium winds.
1
13.4
8.3
−38.1
A: Frequency
Light Wind
2
10.3 Variability: Evening with6.0
−41.7
Standard 9.0
Deviation of
Standard
3 Interval
7.7Deviation of
Percent−14.4
Change
Frequency w/BESS OFF [mHz]
Frequency w/BESS ON [mHz]
4
7.1
6.7
−5.6
1
13.4
8.3
−38.1
5 2
6.6
8.16.0
22.7
10.3
−41.7
3
4
Interval
5

1 Interval
2 1
3 2
3
4 4
5 5

7.7
B: Frequency9.0
Variability: Evening with Medium
Wind

Standard

7.1
Deviation
6.6

of

Standard

6.7
Deviation
8.1

of

Frequency w/ BESS OFF
Frequency w/ BESS ON
B: Frequency Variability: Evening with Medium Wind
[mHz]
[mHz]
Standard Deviation of
Standard Deviation of
15.9 OFF [mHz]
8.6
Frequency w/BESS
Frequency w/BESS
ON [mHz]
13.5
7.98.6
15.9
13.5
14.3
8.87.9
14.3
8.8
12.6
7.27.2
12.6
11.4
11.4
9.59.5

−14.4
−5.6
Percent Change
22.7

Percent−45.9
Change

−41.5
−45.9
−41.5
−38.5
−38.5
−42.9
−42.9
−16.7
−16.7

Figure 8. A frequency time-series is divided into several smaller segments, in this case 60-seconds in
length. The standard deviation of each smaller time series is shown as just above the plot. The mean of
those values is taken to be the “frequency variability” of the 20-min period on a 60-seconds timescale.

Energies 2018, 11, x FOR PEER REVIEW

10 of 17

Figure 8. A frequency time-series is divided into several smaller segments, in this case 60-seconds in
length. The standard deviation of each smaller time series is shown as just above the plot. The mean
of those values is taken to be the “frequency variability” of the 20-min period on a 60-seconds
Energies 2018, 11, 3367
10 of 17
timescale.

Timescales
Timescales that
thatare
aretoo
tooshort
shortpresent
presentproblems
problems as
as well.
well.Figure
Figure9,9,shows
showsthe
themean
meanof
ofthe
thestandard
standard
deviations
over
a
number
of
timescales
and
for
the
same
samples
as
shown
in
Figure
7,
with
same
deviations over a number of timescales and for the same samples as shown in Figure 7,the
with
the
colors
(e.g.,
Sample
1
is
blue
in
both
figures).
At
timescales
from
seconds
to
tens
of
seconds,
the
same colors (e.g., Sample 1 is blue in both figures). At timescales from seconds to tens of seconds,
measured
standard
deviation
depends
on theon
chosen
time scale,
one so
does
not
have
a stable
the measured
standard
deviation
depends
the chosen
timeso
scale,
one
does
not
have ametric.
stable
Moreover,
the shortest
timescales
do notdoshow
goodgood
separation
between
thethe
high
and
metric. Moreover,
the shortest
timescales
not show
separation
between
high
andlow
lowgrid
grid
variability
periods.
At
timescales
of
approximately
one
minute
and
longer,
the
measured
standard
variability periods. At timescales of approximately one minute and longer, the measured standard
deviation
value;
in in
other
words,
thethe
measured
values
are no
deviationvalues
valueslevel
leveloff
offtowards
towardsa near-constant
a near-constant
value;
other
words,
measured
values
are
longer
timescale
dependent.
Moreover,
the
separation
between
more
and
less
variable
periods
no longer timescale dependent. Moreover, the separation between more and less variable periods
increases
increasesup
upto
tothe
theone-minute
one-minutetimescale,
timescale,and
andthen
thenlevels
levelsoff
offas
as well.
well. The
Theone-minute
one-minutetimescale
timescalewas
was
therefore
chosen
to
balance
the
need
to
capture
the
quick
BESS
response
with
stable
metric
therefore chosen to balance the need to capture the quick BESS response with stable metricthat
thatcan
can
distinguish
high
a
low
variability
periods.
distinguish high a low variability periods.

Figure
Figure 9.9. Frequency
Frequencyvariability
variabilitymetrics
metricsfor
fordata
datashown
shownin
in Figure
Figure 7,
7, using
using different
different timescales.
timescales. The
The
colors
colorscorrespond
correspondto
tothe
thesamples
samplesshown
shownin
inFigure
Figure 7.
7.

