HomeMy WebLinkAbout64987NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308
Economic Analysis Case
Studies of Battery Energy
Storage with SAM
Nicholas DiOrio, Aron Dobos, and
Steven Janzou
National Renewable Energy Laboratory
Technical Report
NREL/TP-6A20-64987
November 2015
NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308
National Renewable Energy Laboratory
15013 Denver West Parkway
Golden, CO 80401
303-275-3000 • www.nrel.gov
Economic Analysis Case
Studies of Battery Energy
Storage with SAM
Nicholas DiOrio, Aron Dobos, and
Steven Janzou
National Renewable Energy Laboratory
Prepared under Task No. SS139001
Technical Report
NREL/TP-6A20-64987
November 2015
NOTICE
This report was prepared as an account of work sponsored by an agency of the United States government.
Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty,
express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of
any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately
owned rights. Reference herein to any specific commercial product, process, or service by trade name,
trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation,
or favoring by the United States government or any agency thereof. The views and opinions of authors
expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Available electronically at SciTech Connect http:/www.osti.gov/scitech
Available for a processing fee to U.S. Department of Energy
and its contractors, in paper, from:
U.S. Department of Energy
Office of Scientific and Technical Information
P.O. Box 62
Oak Ridge, TN 37831-0062
OSTI http://www.osti.gov
Phone: 865.576.8401
Fax: 865.576.5728
Email: reports@osti.gov
Available for sale to the public, in paper, from:
U.S. Department of Commerce
National Technical Information Service
5301 Shawnee Road
Alexandria, VA 22312
NTIS http://www.ntis.gov
Phone: 800.553.6847 or 703.605.6000
Fax: 703.605.6900
Email: orders@ntis.gov
Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.
NREL prints on paper that contains recycled content.
iii
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
Executive Summary
Behind-the-meter electric-energy storage has been considered recently as a possible means of
enabling higher amounts of renewable energy on the grid. States such as California have
introduced mandates and subsidies to spur adoption. This work considers customer sited behind-
the-meter storage coupled with photovoltaics (PV) and presents case studies of the financial
benefit of customer-installed systems in California and Tennessee. Different dispatch strategies,
including manual scheduling and automated peak-shaving were explored to determine ideal ways
to use the storage system to increase the system value and mitigate demand charges. Incentives,
complex electric tariffs, and site-specific load and PV data were used to perform detailed
analysis using the free, publicly available System Advisor Model (SAM) tool. We find that
installation of photovoltaics with a lithium-ion battery system in Los Angeles and installation of
lithium-ion batteries without photovoltaics in Knoxville yields positive net-present values
considering high demand charge utility rate structures, battery costs of $300/kWh, and
dispatching the batteries using perfect day-ahead forecasting. All other scenarios considered cost
the customer more than the savings accrued. General conclusions about influential factors in
determining net present value remain elusive because our analysis shows high sensitivity of
battery economics to the complex interplay among scenario parameters and location-specific
information.
iv
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
Table of Contents
Executive Summary ................................................................................................................................... iii
1 Introduction ........................................................................................................................................... 1
2 Methods and Data ................................................................................................................................. 3
2.1 Weather and Load data .................................................................................................................. 3
2.2 Dispatch control strategies ............................................................................................................ 4
2.2.1 Manual dispatch controller ............................................................................................... 4
2.2.2 Automated dispatch controller ......................................................................................... 4
2.3 PV System ..................................................................................................................................... 5
2.4 Batteries ......................................................................................................................................... 5
2.4.1 Lithium-Ion ...................................................................................................................... 5
2.4.2 Lead Acid ......................................................................................................................... 6
2.5 Los Angeles System ...................................................................................................................... 7
2.5.1 Facility .............................................................................................................................. 7
2.5.2 Utility rate structure ......................................................................................................... 7
2.5.3 System configuration ........................................................................................................ 8
2.5.4 Incentives ......................................................................................................................... 8
2.6 Knoxville System .......................................................................................................................... 8
2.6.1 Facility .............................................................................................................................. 8
