HomeMy WebLinkAboutPillar Mountain High Penetration Wind Flywheel and Battery Energy Storage System Evaluation - Oct 2011 - REF Grant 7050803WWW.EPSINC.COM
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st
Flywheel and Battery Energy Storage System Evaluation
for
Kodiak Electric Association, Inc.
October 13, 2011
John D.L. Hieb
James W. Cote, Jr., Ph.D., P.E.
Daniel C. Rogers, P.E.
Kodiak Flywheel and Battery Energy Storage System Evaluation
WWW.EPSINC.COM
PHONE (425) 883-2833 4020 148th AVE. NE, SUITE C, REDMOND, WASHINGTON 98052 FAX (425) 883-0464
PHONE (907) 522-1953 3305 ARCTIC BLVD., SUITE 201, ANCHORAGE, ALASKA 99503 FAX (907) 522-1182
Summary of Changes
Revision Revision Date Revision Description
0 October 13, 2011 Initial Release
Table of Contents
1 Executive Summary .............................................................................................................. 1
2 Introduction ........................................................................................................................... 1
3 Study Preparations ................................................................................................................ 2
3.1 Initial Assumptions .......................................................................................................... 2
3.2 Wind Data Analysis ........................................................................................................ 3
4 Flywheel Evaluation .............................................................................................................. 6
4.1 Increasing Terror Lake Integral Gain .............................................................................. 7
4.2 Automatic Generation Control ........................................................................................ 8
4.3 Flywheel VAR Capability ................................................................................................ 8
4.4 Flywheel Parameter Tuning ............................................................................................ 9
4.5 Minimum Number of Flywheel Modules ....................................................................... 10
5 Battery Evaluation ............................................................................................................... 13
5.1 BESS controls .............................................................................................................. 13
5.2 BESS Extreme Ramp Results ...................................................................................... 14
5.3 Battery Expected Life Calculations ............................................................................... 16
6 Conclusions ......................................................................................................................... 20
Appendix A Wind Data Analysis .............................................................................................. 21
Appendix B Severe 2-Minute Simulations with Flywheels ....................................................... 22
Appendix C Severe 2-minute Ramp Simulations with BESS ................................................... 23
Appendix D Xtreme Power BESS Loss of Life Calculation ...................................................... 24
1 Executive Summary
EPS recommends that KEA pursue the battery technology over the flywheel technology based
on cost and performance. Comparable performance can be achieved using approximately 12
flywheel modules versus two BESS systems.
One battery system initially costs twice that of a flywheel module. The battery bank
replacement cost is a comparably low $300,000. The expected life cycle of the battery packs is
more than 16 years.
The preferred controls are based on an aggressive droop type response. The response should
have a deadband around 60 Hz to prevent constant storage charge/discharge. Based on the
simulation results, the droop setting should be set to 0.85% with a frequency deadband of 59.90
60.02 Hz. These parameters should be evaluated during commissioning and revisited based
on system operational experience.
2 Introduction
Electric Power Systems Inc. (EPS) was contracted to perform a study to determine the system
requirements for an energy storage system designed to counteract the negative effects wind
has upon the Kodiak Electric Association (KEA) frequency regulation. EPS evaluated the
PowerStore flywheel modules from PowerCorp as well as the Xtreme Power battery energy
storage system DPR 15-100C. The base PowerStore module has ratings of 1 MW and 18
MW-seconds of energy. The base DPR module has ratings of 1.5 MW with 1 MW-hour of
energy. EPS was tasked with determining the best device to assist with the frequency
regulation of the KEA system. Due to the technological differences between the flywheel and
the battery systems, the sizing and control systems were investigated separately. This allowed
the control schemes to be tuned to best utilize the strengths and minimize the weaknesses of
each control system. Upon determining the sizing and control system needs, the flywheel and
battery systems were compared using their expected life-cycle costs.
This report is the last in a series of progress reports and is meant close issues brought up in the
previous reports as well as to provide final conclusions. The previous progress reports are
listed below:
July 13, 2011 Progress report #1 for Flywheel and Battery Energy Storage System
Evaluation
August 15, 2011 Progress report for Flywheel and Battery Energy Storage System
(BESS) Evaluation
Additionally this report will reference results from several previous studies conducted by EPS for
KEA leading up to this project. The previous studies are listed below with a quick abstract for
each:
July 8, 2010 Third Terror Turbine / Additional 4.5 MW Wind Generation Study
o This was the first study looking at the addition of 4.5 MW of additional wind to the
KEA system. This study evaluated whether the addition of a third turbine at the
Terror Lake Hydro Generation station would be able to provide enough additional
frequency regulation to compensate for the addition of another 4.5 MW of wind.