With
metric
to to
characterize
gridgrid
frequency
(whether
the BESS
was ON
Withthese
theseresults,
results,the
thechosen
chosen
metric
characterize
frequency
(whether
the BESS
was
or
OFF)
was
the
mean
of
consecutive
60-seconds
standard
deviations.
With
this
metric,
it
is
possible
ON or OFF) was the mean of consecutive 60-seconds standard deviations. With this metric, it is
to
study the
BESS the
performance
under various
conditions
and control
settings.settings.
Initial
possible
to study
BESS performance
under grid
various
grid conditions
and algorithm
control algorithm
results
switching
experiments
are shown
Figurein10,
which
intervals
of BESS
Initial of
results
of switching
experiments
arein
shown
Figure
10,shows
which40-min
showstime
40-min
time intervals
real
power
output
(top),
HRD
wind
power
output
(2nd
from(2nd
top),from
grid top),
frequency
(2nd from(2nd
bottom),
of BESS
real
power
output
(top),
HRD
wind
power
output
grid frequency
from
and
the running
standard
deviation
(60-seconds
timescale)
of grid
frequency
(bottom),
bottom),
and the running
standard
deviation
(60-seconds
timescale)
of grid
frequency
(bottom),for
fortwo
two
switching
experiment performed
performedunder
under
similar
conditions,
butdifferent
with different
frequency
switching experiment
similar
gridgrid
conditions,
but with
frequency
response
response
control algorithm
settings. Specifically,
leftfigure
side shows
of the an
figure
shows an
experiment
control algorithm
settings. Specifically,
the left side the
of the
experiment
conducted
with
conducted
with BESS
the full
1 MWoutput.
BESS power
output. Theon
experiment
theofright
side ofwas
the figure
was
the full 1 MW
power
The experiment
the right on
side
the figure
conducted
conducted
with the
power
output
limited
300winds
kW. The
winds
werehigh
similarly
high and
in
with the power
output
limited
to 300
kW.to
The
were
similarly
and variable
invariable
both cases.
both
cases. The bottom-left
clearly
thatOFF
the BESS
OFF
periods
(black)
largerdeviations
standard
The bottom-left
plot clearlyplot
shows
thatshows
the BESS
periods
(black)
have
largerhave
standard
deviations
thatON
theperiods
BESS ON
This is in
less
in bottom-right
the plot on the
bottom-right
that the BESS
(red).periods
This is (red).
less apparent
theapparent
plot on the
because
the BESS
because
BESS istobeing
restricted to 300 kW.
is beingthe
restricted
300 kW.

Energies 2018, 11, 3367

11 of 17

Energies 2018, 11, x FOR PEER REVIEW

11 of 17

(a)

(b)

Figure
comparisonofoftwo
twodifferent
differentdays
days when
when total
total wind
Figure
10.10.
AA
comparison
wind power
poweroutput
outputatatHaw’i
Haw’iRenewable
Renewable
Development (HRD) and variability were similar. The gain setting used for the f-W curve of the BESS
Development (HRD) and variability were similar. The gain setting used for the f-W curve of the BESS
control algorithm was 30MW/Hz in both cases. The only difference is the limiting of the BESS output
control algorithm was 30MW/Hz in both cases. The only difference is the limiting of the BESS output
power, set to 1 MW for the left plots and 300 kW for the right plots. (Top) Time series of BESS real
power, set to 1 MW for the left plots and 300 kW for the right plots. (Top) Time series of BESS real
power output. (2nd from top) Time series of wind power at the HRD wind farm. (2nd from bottom)
power output. (2nd from top) Time series of wind power at the HRD wind farm. (2nd from bottom)
Time series of grid frequency. (bottom) The 60-seconds standard deviation of grid frequency.
Time series of grid frequency. (bottom) The 60-seconds standard deviation of grid frequency.