2.6.2 Utility rate structure ......................................................................................................... 8
2.6.3 System configuration ........................................................................................................ 8
2.6.4 Incentives ......................................................................................................................... 9
2.7 System Costs and Financial Parameters ........................................................................................ 9
2.8 Financial metrics ......................................................................................................................... 10
2.8.1 Net Present Value ........................................................................................................... 10
2.8.2 Payback Period ............................................................................................................... 10
3 Results and Discussion ..................................................................................................................... 12
3.1 Varying Battery Bank Size .......................................................................................................... 12
3.2 Varying Battery Chemistry ......................................................................................................... 13
3.3 Varying Storage Dispatch ........................................................................................................... 13
3.4 Best Case Summary .................................................................................................................... 14
4 Conclusions ........................................................................................................................................ 15
1
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
1 Introduction
Mandates for energy storage coupled with incentives and the high-profile introduction of
batteries for behind-the-meter storage applications have led to an increased need for tools and
analysis that evaluates financial benefit under various scenarios. In 2010 the California Public
Utilities Commission released a target of 1.3 gigawatts (GW) of energy storage in the state by
2020 [1]. The 1.3 GW target is broken up between the public utilities Pacific Gas & Electric
(PG&E), Southern California Edison (SCE) and San Diego Gas & Electric (SDG&E). Each has
specific procurement targets for transmission, distribution and customer-sited storage. Statewide,
the customer-sited storage target totals 200 megawatts (MW). California has also instituted an
incentive program for energy storage projects through its Self-Generation Incentive Program
(SGIP) [2]. 2014 incentive rates for advanced energy storage projects were $1.62/W for systems
with up to 1 MW capacity, with declining rates up to 3 MW. ConEdison in New York State also
provides an incentive of $2.10/W for battery energy storage projects completed prior to June 1,
2016 [3]. Elsewhere, other states such as Hawaii have energy storage demonstration projects in
progress [4].
Incentives offer additional financial benefit to energy storage systems, but the systems must
serve an ongoing role in providing value to customers to justify the capital expenditure. For
behind-the-meter applications, the reduction of demand charges is one way that these systems
reduce commercial customer bills. A previous study [5] used the Battery Lifetime Analysis and
Simulation Tool (BLAST) developed at the National Renewable Energy Laboratory (NREL) to
consider optimizing the size and operation of an energy storage system providing demand charge
management. Battery degradation and capital replacement costs were not considered. This study
will similarly conduct demand charge management analysis, but will focus on two specific
scenarios using NREL’s freely-available System Advisor Model (SAM) tool. SAM links a high
temporal resolution PV-coupled battery energy storage performance model to detailed financial
models to predict the economic benefit of a system. The battery energy storage models provide
the ability to model lithium-ion or lead-acid systems over the lifetime of a system to capture the
variable nature of battery replacements.
Additional value streams such as using storage to provide ancillary services within California
Independent System Operator’s (CAISO) or other markets are not considered here. For the rate
structures considered, it is assumed that net energy metering (NEM) with a monthly rollover of
excess energy is included. Some additional rules with NEM exist, particularly within Southern
California Edison [6]. The battery storage systems considered in this analysis attempt to remain
compliant with power output restrictions by restricting the battery nominal capacity to remain
lower than the photovoltaic nominal capacity. This restriction was enforced for all rate structures
considered to isolate the contribution batteries can make to minimizing demand charges.
Only commercial facilities were considered since residential utility rate structures typically do
not include demand charges. Commercial facilities in Los Angeles, CA and Knoxville, TN were
considered. For PV installations sized to serve 20% and 50% of the peak load, lithium-ion and
lead-acid battery banks of varying sizes were compared to evaluate net-present value and
payback period for the system by considering variable replacement times, utility rate structures,
and storage dispatch strategies. The analysis reveals the flexibility of SAM in evaluating
2
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
PV+Storage systems for behind-the-meter applications and highlights how systems can be
evaluated to determine financially viable configurations.
3
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2 Methods and Data
SAM is a free software tool which can perform detailed performance and financial analysis
across a variety of renewable energy technologies, including PV+Storage for behind-the-meter
analysis. Details on the PV modeling capabilities can be found in [7], while details on the battery
modeling can be found in [8]. The study uses SAM to process subhourly weather and load data,
predict PV generation, and automatically dispatch the battery to reduce peak demand charges.