This study performed transient stability analysis such as unit trips and line faults.
This study also benchmarked the simulated KEA frequency regulation as
compared to actual system recordings for a 15-minute system snapshot. The
conclusion was the third Terror Lake turbine would improve system frequency
regulation, but would not fully compensate for the additional wind effects on
frequency regulation.
August 20, 2010 Supplementary Wind Benchmark Cases
o This report serves as an addendum to the July 8, 2010 report. The purpose of
this study was to determine how the system frequency regulation of today
compares to the proposed cases with an additional Terror Lake turbine and 1, 2,
or 3 additional wind turbines. The conclusion of this report was that by adding
the third Terror Lake turbine and one more wind turbine, the frequency regulation
would be better than today. However, adding two wind turbines would have a
negative impact on the system frequency regulation.
March 4, 2011 Inertia and Needle Valve Sensitivity for the Third Terror Lake Turbine
o Based to the results of the August 20, 2010 study, this study focused on
improving the expected frequency regulation by improving the response of the
Terror Lake generation station. The study investigated adding inertia to the third
Terror Lake turbine and decreasing the needle opening times for the Terror Lake
turbine governor system. This report also analyzed the impact of developing and
using a Terror Lake unit in condense mode. This report determined that the
additional inertia would have limited impact on the minute-by-minute frequency
regulation, and that changing the needle opening times would have no impact on
the frequency regulation due to the existing governor tuning. This result was
troubling since it highlighted the issue that in order to prevent oscillation between
the electric system and the Terror Lake tunnel, the governor may need to be de-
tuned when the third Terror Lake turbine is added.
April 25, 2011 Initial Flywheel Evaluation
o This study was intended to provide a proof of concept for an energy storage
solution for the KEA frequency regulation issue. This study used the
PowerStore flywheel system to assist the Terror Lake generation station in
regulating the KEA frequency. It showed that there is significant benefit to having
an energy storage system for the frequency regulation.
3 Study Preparations
3.1 Initial Assumptions
EPS has made the following assumptions as part of the performance criteria for the various
phases of this and previous studies:
1. With the 3 additional wind turbines and an additional Terror Lake Turbine,
o The frequency regulation shall be no worse than the current frequency regulation
assuming the use of a proposed energy storage system.
o Diesels will not be used to assist the frequency regulation unless required for
maximum power or energy needs.
o The Terror Lake governors will need to be detuned. This was modeled in our
studies by multiplying the integrator gain constant in the governor speed droop
control by 0.67.
o The actual wind power output from the wind farms will be twice the values seen
today, based on the following assumptions:
Geographical diversity of the wind turbines was not accounted for in this
study as the number of turbines is very small as is the geographic
footprint of the wind farm.
This assumption also provides some margin in the study results as any
wind diversity will likely have a positive impact on the system frequency
regulation.
3.2 Wind Data Analysis
KEA provided one years worth of wind output and frequency data from the year 2010. This
data combined with the original 15-minute worst case wind fluctuation event (Case 1 provided
by KEA) was used to determine the limiting conditions on the KEA system. The frequency data
was analyzed to determine the relative severity of the original Case 1 event. The data was
analyzed by computing the standard deviation of the frequency in each 15 minute interval for
the whole year, as discussed in previous reports. The results were highlighted based on
severity to visualize how often severe frequency changes occurred. Figure 1 shown below is an
example of this review. The spreadsheet used to create this figure is attached in Appendix A.
The yellow highlighted cells are frequency standard deviations close to, but not as severe as the
originally Case 1. The red cells represent time periods when the standard deviation of
frequency is worse than observed in Case 1. The results in the figure show these frequency
results for January 5th to January 12th 2010.
Figure 1: January Frequency Regulation Visualization
Unfortunately, there were many 15-minute time intervals in 2010 with standard deviations of
frequency that were more severe than Case 1. EPS used Case 1 as a severe condition test
case and based the control strategy around maintaining the frequency regulation for this case.
Since there were time intervals that were more severe than Case 1, any proposed solution had
to be able to withstand the most severe wind ramp events.
Using the standard deviation of frequency calculations, the standard deviation values were
sorted by severity, and counted. Table 1 shows the number of 15 minute time intervals that
have frequency deviations for each of the ranges (types) shown in the left most column. As the
standard deviation value increases, the number of time intervals decreases. Each count in
Table 1 represents one 15-minute time frame from 2010.