Table3 3lists
liststhe
the60-seconds
60-seconds frequency
frequency variability
variability metrics
Table
metrics associated
associatedwith
withthe
theswitching
switching
experiments
shown
in
Figure
10.
The
average
percent
variability
reduction
for
the
1
MW
case
(left)
is is
experiments shown in Figure 10. The average percent variability reduction for the 1 MW
case
(left)
58.4 ± 15% with a 95% confidence interval, and the average percent reduction for the 300-kW case
58.4 ± 15% with a 95% confidence interval, and the average percent reduction for the 300-kW case
(right) is 20.9 ± 11% with a 95% confidence interval. The indication is that limiting the BESS output to
(right) is 20.9 ± 11% with a 95% confidence interval. The indication is that limiting the BESS output
30% of rated power yields about a linear 30% reduction in frequency variability for similar wind
to 30% of rated power yields about a linear 30% reduction in frequency variability for similar wind
conditions. The linear relationship was later confirmed through experiments designed to examine of
conditions. The linear relationship was later confirmed through experiments designed to examine
the effect of various frequency response control algorithm parameter settings on BESS frequency
of the effect of various frequency response control algorithm parameter settings on BESS frequency
regulation and usage [20].
regulation and usage [20].
Table 3. The 60-seconds frequency variability metric for the 20-min periods with BESS OFF against
Table 3. The 60-seconds frequency variability metric for the 20-min periods with BESS OFF against
adjacent 20-min periods with BESS ON show in Figure 10. A: Results for the evening with high winds
adjacent 20-min periods with BESS ON show in Figure 10. A: Results for the evening with high winds
and the BESS allowed to use full rated power of 1 MW; B: Results for the evening with similar high
and
the BESS
allowed
to use full
rated
1 MW; B: Results for the evening with similar high
winds
and the
BESS allowed
to use
300power
kW of of
power.
winds and the BESS allowed to use 300 kW of power.
A: 60-Second Frequency Variability: 1000 kW Limit
A: 60-Second
Frequency
1000 kW
Limit
Interval
Frequency
Metric
w/ Variability:
Frequency
Metric
w/
Percent Change
Frequency
Metric
Frequency
Metric
BESS
OFF
[mHz]
BESS
ON
[mHz]
Interval
Percent Change
w/BESS OFF [mHz]
w/BESS ON [mHz]
1
12.9
7.4
−74.3
1
12.9
7.4
−74.3
2
10.7
7.2
−48.6
2
10.7
7.2
−48.6
3
11.2
6.7
3
11.2
6.7
−67.2 −67.2
4
10.9
7.0
−55.7 −55.7
4
10.9
7.0
5
10.8
7.4
−45.9
5
10.8
7.4
−45.9
B:
60-Second
Frequency
Variability:
300
kW
Limit
B: 60-Second Frequency Variability: 300 kW Limit
Frequency
Metric
Frequency Metric
IntervalInterval
Frequency
Metric
w/
Frequency
Metric w/ Percent Percent
Change Change
w/BESS OFF [mHz]
w/BESS ON [mHz]
BESS OFF [mHz]
BESS ON [mHz]
1
9.2
7.0
−23.9
1
9.2
7.0
−23.9
2
11.5
8.5
−26.1
2
11.5
8.5
3
12.7
8.9
−29.9 −26.1
4
11.2
10.5
−6.3 −29.9
3
12.7
8.9
5

12.1

9.9

−18.2

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4
5

12 of 17

11.2
12.1

10.5
9.9

−6.3
−18.2

12 of 17

3.2. Wind Smoothing: Algorithm Development and Testing
3.2. Wind Smoothing: Algorithm Development and Testing
In contrast with the frequency response algorithm, where switching experiments were imperative,
contrastpassive
with the
frequencywere
response
algorithm,
where the
switching
experiments
were
only In
standard
observations
required
to characterize
performance
of the wind
imperative,
only
standard
passive
observations
were
required
to
characterize
the
performance
of
the
smoothing algorithm. This section is not intended to show novelty but was included in this report
wind
smoothing algorithm. This section is not intended to show novelty but was included in this
for completeness.
report
forHRD
completeness.
The
wind farm that came online in 2006, consists of 16 Vestas American Wind Technology
The
HRD
wind
farm
that
camerated
online
in 2006,ofconsists
of 16[26].
Vestas
American
Wind
Technology
660 kW wind turbines,
for
a total
capacity
10.56 MW
This
wind plant
and
purchase
660
kW
wind
turbines,
for
a
total
rated
capacity
of
10.56
MW
[26].
This
wind
plant
and
purchase
power agreement pre-dated the suite of modern wind plant capabilities available today, which
include
power
agreement
pre-dated
the
suite
of
modern
wind
plant
capabilities
available
today,
which
frequency response; primary frequency response would leverage the capabilities of the wind turbine
include
primary
frequency
response impacts
would leverage
the capabilities
of the wind
controls frequency
to respondresponse;
naturally to
negative
system frequency
from its volatility.
The requirements
turbine
controls
to
respond
naturally
to
negative
system
frequency
impacts
from
its
volatility.
The
for the wind power smoothing algorithm were developed based on the Power Purchasing Agreement
requirements
for
the
wind
power
smoothing
algorithm
were
developed
based
on
the
Power
(PPA) between the utility (HELCO) and the wind farm owner, which includes three limitations on the
Purchasing
between
the utility
(HELCO)
and
thestipulates
wind farm
owner, which includes
fluctuationsAgreement
of the wind(PPA)
farm power
output.
Specifically,
the
PPA
that:
three limitations on the fluctuations of the wind farm power output. Specifically, the PPA stipulates that:

the wind farm’s power output may not change more than 1 MW over any two seconds interval,

the wind farm’s power output may not change more than 1 MW over any two seconds interval,

the wind farm’s power output may not change more than 2 MW over any one-minute interval,

the wind farm’s power output may not change more than 2 MW over any one-minute interval,

the
power output
output changes
changes over
over any
any 22 ss intervals
intervals may
may not
not exceed
exceed 300
300 kW.
kW.

the one-minute
one-minute average
average of
of power
A simplified
simplified block
blockdiagram
diagramofofthe
the
wind
power
smoothing
algorithm
is shown
in Figure
11.
A
wind
power
smoothing
algorithm
is shown
in Figure
11. The
The
algorithm
was
designed
to
minimize
PPA
agreement
violations,
as
well
as
minimize
the
standard
algorithm was designed to minimize PPA agreement violations, as well as minimize the standard
deviation of
In the
the most
most basic
basic terms,
terms, the
the
deviation
of power
power output
output over
over the
the 2-seconds
2-seconds and
and one-minute
one-minute intervals.
intervals. In
algorithm
calculates
a
“target”
power
output
from
the
wind
farm
based
on
the
filtered
values
of
the
algorithm calculates a “target” power output from the wind farm based on the filtered values of the
“raw”
output
overover
an adjustable
periodperiod
of time.ofAtime.
standard
proportional-integral-derivative
“raw”wind
windfarm
farm
output
an adjustable
A standard
proportional-integral(PID)
controller
then
calculates
the
desired
BESS
power
command
to
offset
the
difference
between
derivative (PID) controller then calculates the desired BESS power command to offset
the difference
the
actual
wind
farm
output
and
that
target
value.
All
of
the
parameters
were
first
tuned
to avoid
between the actual wind farm output and that target value. All of the parameters were first tuned
to
oscillations
and then
optimized
using
previous
SCADA
data
of of
HRD
output.
avoid
oscillations
and then
optimized
using
previous
SCADA
data
HRDwind
windfarm
farm power
power output.
Additionally,
to return
50% state
state of
of charge
charge by
by
Additionally, the
the algorithm
algorithm was
was designed
designed to
return the
the BESS
BESS to
to the
the nominal
nominal 50%
calculating
the
battery
“bias”,
which
is
defined
as
the
amount
of
power
needed
to
return
the
BESS
calculating the battery “bias”, which is defined as the amount of power needed to return the BESS to
to 50%
SOC
from
currentSOC
SOCover
over1515min.
min.Because
Becausethe
thebattery
batterybias
bias is
is continuously
continuously updated,
updated, the
the
50%
SOC
from
itsits
current
associated
bias
power
commands
exponentially
approach
zero
as
the
battery
nears
50%
SOC,
with
the
associated bias power commands exponentially approach zero as the battery nears 50% SOC, with
result
being
thatthat
the SOC
management
could
taketake
the battery
fromfrom
a fulla charge
or zero
charge
to 50%
the
result
being
the SOC
management
could
the battery
full charge
or zero
charge
to
SOC
in
approximately
one
hour.
The
final
algorithm
was
validated
against
the
subset
of
SCADA
data
50% SOC in approximately one hour. The final algorithm was validated against the subset of SCADA
that was
algorithm
tuning.tuning.
The algorithm
passedpassed
laboratory
and siteand
acceptance
tests to
data
thatnot
wasused
not for
used
for algorithm
The algorithm
laboratory
site acceptance
the satisfaction
of HNEIoftechnical
reviewers.
tests
to the satisfaction
HNEI technical
reviewers.

Figure 11.
11. Schematic
Schematic of
of the
the wind
wind smoothing
smoothing control
control algorithm.
Figure
algorithm.