The simulations are conducted over a 25 year analysis period to capture full lifetime costs
including battery bank replacements. Two facilities are studied, one in Los Angeles and one in
Knoxville. For each study, local utility rate structures are implemented and parametric analysis is
completed to evaluate the effect of PV and battery bank sizing on the system net present value.
Additionally, different battery control strategies are explored to evaluate the importance of
dispatch control on overall economics.
2.1 Weather and Load data
One-minute weather data was obtained from NREL’s Measurement and Instrumentation Data
Center (MIDC). The weather data was taken from 2012 at the weather stations nearest to the
commercial facilities studied.
Electric load data for 2012 was taken from EnerNOC’s free online database for 98 commercial
facilities [9]. The datasets provide electricity demand information in 5-minute time-steps over
one year. Figure 1 shows one day of load and irradiance data for the Los Angeles commercial
facility. The plot shows that the facility experiences higher load in the early morning and late
evening. Figure 2 shows the Knoxville facility electric load and beam irradiance for the same
day. The Knoxville site experiences higher load during the afternoon and early evening hours.
Figure 1: January 1st load and irradiance data
for LA facility
Figure 2: January 1st load and irradiance data
for Knoxville facility
4
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2.2 Dispatch control strategies
2.2.1 Manual dispatch controller
The manual dispatch controller allows a user to choose how to charge and discharge the battery
depending on the hour and month. This controller is described in detail in [8].
2.2.1.1 Los Angeles
To account for the load profile seen in Figure 1, the manual controller was programmed with the
schedule outlined in Table 1.
Table 1: LA Manual Dispatch Controller
Month Time Strategy
All 12 am – 6 am &
4 pm – 12 am
Charge from PV
Charge from Grid
Discharge up to 0.5% capacity each time step (5
minutes)
All 7am – 3 pm Charge from PV
Charge From Grid
2.2.1.2 Knoxville
To account for the load profile seen in Figure 2, the manual controller was programmed with the
schedule outlined in Table 2.
Table 2: Knoxville Manual Dispatch Controller
Month Time Profile
All 12 pm – 7 pm
Charge from PV
Charge from Grid
Discharge up to 0.5% capacity each time step (5
minutes)
All 12 am – 11 am &
8 pm – 12 am
Charge from PV
Charge From Grid
2.2.2 Automated dispatch controller
The automated dispatch controller runs in a look-ahead mode. In look-ahead mode, perfect PV
and load forecasting are performed over a period of 24 hours and the dispatch strategy is set to
reduce peak grid purchases as much as possible. This mode illustrates an upper bound on
controller performance for a 24 hour look ahead.
The automated controller is not yet publicly released within SAM. Given the predicted load and
PV profile, the automated control algorithm proceeds by computing the net grid power required
without a battery system for every time step. It sorts the powers to determine the highest grid
power required. The battery is then programmed to discharge throughout the day to reduce the
peak grid powers as much as possible before reaching the minimum state of charge.
5
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2.3 PV System
SunPower SPR-210-BLK-U modules [10] with SMA America: STP24000TL-US-10 480V
inverters [11] were selected as baseline system hardware for both case studies. Two system sizes
were considered for each location. One system was sized with the goal of providing
approximately 20% of peak power from PV during full production. The other system was sized
to provide about 50% of peak power.
2.4 Batteries
Two types of battery systems were considered, lithium-ion and lead-acid. Lead-acid batteries
have been in use for many years and are typically less expensive than lithium-ion batteries, but
lithium-ion batteries typically have better lifetime cycling properties, potentially reducing the
number of battery replacements over a system lifetime [12].
2.4.1 Lithium-Ion
Daily-cycle lithium-ion nickel manganese cobalt oxide (NMC) batteries similar to the Tesla
Powerwall batteries were selected for the lithium-ion battery bank. Table 3 shows properties for
a single Powerwall battery pack.
Table 3: Tesla Powerwall Specifications [13]
Property Value
Price $3000
Capacity 7 kWh
Power 2.0 kW continuous, 3.3 kW peak
Efficiency 92%
Voltage 350 – 450 V
Current 5.8 A nominal, 8.6 A peak
Weight 100 kg
Dimensions 1300 mm x 860 mm x 180 mm
This analysis assumed that 7 kWh of energy can be cycled daily within the minimum and
maximum state of charge limits, which are assumed to be 30% and 100% respectively. This
implies a full capacity of 10 kWh which is assumed to cost $3000, implying a price of
$300/kWh. We assume this cost includes installation and permitting, but additional analysis
would be required to incorporate these costs at a specific site.