STD. DEV Range JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Type 10 0.05 1791 1807 1861 2062 1736 813 623 1047 1818 1790 1445 726
Type 20.05 0.075 575 493 596 467 676 1086 916 682 599 690 649 815
Type 30.075 0.10 256 216 239 169 247 485 720 615 291 278 332 417
Type 40.10 0.125 168 116 132 81 152 234 437 460 116 104 267 472
Type 50.125 0.15 106 36 75 70 82 109 203 129 36 43 139 418
Type 60.15 0.175 49 15 30 27 54 73 54 32 15 33 41 104
Type 70.175 0.20 18 2 19 4 21 47 18 8 4 23 7 17
Type 80.20 0.225 4 0 15 0 2 28 4 2 1 12 3 3
Type 90.225 0.25 6 0 1 0 4 4 1 1 0 3 1 3
Type 10>0.25 3 3 4 0 2 1 0 0 0 0 0 1
Table 1: Wind Types Based on Standard Deviation of Frequency
In order to compare the life-cycle costs of the competing technologies, 30 different time frames
were selected based on the standard deviation of frequency and are shown in Table 2. These
standard deviations were spread throughout the year to capture seasonal differences in wind
regimes. Three snapshots were taken for each type listed in Table 1. These three snapshots
represent high, medium and low standard deviations within the specified range. For example,
the Type 4 row in Table 2 is represented by April 7, 22:00, August 24, 12:15, and December 19,
22:00. These fifteen minute simulations have standard deviations of frequency of 0.1009,
0.1124, and 0.1247 Hz respectively.
Type Std. Dev Range Low Std. Dev Mid Std. Dev High Std. Dev
1 0 0.05 Jan 2, 2:45 May 18, 16:30 Sep 25, 13:15
2 0.05 0.075 Feb 16, 1:00 Jun 26, 12:45 Oct 20, 8:00
3 0.075 0.10 Mar23, 16:15 Jul 6, 9:15 Nov 26, 18:45
4 0.10 0.125 Apr 7, 22:00 Aug24, 12:15 Dec19, 22:00
5 0.125 0.150 Jan 12, 8:00 May 30, 1:00 Sep 24,20:15
6 0.150 0.175 Feb 14, 21:45 Jun 2, 14:15 Oct 8, 18:15
7 0.175 0.20 Mar6, 19:15 Jul 5, 20:15 Nov 10, 14:45
8 0.20 0.225 Mar9, 14:30 Aug23, 10:30 Dec8, 7:00
9 0.225 0.250 Jan 11, 16:45 May 31, 22:00 Oct 1, 21:31
10 0.250 Higher Jan 11, 17:15 Jun 2, 19:00 Mar4, 0:30
Table 2: Selected Snapshots for Life-Cycle Calculation
The 30 selected cases in Table 2 were then simulated with each proposed energy storage
solution in place. The battery vendor, Xtreme Power then evaluated the impact each case
would have on battery life cycle.
EPS also analyzed the wind data looking for system trip events where multiple wind units
tripped in rapid succession. This was done by selecting each 5-second time frame in the annual
data and determining if the wind power output decreased by 2 MW or more. These instances
only occurred 4 times: January 5 at 11:58, February 12 at 4:05, Feb 28 at 11:13, and Aug 8 at
7:46. Each of these time frames were faults or system outages and were not the result of wind
ramp. As such these time frames will not be used as design criteria for the potential storage
solution.
In order to find the worst-case wind ramp conditions for the flywheel storage device, the data
was analyzed looking for the largest change in wind power over a 2-minute period. EPS only
selected the cases where the wind dropped more than 2 MW in 2 minutes. This would be
equivalent to a 4 MW drop if the number of wind turbines were to double. These time frames
would provide the most severe condition for the flywheels which are limited on their energy
storage. Table 3 contains the dates and times of these severe ramps.
