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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
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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 behindthe-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

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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

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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

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PV+Storage systems for behind-the-meter applications and highlights how systems can be 

evaluated to determine financially viable configurations.
3

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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
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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

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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.
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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.
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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

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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

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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

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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

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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.
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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 dayahead 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

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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 leadacid 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. Fiveyear 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 
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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 leadacid 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.

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