Figure 12
12 shows
showsa atime
timeseries
series
power
output
from
the HRD
(black)
and
the
Figure
of of
power
output
from
the HRD
windwind
farm farm
(black)
and the
BESSBESS-smoothed
time (red),
seriesfor
(red),
the morning
April
The
bottom
plots are
smoothed
time series
the for
morning
of Aprilof
4th,
2013.4th,
The2013.
bottom
three
plotsthree
are expanded
expanded
sections
of
the
time
series
to
show
detail.
Qualitatively,
it
is
apparent
from
comparison
of
sections of the time series to show detail. Qualitatively, it is apparent from comparison of the two
the
two
time
series
(black
and
red)
that
the
algorithm
is
able
to
smooth
output
from
the
wind
farm.
time series (black and red) that the algorithm is able to smooth output from the wind farm.

Energies
2018,
11,
x FOR
PEER
REVIEW
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2018,
11,
x3367
FOR
PEER
REVIEW
Energies
2018,
11,

13of
1313
ofof17
1717

Figure1313shows
showshistograms
histogramsofofmetrics
metricsrelated
relatedtotothe
thethree
threePPA
PPAlimitations
limitationscalculated
calculatedfrom
fromthe
the
Figure
Figure
13spanning
shows histograms
metrics
related
to the
three
PPA
limitations
calculated
from
BESS
dataset
spanning
a.m.toto6of
6p.m.,
p.m.,
April
5th2013.
2013.
The
three
metrics
are,from
from
lefttotoright:
right:the
the
BESS
dataset
8 8a.m.
April
5th
The
three
metrics
are,
left
BESS
dataset
spanning
8
a.m.
to
6
p.m.,
April
5th
2013.
The
three
metrics
are,
from
left
to
right:
the
maximum
change
in
power
output
over
two
seconds,
the
maximum
change
in
power
output
over
maximum change in power output over two seconds, the maximum change in power output over 6060
maximum
change
in power
output
over
two seconds,
the over
maximum
change Again,
inAgain,
power
output
over
s,and
andthe
theone-minute
one-minute
averages
themaximum
maximum
changes
overtwo
twoseconds.
seconds.
the
windfarm
farm
s,
averages
ofofthe
changes
the
wind
60power
s, and
the one-minute
averages
theblack
maximum
changes
over
two seconds.
Again,
wind
output
represented
the
black
lineand
andthe
thesmoothed
smoothed
powerby
bythe
thethe
red
line.farm
The
power
output
isisrepresented
bybyof
the
line
power
red
line.
The
power
output
is
represented
by
the
black
line
and
the
smoothed
power
by
the
red
line.
The
histograms
histograms
show
that
the
wind
smoothing
algorithm
is
very
effective
at
reducing
power
output
histograms show that the wind smoothing algorithm is very effective at reducing power output
show
that the
wind
smoothingtimescale
algorithm
is
very
effective atless
reducing
power
output
fluctuations
at the
fluctuations
theone-minute
one-minute
timescalebut
but
significantly
lesseffective
effective
the
twoseconds
secondstimescale.
timescale.
fluctuations
atat
the
isissignificantly
atatthe
two
one-minute
timescale
but
is
significantly
less
effective
at
the
two
seconds
timescale.
At
the
fast
At
the
fast
two
seconds
timescale,
the
algorithm
reduces
range
of
the
maximum
change
(left),
but
At the fast two seconds timescale, the algorithm reduces the range of the maximum change (left), two
but
themean
mean
valueremains
remains
virtually
unchanged
(right).
However,
the
one-minute
timescale
(center),
seconds
timescale,
the algorithm
reduces
the range
of However,
the
maximum
change
(left), but
the mean
value
the
value
virtually
unchanged
(right).
atatthe
one-minute
timescale
(center),
thealgorithm
algorithm
has
strongimpact,
impact,
withthe
theaverage
average
maximumchange
changeininpower
poweroutput
output
reduced
remains
virtually
unchanged
(right).with
However,
at the maximum
one-minute
timescale
(center),
the algorithm
has
the
has
a astrong
reduced
byby
more
than
60%.
a
strong
impact,
with
the
average
maximum
change
in
power
output
reduced
by
more
than
60%.
more than 60%.

Figure12.
12.Time
Time
series
power
output
from
theHRD
HRD
wind
farm
(black)
and
thesmoothed
smoothed
power
Figure
series
of
power
output
from
thethe
HRD
wind
farm
(black)
andand
the
smoothed
power
(red)
Figure
Time
series
ofof
power
output
from
wind
farm
(black)
the
power
(red)
for
the
morning
of
4/4/2013.
The
bottom
three
plots
show
the
sections
indicated
by
the
blue
boxes
for the
of 4/4/2013.
TheThe
bottom
three
plots
show
thethe
sections
indicated
byby
thethe
blue
boxes
in
(red)
formorning
the morning
of 4/4/2013.
bottom
three
plots
show
sections
indicated
blue
boxes
ingreater
greater
detail.
greater
detail.
in
detail.