Date 5 January 10 January 11 January 11 January 8 February 12 February 22 February 28 February
Time 11:58 15:09 17:00 23:10 21:20 4:00 14:20 11:10
Date 3 March 4 March 4 March 6 March 8 March 9 March 11 March 13 March
Time 18:30 0:20 22:55 14:10 10:15 5:35 16:45 6:00
Date 25 April 1 June 11 July 23 August 23 August 24 September 24 September 1 October
Time 23:00 13:45 12:40 10:07 12:10 17:20 18:55 13:00
Date 1 October 1 October 3 October 12 October 12 October 12 October 13 October 11 November
Time 21:30 22:00 9:00 0:30 0:50 3:10 3:20 11:50
Date 16 November 22 November 25 November 25 November 26 November 28 November 29 November 29 November
Time 13:30 21:00 0:20 2:30 8:40 18:05 3:30 10:40
Date 29 November 30 November 30 November 30 November 30 November 2 December 3 December 3 December
Time 23:45 0:20 2:30 5:45 8:10 6:40 8:30 17:05
Date 8 December 9 December 13 December 13 December 13 December 13 December 14 December 14 December
Time 6:55 21:00 2:05 4:10 9:05 15:10 3:44 7:05
Date 14 December 14 December 14 December 18 December 19 December 22 December 22 December 23 December
Time 8:00 11:00 19:25 12:10 5:55 11:10 12:20 21:00
Table 3: Severe 2-Minute Wind Ramp Events
4 Flywheel Evaluation
After the initial evaluation of PowerCorps PowerStore flywheel, it was fairly clear that the
technological limitation of the device would be its energy storage capacity. With a base rating of
18 MW-sec, the flywheel energy can be quickly absorbed by a system the size of Kodiaks,
especially considering the fluctuations of a 9 MW wind plant.
EPS spent a significant amount of time tuning the flywheel control system parameters to
minimize the number of flywheels that are necessary to provide the same frequency regulation
in the future as is experienced today. The flywheel variables and system improvements that
were investigated include:
1. Increasing the integral gain of the Terror Lake governors
2. Using an Automatic Generation Control (AGC) system and tuning
3. Using the Flywheel VAR capabilities
4. Tuning the flywheel parameters for droop response
5. Changing the number of flywheel modules
4.1 Increasing Terror Lake Integral Gain
Due to the concerns over the lack of energy in the flywheels, EPS questioned whether the
Terror Lake generation station could be tuned to be more responsive. If the Terror Lake unit
could be more responsive, then the flywheel energy requirements would be reduced. This issue
was brought up in the March 4, 2011 report. The main concern of over-improving the Terror
Lake governor response is the potential of power system oscillations occurring within the Terror
Lake tunnel system.
EPS ran a series of simulations with varying values of the Terror Lake governor integral gain.
The system event was a wind turbine trip. These simulations were intended to show the
governor manufacturer what the expected power oscillations would be with different integral
gain values. The simulation results sent to the governor manufacturer are shown below in
Figure 2.
Figure 2: System Response to Varying Governor Gain
The simulations shown above imply that there is some room for improvement, but KEA informed
EPS that the governor manufacturer would not feel comfortable recommending a significant
change to the governor gains at this time and would need to retune the governor if any gain
changes are made. As such, EPS concluded our investigation of this potential system
improvement. EPS recommends that KEA and the governor manufacturer work to maximize
the Terror Lake governor response when commissioning the proposed third Terror Lake turbine.
4.2 Automatic Generation Control
EPS further investigated the possibility of using signals from the flywheel to trigger additional
movement of the Terror Lake units via an automatic generation control (AGC) type of system.
The AGC would use the power and state of charge signals of the flywheel system as inputs. A
classical AGC would have an Area Control Error or ACE. This signal would be a combination of
the system frequency error and tie line flow error. Since KEA is an island the tie line flow error
would always be zero. EPS set up an AGC model for the KEA system using frequency,
flywheel power output, and flywheel state of charge as the inputs to create an ACE signal.
The system was setup such that the ACE would be calculated using the following equation:
ACE = (Frequency Error) 1*(Flywheel MW) - 2*(Max Charge - Flywheel SOC)
AGC would have a deadband for ACE where no action is taken. For a negative ACE, the
generators would be given commands to raise their generation. The AGC parameters that
could be adjusted are ACE deadband, 1, and 2. Additionally, the EPS AGC model could use
a panic mode in which the unit command pulses could be doubled. The AGC controls need to
be coordinated with the flywheel controls in order to provide the optimum system-wide
response.
Initially, the AGC simulations were providing better frequency regulation compared to the
simulations with one Terror Lake unit operating in isochronous mode. However, this was due to
the operation of the deflector control in the turbine / governor. Since the AGC needs to have
some dead-band to prevent control oscillations around 60 Hz, there are steady state conditions
that are marginally above 60 Hz. The deflector controls are setup such that they will quickly
enter the water stream when the frequency goes above 60 Hz to protect the unit from over-
speed. These simulations were overly optimistic because the Terror Lake units at times ended
up in steady-state water wasting mode. As the wind power dropped, the deflectors would pull
away from the stream resulting in near immediate response. This made the simulations seem
much improved, but steady state water wasting should be avoided as it defeats the purpose of
integrating additional wind.