This analysis assumed that the battery begins with 7 kWh of daily cycle life and is replaced once
the maximum capacity has degraded to 70% of the original value. As detailed lifetime cycling
information is not readily available, we assumed that the batteries can be fully cycled daily for
five years beyond the stated 10-year warranty period before the battery capacity degrades to 70%
of the original installed capacity, at which point the battery bank must be replaced. This implies
about 365 cycles per year, for 15 years, or 5475 cycles.
6
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2.4.2 Lead Acid
Deep cycle valve-regulated lead-acid (VRLA) absorbed glass mat (AGM) batteries were selected
for the lead acid battery bank similar to the Outback Energy Cell. Table 4 shows the Outback
EnergyCell properties.
Table 4: Outback EnergyCell 200RE Specifications [14] [15]
Property Value
Price $425
Capacity 1.680 kWh (4 hour discharge)
Power 420 W (4 hour discharge)
Efficiency Unlisted
Voltage 12 V
Current 35 A (4 hour discharge), 30 A (charging)
Weight 60 kg
Dimensions 320 mm x 551 mm x 126 mm
The lead-acid system was similarly allowed to cycle from 30% to 100% state-of-charge, or
approximately 1.18 kWh per battery at a four-hour discharge rate. The cost of one battery was
found to be about $425, implying a price of $255/kWh. This cost again assumes that installation
and permitting are included, though additional analysis would need to be performed for a
specific site. No efficiency was listed for the battery, so single point AC/DC and DC/AC
conversion efficiencies of 92% were assumed. These efficiencies were chosen to remain
consistent with the lithium-ion system.
The battery bank is assumed to be replaced when the maximum capacity has degraded to 70% of
the original value. The following cycling information was used from the battery spec sheet [14].
Table 5: Outback EnergyCell 200 RE Lifetime
Average Depth-of-Discharge (%) Cycles Elapsed Relative Maximum Capacity (%)
10 6200 80
20 5700 80
50 1800 80
80 600 80
100 425 80
As the battery is allowed to consistently discharge 70% of its energy, the expected lifetime
before degradation to 70% of maximum capacity and replacement is about 1400 cycles, as
shown in Table 5. The “Relative Maximum Capacity” is the maximum capacity of the battery
relative to the original maximum capacity, which changes over time as cycles elapse.
7
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2.5 Los Angeles System
Due to favorable climate, incentives for battery storage systems, and its utility rate structures,
California provides an ideal location for performing a detailed financial analysis on the merits of
installing a battery bank with PV.
2.5.1 Facility
EnerNOC facility number 384 was examined for this study of the hypothetical costs and benefits
of battery storage. The 27,625 ft2 grocery store/market is located in Los Angeles. The facility had
an annual peak load of about 247 kW in 2012 and no photovoltaic or battery storage system.
2.5.2 Utility rate structure
After consideration of several commercial rate structures with demand charges, Southern
California Edison’s TOU-GS-2 rate structure was selected [16] for analytic purposes. This rate
structure serves medium-size commercial and industrial facilities with peak loads between 20-
200 kW. The structure was chosen because after the installation of PV and batteries, the load of
the facility under study falls within the rate structure load range. Option B of the rate structure
was selected to further study controller behavior with time-of-use demand charges. The rate
structure is monthly such that demand charges are reset at the end of each month. The utility rate
structure as reproduced from the Utility Rate Database (URDB) [17] is shown in Table 6). We
did not gather data on the facility’s actual tariff.