Figure13.
13.Histograms
Histogramsof
threemetrics
metricsrelated
relatedto
thepower
powerpurchasing
purchasingagreement
agreementbetween
betweenthe
theHRD
HRD
Figure
Figure
13.
Histograms
ofofthree
three
metrics
related
totothe
the
power
purchasing
agreement
between
the
HRD
windfarm
farmand
andthe
theutility,
utility,HELCO.
HELCO.The
Themetrics
metricsare
arethe
themaximum
maximumchange
changein
poweroutput
outputover
over22 2ss s
wind
HELCO.
wind
farm
and
the
utility,
The
metrics
are
the
maximum
change
ininpower
power
output
over
(left),
the
maximum
change
in
power
output
over
60
s
(middle),
and
the
one-minute
average
the
(left),
the
maximum
change
in
power
output
over
60
s
(middle),
and
the
one-minute
average
(left), the maximum change in power output over 60 s (middle), and the one-minute average of
ofofthe
the
maximum
power
output
over
2
s
(right).
The
black
(red)
lines
indicate
metrics
calculated
from
data
maximum
power
output
over
2
s
(right).
The
black
(red)
lines
indicate
metrics
calculated
from
data
maximum power output over 2 s (right). The black (red) lines indicate metrics calculated from data
whenthe
theBESS
BESSwas
wasinactive
inactive(active).
(active).The
Thedata
datawas
wascollected
collectedfrom
from88 8a.m.
a.m.to
p.m.on
on4/5/2013.
4/5/2013.
when
when
the
BESS
was
inactive
(active).
The
data
was
collected
from
a.m.
toto66 6p.m.
p.m.
on
4/5/2013.

3.3.Early
EarlyTesting
Testingand
andLessons
LessonsLearned
Learned
3.3.
Shortlyafter
aftercommissioning,
commissioning,the
thefocus
focusofofthe
theresearch
researchwas
wasthe
theoptimization
optimizationofofthe
thefrequency
frequency
Shortly
responsealgorithm.
algorithm.Particular
Particularattention
attentionwas
waspaid
paidtotothe
the“gain”
“gain”setting
settingofofthe
thef-W
f-Wcurve.
curve.While
Whilehigher
higher
response

Energies 2018, 11, 3367

14 of 17

3.3. Early Testing and Lessons Learned
Shortly after commissioning, the focus of the research was the optimization of the frequency
14 of 17
response algorithm. Particular attention was paid to the “gain” setting of the f-W curve. While higher
proportional gains led to greater grid benefit, it did so with a substantial burden on the BESS resulting
proportional gains led to greater grid benefit, it did so with a substantial burden on the BESS resulting
in more energy throughput (cycling). During early testing, gains of up to 40 MW/Hz were attempted
in more energy throughput (cycling). During early testing, gains of up to 40 MW/Hz were attempted
but operation for extended times at these higher gains were found to increase the temperature of the
but operation for extended times at these higher gains were found to increase the temperature of the
modules (each module is 1/2 of a line replaceable unit), as shown in Figure 14. As the temperature
modules (each module is 1/2 of a line replaceable unit), as shown in Figure 14. As the temperature
excursions would cause excessive wear on the battery modules, a setting of 40 MW/Hz with no
excursions would cause excessive wear on the battery modules, a setting of 40 MW/Hz with no
deadband was determined to be beyond the desirable operational limits of the BESS.
deadband was determined to be beyond the desirable operational limits of the BESS.
Another item of interest was the distribution of temperatures in the PM. It was found that
Another item of interest was the distribution of temperatures in the PM. It was found that the
the initial configuration of the HVAC air vents, set as they were upon shipment, were not optimal.
initial configuration of the HVAC air vents, set as they were upon shipment, were not optimal. In
In particular, the air vents shown in Figure 15 at the bases of the two HVAC units were wide open.
particular, the air vents shown in Figure 15 at the bases of the two HVAC units were wide open. This
This resulted in less air flow to the battery modules, which resulted in higher battery temperatures
resulted in less air flow to the battery modules, which resulted in higher battery temperatures overall.
overall. Further, this resulted in increased temperature gradient across the LRUs, as can be seen in
Further, this resulted in increased temperature gradient across the LRUs, as can be seen in Figure 15
Figure 15 (left). HNEI personnel made adjustments on-site, resulting in an improvement to both total
(left). HNEI personnel made adjustments on-site, resulting in an improvement to both total
temperature and temperature gradient (right). Note that the two sets of temperature measurements
temperature and temperature gradient (right). Note that the two sets of temperature measurements
shown in Figure 15 were collected on dates when weather and grid conditions, and the corresponding
shown in Figure 15 were collected on dates when weather and grid conditions, and the corresponding
BESS usage, were similar. There are a number of other studies currently underway that utilize data
BESS usage, were similar. There are a number of other studies currently underway that utilize data
from the subject BESSs. A recently published paper used BESS usage data over a three year period to
from the subject BESSs. A recently published paper used BESS usage data over a three year period to
develop a representative duty cycle [22], which was used to test cell degradation and calendar aging
develop a representative duty cycle [22], which was used to test cell degradation and calendar aging
in the laboratory [21].
in the laboratory [21].
Energies 2018, 11, x FOR PEER REVIEW