When EPS made the necessary corrections to the Terror Lake governor gains to prevent steady
state water wasting, the AGC system control no longer provided significant frequency regulation
benefit.
4.3 Flywheel VAR Capability
EPS was tasked with determining the ability of the flywheel to provide reactive power support to
the KEA system. The model provided by PowerCorp has the capability of this type of control.
During the PowerCorp visit to the EPS office in Redmond, WA, the reactive power settings were
discussed with Dr. Andrew Tuckey. He advised against using the flywheel system for reactive
support in steady state conditions.
The reasoning provided by Dr. Tuckey was due to the behavior of the limiter in the flywheel
module. The limiter is based on the thermal ratings of the power electronics. These limits, in
turn, are based on the current flowing through the power electronics circuitry. He explained that
by having steady state reactive current flowing through the device, the ability of the system to
respond to a system frequency disturbance will be reduced. As the response to frequency
disturbances is the primary purpose of this device, he recommended against this practice. He
advocated the use of switched capacitor banks which could be automatically controlled using
the PowerCorp system and would ultimately be more cost-effective. As such, EPS
recommends against using the flywheel system for steady state reactive compensation.
4.4 Flywheel Parameter Tuning
The model provided to EPS by PowerCorp has four main parameters that describe the control
of the flywheel power output. The four parameters are:
1. Static Frequency Gain (Droop)
2. Dynamic Frequency Gain (Rate of change of frequency component)
3. Frequency Deadband Hi
4. Frequency Deadband Lo
When tuning these parameters EPS started with a simple, aggressive droop with a tight
deadband around 60 Hz. This approach provided poor results. The flywheel quickly ran out of
energy before the Terror Lake units had a chance to respond. Since the Terror Lake units are
operating on a droop line or in isochronous mode, the primary input is frequency. If the flywheel
regulates the frequency close to 60, the Terror Lake units will not move, and the flywheel will
eventually run out of energy. When the flywheel runs out of energy it quickly ramps down to 0
MW. When the flywheel ramps down, the event appears more severe than the original wind
ramp. Therefore it is important to find a control strategy that will avoid exhausting the flywheel
energy.
The next approach was to have a larger deadband around 60 Hz. Initially 59.85-60.15Hz was
selected. This approach provided significantly better results since only the Terror Lake units
responded to frequencies down to 59.85 Hz and would continue ramping up as long as the
frequency is below 60. However, this scheme did have a problem in that the flywheel never
recharged. When the frequency goes above 60 Hz, the deflectors enter the stream to prevent
overspeed. Hence, the frequency was rarely above the deadband value of 60.15 and without
an opportunity to recharge, the flywheels eventually ran out of energy.
The next approach was to have the upper frequency deadband reduced to 60 Hz, such that any
time the frequency moved above 60 Hz, the flywheel would recharge. This control change had
three positive effects. 1) The flywheel had a chance to recharge in preparation for the next
downturn in wind generation. 2) The flywheel would spend much more time fully charged and
be better prepared for a downturn in wind generation. 3) By absorbing power above 60 Hz, the
flywheel can prevent some water wasting at the Terror Lake generation station since it reduces
the net upward slope of wind generation. For these reasons, the upper frequency deadband
setting should be set to slightly above 60 Hz.
The dynamic frequency gain was not used since its application would be better suited to unit trip
events. This control scheme would allow for additional flywheel power based on the slope of
the system frequency decay. EPS recommends discussion with the manufacturer on the proper
use of this parameter if flywheels are the selected technology. However, considering the low
energy concerns, any additional power output would use that energy more quickly.
This left the lower frequency deadband and the static frequency gain to be determined. Several
simulations with a mix of these settings at both high and low values were simulated. The control
strategy with a deadband of 59.85 Hz and a static frequency gain of 1.15 appeared to be the
best to use. Again, these settings made sense with the behavior of the Terror Lake governors
which are highly sensitive to frequency. The flywheel controls allow Terror Lake to continue
providing the majority of the frequency regulation, but provide support for large wind ramp
events. With four flywheels using these settings, the future Case 1 standard deviation of
frequency is the same as the current Case 1 configuration. A plot of the simulated future case
with 3 Terror Lake generators and 6 wind turbines is shown below in Figure 3
Figure 3: Case 1 - 3 Terror, 6 Wind, 4 Flywheels, Same Standard Deviation as Present Case 1
The top left traces show system frequency from the original recording (purple) and the
simulation (red). The top right black trace is the flywheel state of charge. The bottom left shows
the wind (orange), Terror Lake (blue/green), and flywheel (black) power outputs. The bottom
right traces show the power fraction in red and black. The power fraction is equal to one minus
the portion of water that is deflected. The standard deviation of frequency for the case shown is
0.1547 Hz. The present case with 2 Terror Lake units and 3 wind turbines had a simulated
standard deviation of 0.1566 Hz. This control strategy accomplishes the frequency goal, and will
be referred to as control strategy G.