Table 6: Southern California Edison TOU-GS-2 Option B Rate Structure
Charge Time Cost
Fixed charge All 267.4 ($)
Base demand charge All 13 ($/kW)
TOU demand charge June – September
8 am – 12 pm, 6pm – 11 pm. (Monday-Friday)
All day (Saturday-Sunday)
4.88 ($/kW)
June – September
Noon – 6pm (Monday-Friday)
16.69 ($/kW)
All other times 0 ($/kW)
TOU energy charge January – May, October – December
12 am – 8 am, 9 pm – 12 am (Monday-Friday)
All day (Saturday-Sunday)
0.06417 ($/kWh)
January – May, October – December
8 am – 9 pm (Monday-Friday)
0.08487 ($/kWh)
June – September
12 am – 8 am, 11 pm – 12 am (Monday-Friday)
All day (Saturday-Sunday)
0.05939 ($/kWh)
June - September
8 am – Noon, 6 pm – 11 pm (Monday-Friday)
0.08047 ($/kWh)
June - September
Noon – 6 pm (Monday-Friday)
0.12343 ($/kWh)
8
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2.5.3 System configuration
The peak load for the Los Angeles facility was 246 kW. To serve approximately half of this peak
load, a PV system of 125 kWdc nameplate capacity was considered. A smaller 50 kWdc
installation was also considered. For each system, a parametric set of battery sizes ranging from
0 kWh to 150 kWh was considered.
2.5.4 Incentives
Through its Self-Generation Incentive Program, the State of California offers advanced energy
storage incentives of $1.62/W, up to a system size of one MW [2]. One restriction on the
incentive is that the project installer cannot receive an incentive greater than 60% of eligible
project costs. The rated capacity of the energy storage system is calculated as the average
discharge power output over a two-hour period. For storage projects coupled with generation
technologies such as PV, the rated capacity of the storage cannot be larger than the rated capacity
of the PV system. State incentives for PV are no longer available within the SCE service area
[18], but the federal residential renewable energy tax credit of 30% is available [19].
2.6 Knoxville System
To provide contrast to the heavily incentivized California system, a primary/secondary school
near Knoxville, TN was considered.
2.6.1 Facility
The school listed as EnerNOC facility 101 is 274,418 ft2 and had a peak load of about 1300 kW
in 2012.
2.6.2 Utility rate structure
For a commercial facility with a peak load of about 1300 kW, the Knoxville Utilities Board
offers the general power rate schedule GSA [20]. Values were again reproduced from the URDB.
Charge Time Cost
Fixed charge All 140 ($)
Base demand charge January – May, October - December
First 1000 kW / Over 1000 kW
11.76 / 13.18 ($/kW)
June – September
First 1000 kW / Over 1000 kW
12.52 / 13.94 ($/kW)
Energy charge All year 0.06641 ($/kWh)
2.6.3 System configuration
The peak load for the Knoxville facility was 1296 kW. One set of simulations considered a PV
installation of 270 kW, while a second set considered a 650 kW system. For each simulation, a
parametric set of battery sizes ranging from 0 kWh to 450 kWh was considered.
9
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2.6.4 Incentives
The state of Tennessee offers an incentive that rebates 100% of sales and use tax on
photovoltaics in addition to other renewable technologies [18]. The federal energy investment
tax credit of 30% was also applied.
2.7 System Costs and Financial Parameters
For the analysis there are many financial parameters to be considered which could impact the
value of installing a battery system. Battery costs are captured on a $/kWh basis as reported by
vendors. Installation labor, margin, operation and maintenance, and other costs are captured as a
function of the size of the installed PV system using the defaults in SAM. The battery bank is
assumed to be DC connected such that battery power output is inverted to AC using the same
inverter as the PV system.
Table 7: System Costs
Variable Value
Module cost 0.71 $/Wdc
Inverter cost 0.21 $/Wdc
Battery cost $300/kWh Lithium Ion
$255/kWh Lead Acid
Balance of system equipment 0.57 $/Wdc
Installation labor 0.15 $/Wdc
Installer margin and overhead 0.75 $/Wdc
Permitting 0.06 $/Wdc
Operation and Maintenance 20 $/kW-yr
To compute the net present value of the system for each scenario considered, the analysis is
conducted using the values in Table 8.
10
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
Table 8: Financial parameters
Variable Value
Analysis period 25 years
Debt percent 100%
Loan term 25 years
Loan rate 7.5% per year
Inflation rate 2.5%
Real discount rate 5.5%
Nominal discount
rate
8.14%
Depreciation 5-yr MACRS
Federal income tax 28% per year
State income tax 7% per year
Sales tax 5% of installed cost
(None in Knoxville after incentive)
2.8 Financial metrics
To evaluate the economic performance of a system, two financial metrics were considered for
each simulation. The metrics described are implemented according to [21].