Figure14.
14.Time
Timeseries
series of
of the temperature
seven
cells)
over
a three-day
Figure
temperature of
ofbattery
batterymodules
modules(two
(twogroups
groupsofof
seven
cells)
over
a threeperiod
when
the the
gaingain
setting
of the
frequency
control
algorithm
was
increased
from
10 10
MW/Hz
to
day
period
when
setting
of the
frequency
control
algorithm
was
increased
from
MW/Hz


MW/Hz.
40 °C
C and
C.
to4040
MW/Hz.The
Theoperating
operatingspecification
specificationfor
forthese
thesebattery
batterycells
cellsisisbetween
between−−40
and +55
+55 °C.

Energies 2018, 11, 3367
Energies 2018, 11, x FOR PEER REVIEW

15 of 17
15 of 17

1 of a LRU) for different
Figure15.
15. Temperature
Temperature of
of the
the battery
battery modules
modules (two
(two groups
groups of
of seven
seven cells,
cells, ½
Figure
2 of a LRU) for different
configuration
of
the
HVAC1
vents.
Units
are
degrees
Celsius.
configuration of the HVAC1 vents. Units are degrees Celsius.

4.
4. Summary
Summary and
and Future
Future Work
Work
In
a 1a MW,
250250
kW-Hr
fast-acting
BESSBESS
unit was
on the on
Hawaii
Island
InDecember
Decemberofof2012,
2012,
1 MW,
kW-Hr
fast-acting
unitinstalled
was installed
the Hawaii
electric
grid at the
transmission
level. Thislevel.
BESSThis
was BESS
the first
of the
three
grid-scale
units installed
by
Island electric
grid
at the transmission
was
first
of threeBESS
grid-scale
BESS units
the
Hawaiiby
Natural
EnergyNatural
InstituteEnergy
as part of
an integrated
testing, and
evaluation
program.
installed
the Hawaii
Institute
as partresearch,
of an integrated
research,
testing,
and
Two
control program.
algorithms
were
developed,
one designed
to smoothone
power
outputtofrom
a co-located
wind
evaluation
Two
control
algorithms
were developed,
designed
smooth
power output
farm
a second to
regulate
gridtofrequency.
control algorithms
proved
successful
fromand
a co-located
wind
farmisland-wide
and a second
regulate Both
island-wide
grid frequency.
Both
control
at
implementing
their
control objectives.
When controlled
by the
wind smoothing
algorithm,
algorithms
proved
successful
at implementing
their control
objectives.
When controlled
by the
the BESS
wind
was
able to algorithm,
reduce thethe
maximum
change
inreduce
power the
output
from the
windinfarm
over
a one-minute
smoothing
BESS was
able to
maximum
change
power
output
from the
periods
by more
60%. When
controlled
bythan
the frequency
response
algorithm,
the BESS response
reduced
wind farm
over athan
one-minute
periods
by more
60%. When
controlled
by the frequency
the
average the
standard
of grid
frequency
overdeviation
one-minute
by between
and 60%
algorithm,
BESS deviation
reduced the
average
standard
of periods
grid frequency
over20%
one-minute
compared
periods20%
when
the60%
BESS
was OFF,
on the
forOFF,
the control
algorithm.
periods bytobetween
and
compared
todepending
periods when
the settings
BESS was
depending
on the
The
results
that a relatively
small BESS
unit, namely
MW BESSsmall
installed
a grid
with
settings
fordemonstrate
the control algorithm.
The results
demonstrate
that aa1relatively
BESSonunit,
namely
aa peak
of installed
around 180
to provide
grid benefit
and
utility
company
1 MWload
BESS
on aMW,
gridwas
withable
a peak
load of measurable
around 180 MW,
was able
to the
provide
measurable
elected
to develop
a budget
to maintain
and operate
the asystem.
grid benefit
and the
utility company
elected
to develop
budget to maintain and operate the system.
Additionally,
Additionally,the
theresults
resultsdemonstrate
demonstratethat
thatthe
theimpact
impactof
ofBESS
BESSunits
unitson
onthe
thegrid,
grid,and
andindeed
indeed the
the
metric
metric used