4.5 Minimum Number of Flywheel Modules
With the control strategy problem solved, EPS next determined the minimum number of
flywheels necessary to respond to all wind ramp events through the year. Due to the minimal
energy storage of the flywheel systems, the most severe events are the prolonged wind ramp
events. However, a trip event which results in a low frequency for a short period of time is
easily handled by the flywheel system. The time frames used to determine the minimum
number of flywheel systems is shown in Table 3. Control strategy G was used for these
simulations, and the number of flywheels was increased until the simulations did not run out of
energy. An example showing the results for 12 flywheels is shown below in Figure 4.
Figure 4: Extreme Ramp - 12 Flywheels with Control Strategy G
The bottom left set of traces show the power outputs of the different units. The orange trace is
the wind plant, the black is flywheel output, and the blue and green represent the Terror Lake
units. In this simulation, the wind output moves from 8 MW down to 0 in approximately 9
minutes. The top right black trace shows the flywheel state of charge. It can be seen that the
flywheel runs out of energy near the end of the simulation, but the wind is at zero and the
flywheel does not need energy to cover any additional loss of wind. The top left traces show
system frequency from 58 to 60.5 Hz. The purple trace shows the recorded frequency from
2010, and the red trace shows the simulation frequency with double the wind. The results of the
other severe 2-minute ramp events are shown in Appendix B. Several of the events in the
severe ramp set of data are likely unit trip events in rapid succession. This is a situation where
doubling the wind output is unlikely to be a realistic situation when KEA doubles the number of
wind turbines. While the recordings suggest that two of the turbines can trip in rapid
succession, it is even less likely that the equivalent of four turbines would trip in the same time
frame when the wind is doubled. However, the extreme ramp shown in Figure 4 can be
expected, and must be the design criteria for the flywheels since these events appear to happen
approximately 60 times per year.
Without the system improvements discussed earlier such as increasing governor gain or
implementing supplementary automatic generator control, 12 flywheel systems are
recommended. The flywheels come in modules with one energy storage size with several
different power output levels. The power requirements for the proposed control strategy are
approximately 3 MW. As such, EPS recommends using the flywheel configuration with the
smallest power rating to minimize the project cost. Currently the smallest power rating
PowerCorp produces is a 0.5 MW unit. With 12 of these, the power rating would be 6 MW
which is more power than is necessary. The cost of 12 flywheel modules seems prohibitive.
5 Battery Evaluation
After the initial evaluation of the BESS, it was clear that the energy issues seen with the
flywheels were not going to be an issue since one BESS module contains 1 MWhr of energy
which is 200 times the energy of the flywheel module. However, the battery system does have
loss of life issues. As the batteries charge and discharge, there is a predictable system
degradation. It is this degradation that will be the major factor in defining the life-cycle cost of
the BESS technology.
5.1 BESS controls
Many of the assumptions made with the flywheel systems are still valid with the battery system.
Mainly, the control characteristics are quite similar. This is due to the fact that both systems
employ power electronics to convert the stored energy (battery charge, or flywheel rotating
mass) into power injections into the grid. The user models for both energy storage systems
came with a droop characteristic with some sort of frequency deadband. The main difference
between the models, of course, is the energy content.
Since many of the flywheel control parameters were defined by the operation characteristics of
the Terror Lake generation station, a very similar control strategy should be used for the BESS.
EPS ran a simulation using the same droop and deadband characteristics used for the flywheel.
The results are shown below in Figure 5. The frequency is shown in the top left. The unit
power outputs are shown in the bottom left where the green trace is the combination of the wind
and BESS output. This signal shows the actual change in wind output as seen from the Terror
Lake generation station. The top right plot shows unit reference signals and BESS state of
charge. The state of charge signal is zoomed in to 45-55%. The bottom right shows the Terror
Lake power fraction (per unit amount of water not being deflected, or hitting the turbine). This
simulation was run with a 3 MW capacity and 1 MWhr of energy storage. The simulation is
faulty in that the XP modules come in sizes of 1.5 MW with 1 MWhr of energy. So this
simulation shows 2 modules with half the energy storage. The only difference between our
simulations and those with the larger amount of storage is that the state of charge curve would
move half as much.