2.8.1 Net Present Value
The net present value (NPV) is a way to examine costs and revenues while accounting for the
time value of money. If the NPV of a system is positive, then the investment is predicted to
provide a return on investment greater than the initial and ongoing cash expenditures associated
with ownership of the system. A negative NPV indicates the returns are worth less than the cash
outflows and the investment does not show a financial benefit, although unquantified benefits
may be present. NPV is a valuable metric to consider for PV coupled with storage systems, since
variable battery bank replacements (cash outflows) can be captured and discounted depending on
the year the replacement occurs.
2.8.2 Payback Period
The payback period (PBP) is the time in years it takes for project savings in years two and later
of the project to equal or exceed the initial cost. This metric is included because of its ability to
quickly communicate tangible value of a project. A system which yields a very short payback
period is typically a profitable one because subsequent years of the project result in pure revenue
for the system owner. A system which cannot be paid back over the lifetime of the system is a
poor investment because money is still owed when the system is retired and no longer able to
generate revenue.
One complication of using the payback period to evaluate PV coupled with storage is that battery
bank replacements vary in timing and may become necessary during the system lifetime. A
project which has paid off its debt may eventually have to incur more debt to replace a battery
11
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
bank. The payback period is reported as the first time where the project savings exceeds the debt,
but care must be taken to also consider additional cash expenditures in later years.
12
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
3 Results and Discussion
3.1 Varying Battery Bank Size
To examine the financial implications of differently sized battery installations for demand charge
reduction, the automated peak shaving controller was used with a fixed size PV installation and
varying sizes of battery bank. Modifying the battery bank size also changed the battery bank
voltage and incentive level. Varying the lithium-ion battery bank size for two PV system sizes
resulted in changing NPVs and PBPs as shown in Figure 3 for California and in Figure 4 for
Tennessee.
Figure 3: Los Angeles, lithium ion system with
automatic dispatch
Figure 4: Knoxville, lithium ion system with
automatic dispatch
For Los Angeles, it is apparent that increasing the battery bank size up to about 100 kWh
improves the net present value and payback period for this particular system using perfect day-
ahead forecasting of weather and electric load. As the battery size increases above 100 kWh, the
additional battery cost is greater than the reduction of demand charges. Reducing the size of the
PV system, or eliminating it altogether, improves the net present value and payback periods,
because most load occurs early in the morning and late in the evening when PV production is
low. The payback period for the 125 kW PV system declines from about 17 years to 14 years
when the battery bank is increased from 100 kWh to 110 kWh. This is because of increased
demand charge reduction, which improves the project savings and yields a cumulative savings
greater than project debt before a battery bank replacement occurs. For smaller battery systems,
the battery bank is replaced before the savings exceeds the project debt, prolonging the payback
period.
For Knoxville, no battery size adds financial value for the system studied when accompanied
with PV panels. When a battery is installed without any PV there is a positive net present value
for all battery sizes considered. The simulation does not compute payback periods greater than
the analysis period such that 25 years is the maximum payback period computed, and could
indicate an even longer payback.
13
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
3.2 Varying Battery Chemistry
Switching to the lead-acid battery bank and examining alternately sized battery banks revealed
major differences in financial metrics compared to the lithium-ion results above. For the lead-
acid battery system, the daily cycle process results in battery bank replacements every six or
seven years. Over the course of 25 years, these more frequent replacements result in large capital
costs. Consequently, installation of this system for DCR under the current assumptions does not
add financial value for either facility for the two PV system sizes considered. Figure 5 shows that
there is not a positive net-present value for any configuration considered. The payback period
increases as the battery bank size increases and eventually reaches the system lifetime. Figure 6
illustrates the cash flows of a lead-acid battery scenario.
Figure 5: Los Angeles, lead acid system with
automatic dispatch
Figure 6: Los Angeles, 90 kWh lead-acid system,
50 kW PV, cash flow
In the first year, a large cash influx occurs as cash incentives and tax savings are accrued. Five-
year MACRS depreciation contributes to net positive cash flows over the first three years.