used to
to quantify
quantify the
the impact,
impact, can
can be
be highly
highly timescale
timescale dependent.
dependent. For the
the case
case of
of the
the wind
wind
smoothing
smoothing algorithm,
algorithm, while
while the
the BESS
BESS was
was able
able to
to significantly
significantly reduce
reduce wind
wind power
power output
output variability
variability
on
on the
the one-minute
one-minute timescale,
timescale, there
there was
wasalmost
almostno
noeffect
effectat
atthe
thetwo-seconds
two-secondstimescale.
timescale. The
The measured
measured
effect
effectof
ofthe
theBESS
BESS on
on grid
grid frequency
frequency while
while running
running the
the frequency
frequency response
responsealgorithm
algorithmdepends
dependson
on the
the
timescale
timescale that
that is
is examined.
examined. While it
it is
is desirable
desirable to
to measure
measure the
the BESS
BESS impact
impact on
on grid
grid frequency
frequency on
on
short
shorttimescales
timescalesbecause
becauseof
ofthe
the quick
quick response
responsetime
timeof
ofthe
the BESS,
BESS, itit was
was shown
shown that
that timescales
timescales shorter
shorter
than
than aa minute
minute can
can result
result in
in measurements
measurementsof
of grid
grid frequency
frequencythat
thatare
aretimescale
timescaledependent
dependentand
anddo
donot
not
separate
separate high
high and
and low
low variability
variability periods
periods well.
well. ItIt may
may be
be possible
possible to
to take
take advantage
advantage of
of the
the timescale
timescale
dependence
dependence to
to optimize
optimize BESS
BESS control
control algorithms
algorithms for
for their
theirparticular
particularintended
intendedtasks.
tasks. The
The natural
natural
timescale
BESS
optimization
would
be at the
short timescales
over whichover
traditional
timescalefor
forsuch
such
BESS
optimization
would
be relatively
at the relatively
short timescales
which
generation
are incapable
responding.
traditional units
generation
units areofincapable
of responding.
Future work plans include investigating the impact of the Hawaii island BESS on large frequency
events, and the ability of a 2 MW, 397 kW-Hr BESS to help stabilize the small isolated grid on the
island of Molokai.

Energies 2018, 11, 3367

16 of 17

Future work plans include investigating the impact of the Hawaii island BESS on large frequency
events, and the ability of a 2 MW, 397 kW-Hr BESS to help stabilize the small isolated grid on the island
of Molokai.
Author Contributions: Conceptualization, R.R. and K.M.; Methodology, R.R., K.M., M.T., K.S.; Software, K.M.,
M.T., K.S.; Validation, K.M., and K.S.; Formal Analysis, K.M. and M.T.; Investigation, R.R.; Resources, R.R.; Data
Curation, M.T. and K.S.; Writing-Original Draft Preparation, K.S.; Writing-Review & Editing, R.R., M.T., and K.S.;
Visualization, M.T.; Supervision, R.R.; Project Administration, R.R.; Funding Acquisition, R.R.
Funding: This work was funded by ONR under the Hawaii Energy and Environmental Technologies (HEET) 2010
Initiative, award No N00014-11-1-0391 and the Asia Pacific Research Initiative for Sustainable Energy Systems
2012, award No. N00014-13-1-0463.
Acknowledgments: The authors are grateful to the Hawaii Electric Light Company for their support and
partnership on this project and their ongoing support to the operations of the Hawaii Sustainable Energy Research
Facility (HiSERF), Haw’i Renewable Development for hosting the BESS, and Jerry Haverstick and Len Sekowski
(Altairnano) for their help through the course of this study.
Conflicts of Interest: The authors declare no conflict of interest.

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