Figure 5: BESS Results with Control Strategy G
As before, the standard deviation of frequency seen in this future case simulation is equivalent
to the simulation of todays case. The BESS will not run out of energy in a 15 minute simulation
as it has enough energy storage to provide 40 minutes at full power output. The maximum
power output from the BESS is approximately 1.8 MW for this disturbance so the 3 MW for the
two BESS modules should be sufficient.
5.2 BESS Extreme Ramp Results
In order to confirm that the control strategy works properly for the worst case conditions seen in
2010, the extreme 2-minute ramps were simulated with the BESS in place. These are the same
scenarios used to determine the necessary flywheel energy. Because the BESS should have
no trouble with the energy storage, this evaluation was used to determine if the power rating of
the BESS was sufficient. Figure 6 shown below has the results of the November 30th ramp
event that was shown above in Figure 4 but with the battery system instead of the flywheel
system.
Figure 6: Extreme Ramp - BESS with Control Strategy G
It can be seen from the simulation that the BESS system has enough energy (top right black
trace) and that the power output does not reach its maximum value for this extreme case.
Despite the loss of 5 MW of wind in the first 90 seconds, the frequency only dips to 59.5 Hz.
With the proposed BESS control strategy, there were some conditions that required the BESS
to reach its maximum power rating. The BESS does still provide the 3 MW rated power until the
frequency recovers. During these events, only the January 11 at 17:00 event causes the
frequency to fall below 59.0 Hz. This event is shown below in Figure 7. The rest of the results
are shown in Appendix C.
Figure 7: Extreme Ramp BESS Maxed Power Output
Since the BESS has a comparably large energy storage rating, the BESS sizing requirement will
be based on the required power output. The wind ramp event shown in Figure 7 causes trouble
due to the near instantaneous loss of 6 MW of wind power. The BESS contributes 3 MW
resulting in a net loss of 3 MW of wind power. The resulting frequency is approximately 58.6
Hz. Seeing as this is the only wind ramp event that causes the BESS to reach its power limit
and still have a frequency dip below 59 Hz, it should not require an additional BESS module to
increase the maximum power output to 4.5 MW for this one case. This is especially true when
compared to current operation such that when a wind turbine trips near full output, the
frequency decays below 59.0 Hz and occurs much more frequently than once per year. With
these considerations, EPS recommends that two BESS modules be used to assist KEA with
frequency regulation.
5.3 Battery Expected Life Calculations
In order to determine the life-cycle cost, EPS needed to know how often the batteries would
need to be replaced. Figure 8, taken from Xtreme Powers website, describes their warranty
characteristic. For small state of charge movements, these batteries can provide hundreds of
thousands of cycles, but far fewer cycles for larger movements. EPS next determined how
large, and how many charge / discharge cycles should be expected in a given year.
Figure 8: Xtreme Power Battery Warranty Curve
Using the selected time frames from Table 2, simulations were run with double the wind output.
Using these different types of wind regimes, the simulations provided an idea of how many
cycles the batteries would go through. The thirty selected time frames represent three samples
of each type of wind event. Type 1 events are the least severe with type 10 events being the
most severe. The yearly distribution of these events shown in Table 1 can then be used to
determine the expected number of cycles for the year. For example, if a 15-minute type 1 event
causes one 1% SOC cycle, and there are 17,519 type 1 events in a year, then type 1 events
would cause 17,519 1% SOC cycles. The task is to determine the number of cycles for each
event type and combine them into a loss-of-life calculation.
EPS provided simulation results of the thirty 15-minute simulations to Xtreme Power engineers
for this loss-of-life analysis. The data provided to Xtreme Power was the power output and state
of charge in one second increments. Xtreme Power engineers analyzed the data and provided
EPS with a spreadsheet with the loss-of-life for each of the 15 minute simulations based on the
percentage of life lost. The spreadsheet they provided is attached in Appendix D.