Eventually depreciation is not enough to offset the debt payments. In year seven, the first battery
replacement occurs resulting in a large negative cash flow. Three additional battery bank
replacements occur over the rest of the analysis period, offsetting the financial benefit of
installing storage to reduce demand charges.
3.3 Varying Storage Dispatch
Previous results have considered using a perfect day-ahead forecasting to automate battery
dispatch for peak shaving. The present version of SAM offers a manual dispatch controller that
allows a user to specify when to charge and discharge the battery, and how much to discharge.
The scheduling is possible on an hourly basis for each month. The manual dispatch controller
was used to compare against the automatic strategy. Figure 7 and Figure 6 show that the manual
controller is insufficient to generate a positive net-present value for any battery size regardless of
whether a PV system is installed in Los Angeles or Knoxville at the system configurations
previously described. The payback period shown cannot exceed the analysis period of the
14
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
system, but actual payback period would keep increasing beyond 25 years as the battery bank
energy increases.
Figure 7: Los Angeles, lithium ion system with
manual dispatch
Figure 8: Knoxville, lithium ion system with
manual dispatch
The manual dispatch controller is insufficient for peak shaving applications because of the
variable nature of load demand. Though the schedule was tailored to dispatch the battery in
typical times of high demand, the controller naively follows its preset schedule even when other
spikes occur. Differences in monthly demand charges between these two scenarios reflect spikes
that could have been predicted by a day-ahead forecast but are not included in the typical
demand profile used to set the manual dispatch. Because demand charges are based on the
highest peak over the month, any unexpected spike which cannot be offset by the battery negates
any savings that may be accrued. Similar differences between perfect day-ahead forecasting and
manual dispatch control are obtained regardless of the location or battery chemistry.
3.4 Best Case Summary
For the two locations studied, the following configurations yielded the best net-present value.
Table 9: Best case summary
Los Angeles Knoxville
Net Present Value $31,874 $60,731
Battery Bank Size 110 kWh / 55 kW 300 kWh / 150 kW
PV Array Size 50 kW 0 kW
Dispatch Strategy Automatic Automatic
Battery Chemistry Lithium Ion Lithium Ion
15
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
4 Conclusions
The financial impact of adding PV+Storage to two commercial facilities in different locations
was examined using SAM. Multiple sizes of battery banks and PV systems were considered.
Lithium ion and lead acid batteries were used to evaluate the tradeoffs between cost and cycle
life. With a perfect 24 hour forecast for weather and load data, an automated dispatch controller
was used to reduce demand charges and compared against a simpler manual control strategy.
Installing PV in Knoxville yielded a negative NPV for the sizes considered. Adding a lithium-ion
battery system with automatic peak shaving control mildly improved the investment but did not
offset the large negative NPV of the system. With no PV system, installing a lithium-ion system
in Knoxville yielded a positive NPV for all battery sizes considered. Installation of a lithium-ion
battery system in Los Angeles while using the automatic peak-shaving strategy yielded a positive
NPV for most system sizes, illustrating that battery energy storage may prove valuable with
specific utility rates, ideal dispatch control, long cycle life and favorable battery costs. The lead-
acid batteries considered needed to be replaced more frequently, resulting in large recurring
capital costs that led to negative NPVs for both locations and dispatch strategies. Using the
manual dispatch strategy proved insufficient for consistently shaving load peaks regardless of
location or battery type.
It is clear that many details should be considered when evaluating the economic potential of
behind-the-meter storage for demand-charge reduction. In addition to considering the weather
and load profiles of the site, it is necessary to evaluate the utility rate structure, and determine
whether the addition of battery storage can be leveraged to reduce costs enough to justify the
upfront capital expenditure and replacement costs. The greatest financial benefit will be derived
at certain sizes of PV and battery couplings, requiring optimization or parametric analysis.
Further complications result when considering how to dispatch the battery to maximize its
benefit, especially because load and weather forecasts are uncertain, and battery lifetime depends
on cycling behavior. SAM provides a means to begin evaluating these complex problems.
16
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
References
[1] California Public Utilities Commission, "Assembly Bill 2514 Decision," 3 September 2013.
[Online]. Available:
http://docs.cpuc.ca.gov/PublishedDocs/Published/G000/M078/K912/78912194.PDF.