Table 4 shows the relevant results of their calculations as well as the calculation performed by
EPS to determine the cumulative loss-of-life. The Case columns list the selected 15-minute
simulations corresponding to the times listed in Table 2. The Total Impact column lists the
impact each individual 15-minute simulation has on the battery life. The Type Mean column lists
the average Total Impact for each frequency standard deviation type. The Type Max column
lists the maximum Total Impact for each type. There are 34,040 15-minute samples throughout
the year, and the number of each wind event type is listed in the Samples column. The yearly
usage columns list the Type Mean or Type Max multiplied by the Number of Samples to provide
the impact on BESS life for each frequency type. The bottom of Table 4 lists the yearly usage
and the life expectancy of the batteries.
The results provided by Xtreme Power confirm the selection criteria used by EPS. Specifically,
the worst case conditions (types 7-10) had much more battery usage per 15-minute simulation
when compared to the lower variability conditions (types 1-3). These results confirm the EPS
selection criteria and add confidence that the standard deviation of frequency correlates with the
BESS loss of life calculations. With this confirmation, EPS could continue with the loss-of-life
analysis.
Samples
Total Impact Type Mean Type Max #of Type Type Mean Type Max
hi 0.0000000%
mid 0.0002303%
lo 0.0000000%
hi 0.0002449%
mid 0.0002449%
lo 0.0002449%
hi 0.0002449%
mid 0.0002604%
lo 0.0002449%
hi 0.0002768%
mid 0.0002604%
lo 0.0005217%
hi 0.0004906%
mid 0.0004751%
lo 0.0002604%
hi 0.0002449%
mid 0.0002604%
lo 0.0000000%
hi 0.0003130%
mid 0.0002768%
lo 0.0004000%
hi 0.0003762%
mid 0.0009804%
lo 0.0003130%
hi 0.0010288%
mid 0.0003130%
lo 0.0005433%
hi 0.0009512%
mid 0.0003538%
lo 0.0009823%
6.205241% 9.625221%
16.12 10.39
Case
1
15 Minute Yearly Usage
0.0000768% 0.0002303% 17519 1.3448126% 4.0344377%
0.0002500% 0.0002604% 4265 1.0663518% 1.1104017%
0.0002449% 0.0002449% 8244 2.0186238% 2.0186238%
0.0620271% 0.0752012%
0.0003530% 0.0005217% 2739 0.9667403% 1.4288936%
0.0004087% 0.0004906% 1446 0.5910007% 0.7094674%
0.0001684% 0.0002604% 527 0.0887489% 0.1372056%
0.0106741% 0.0137517%
Yearly Usage (% of Battery Life)
0.0005565% 0.0009804% 74 0.0411817% 0.0725467%
0.0006283% 0.0010288% 24 0.0150801% 0.0246912%
2
3
4
5
6
7
8
9
10
Battery Life (Years)
0.0007624% 0.0009823% 14
0.0003299% 0.0004000% 188
Table 4: BESS Loss of Life Calculation
The two methodologies were selected to represent an expected life-expectancy (Mean column)
and a pessimistic life-expectancy (Max column). It can be seen that depending on the method
used to calculate the life-expectancy, the batteries should last between 10 and 16 years.
Additionally, the provided case had ½ the energy capacity that the actual system would have so
the life expectancy would be even larger as the depth of discharge in the cycles would be half
as large. The life-expectancy should be used in combination with the expected BESS power
losses, and expected maintenance costs to determine the total life-cycle costs. However,
considering that 12 flywheel modules would be required to provide a similar system frequency
response, the BESS system makes more sense. The BESS makes even more sense when
looking at the system end-of-life when only the spent batteries need to be replaced. The battery
replacement cost estimate of $300,000 is only a small portion of the initial capital cost of the
battery system.
6 Conclusions
EPS makes the following recommendations based on the results of this study:
EPS recommends that KEA pursue the battery technology over the flywheel technology
based on cost.
o Approximately 12 flywheel modules would be necessary to provide the required
energy if flywheel systems are used.
o One battery system initially costs twice that of a flywheel.
o The battery bank replacement cost is a comparably low amount of $300,000.
o The expected life of the battery packs is 16 years.
The recommended control strategy is based on droop with a deadband around 60 Hz.
o The recommended droop setting for the BESS is 0.85%.
o The recommended deadband setting for the BESS is from 59.9 to 60.02 Hz.
o This would result in full power output at 59.39 Hz assuming a power rating of 3
MW.
The recommended settings for the BESS control should be evaluated with system experience,
but based on these settings the battery technology is clearly the better technology for KEAs
system.
Appendix A – Wind Data Analysis
Appendix B – Severe 2-Minute Simulations with Flywheels
Appendix C – Severe 2-minute Ramp Simulations with BESS
Appendix D – Xtreme Power BESS Loss of Life Calculation