[2] California Public Utilities Commission, "2014 Self-Generation Incentive Program
Handbook," CPUC, 2014.
[3] ConEdison, "Demand Management Initiatives," 2015. [Online]. Available:
http://www.coned.com/energyefficiency/demand_management_incentives.asp.
[4] D. Shimogawa, "Hawaii Electric to use energy storage project developed by California
startup," Pacific Business News, 11 September 2014. [Online]. Available:
http://www.bizjournals.com/pacific/blog/morning_call/2014/09/hawaiian-electric-to-use-
energy-storage-project.html.
[5] J. Neubauer and M. Simpson, "Deployment of Behind-The-Meter Storage for Demand
Charge Reduction," National Renewable Energy Laboratory, Golden, 2015.
[6] Southern California Edison, "NEM FAQ," 2015. [Online]. Available:
https://www.sce.com/wps/portal/home/residential/generating-your-own-power/net-energy-
metering/faq/%21ut/p/b1/hdBNb4JAEAbgX8OVfZdFSntbAsWlFKr0A_di0NCVBlmDVP5
-
10YPtV9zm8nzTjJDJCmJ7KpDo6qh0V3VHnvpLakf86koIPwo8SCCh_vcDVLqPrkGLAz
AL8XxX_6FyAsSsMgQ5Bmfc9yFziWIb2.
[7] P. Gilman, "SAM Photovoltaic Model," National Renewable Energy Laboratory, Golden,
2015.
[8] N. DiOrio, A. Dobos, S. Janzou, A. Nelson and B. Lundstrom, "Technoeconomic Modeling
of Battery Energy Storage in SAM," National Renewable Energy Laboratory, Golden, 2015.
[9] EnerNOC, "2012 Commercial Energy Consumption Data," 2013. [Online]. Available:
https::/open-enernoc-data.s3amazonaws.com/anon/index.html. [Accessed 24 August 2015].
[10] SunPower, "SunPower," [Online]. Available: http:://us.sunpower.com. [Accessed 21
August 2015].
[11] SMA America, "SMA America," 2015. [Online]. Available: http://www.sma-america.com/.
[12] D. Linden and T. Reddy, Linden's Handbook of Batteries - 4th Edition, New York :
McGraw Hill, 2011.
[13] Tesla Motors, "Powerwall," 2015. [Online]. Available:
17
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
http://www.teslamotors.com/powerwall. [Accessed 14 August 2015].
[14] Outback Power, "Energy Cell RE," [Online]. Available:
http://www.outbackpower.com/outback-products/energy-storage/item/energycell-
re?category_id=442. [Accessed 17 August 2015].
[15] altE Store, "Outback Power EnergyCell 200RE 12V 178 AH AGM Battery," [Online].
Available: http://www.altestore.com/store/Deep-Cycle-Batteries/Batteries-Sealed-
Agm/Outback-Power-EnergyCell-200RE-12V-178AH-AGM-Battery/p9857/. [Accessed 17
August 2015].
[16] Southern California Edison, "General Service Demand GS2," 30 Jan 2015. [Online].
Available: https://www.sce.com/NR/sc3/tm2/pdf/ce30-12.pdf. [Accessed 14 Aug 2015].
[17] OpenEI, "Utility Rate Database," [Online]. Available:
http://en.openei.org/apps/USURDB/rate/view/539fc260ec4f024c27d8b167#2__Demand.
[Accessed 14 August 2015].
[18] DSIRE, "California Solar Initiatives - PV Incentives," 18 June 2015. [Online]. Available:
http://programs.dsireusa.org/system/program/detail/2362. [Accessed 17 August 2015].
[19] U.S. Department of Energy, "Residential Renewable Energy Tax Credit," [Online].
Available: http://energy.gov/savings/residential-renewable-energy-tax-credit. [Accessed 17
August 2015].
[20] Knoxville Utilities Board, "Electric Rate Schedules," [Online]. Available:
http://tinyurl.com/nte9t36. [Accessed 24 August 2015].
[21] W. Short, D. J. Packey and T. Holt, "A Manual for the Economic Evaluation of Energy
Efficiency and Renewable Energy Technologies," National Renewable Energy Laboratory,
Golden, 1995.