NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Analyzing the Effects of Climate and 

Thermal Configuration on 

Community Energy Storage Systems

Jeremy Neubauer, Ahmad Pesaran

National Renewable Energy Laboratory

Daryl Coleman, Deeyu Chen

Southern California Edison

Electrical Energy Storage Applications and Technologies 

(EESAT) Conference

October 20 – 23, 2013

San Diego, California

NREL/PR-5400-60290
Introduction

System Model

Duty Cycle

Results

Conclusions
3

Motivation & Objective

• Motivation: Community Energy Storage (CES) is a highpotential application for grid-connected storage

• Challenge: Requirements for active thermal 

management systems may increase maintenance 

requirements beyond what utilities are willing to 

accept

• Objective: Assess the impact of different climates and 

thermal management strategies on Community Energy 

Storage (CES)
4

Approach

• Build a realistic lifetime model for CES that 

includes thermal, electrical, and degradation 

response characteristics

• Apply real-world climate data and residential 

load data with a representative peak-shaving 

control algorithm to investigate system response

• Analyze the impact of different climates and 

thermal configuration on CES performance
Introduction

System Model

Duty Cycle

Results

Conclusions
6

Lumped Capacitance Thermal Network
7

Climate Data

• Apply typical meteorological year 

data to simulations

o Ambient temperature

o Solar irradiance

o Soil temperature at 40” below 

grade calculated using 20 day 

running average of ambient 

temperature

• Select locations to provide a range 

of relevant conditions

o Minneapolis, MN: Low average 

temperatures and solar irradiance

o Los Angeles, CA: National average 

ambient temperature and high 

solar irradiance

o Phoenix, AZ: High ambient 

temperature and extremely high 

solar irradiance

0 50 100 150 200 250 300 350

0

5

10

15

20

25

30

35

40

45

Day of Year

Ambient Temperature (degC)

Phx, Hourly

Phx Daily

LA, Hourly

LA, Daily

0 50 100 150 200 250 300 350

0

200

400

600

800

1000

1200

Day of Year

Solar Irradiation (W/m/m)

Phx, Daily Avg

Phx Daily Max

LA, Daily Avg

LA, Daily Max
8

Thermal Configurations

• Greenhouse

o Parameters selected to be representative of a scenario where 

solar irradiation has a large effect on battery temperature

• Shaded Greenhouse

o Same as Greenhouse, but with solar irradiance reduced by 60% 

to be representative of a scenario where ambient temperature 

is the main environmental factor affecting battery temperature

• Vault

o Parameters selected to be representative of a best case passive 

thermal configuration where battery temperature closely tracks 

soil temperature

o WARNING: Not all vaults are created equal! Poorly designed 

vaults may be more thermally similar to our greenhouse 

scenario (or even worse)
9

Electrical Configurations

• One 75-mile BEV battery

o Modeled after Saft VL41M li-ion cells scaled to 60.5 Ah per cell, 

placed 100 in series yielding a ~22.1 kWh total energy

• Two starting States of Health (SOH)

o Representative of a new or a used BEV battery as calculated 

using the average SOH after 10 years of Los Angeles BEV driving 

in a charge-at-home scenario by a set of 91 likely BEV drivers

• One available power

o 50 kW inverter limit, but real-time battery power is limited by 

voltage and SOC factors as well. 

• Three available energies

o Varies the maximum DOD allowed, which will impact duty cycle 

and battery life.

• Semi-empirical, physically justified battery life model

o Based on extensive laboratory life test data for a nickel cobalt 

aluminum (NCA) lithium (Li)-ion battery chemistry

o Calculates capacity loss and resistance growth in both 

operational and storage conditions considering time, number of 

cycles, depth of discharge, state of charge (SOC), voltage, & 

temperature

o See K. Smith, et al, “Comparison of Plug-In Hybrid Electric 

Vehicle Battery Life Across Geographies and Drive Cycles, SAE 

2012-02-0666

Available Energy Nameplate Max 

DOD

17.7 kWh 80%

13.3 kWh 60%

8.9 kWh 40%

Initial SOH

New 

Battery

Used Battery

∆Q1 0 -0.25

∆Q2 0 -0.04

∆R1 0 0.14

∆R2 0 0.11
Introduction

System Model

Duty Cycle

Results

Conclusions
11

Peak Shaving Algorithm

• Objective: Maximize the reduction in peak load 

using the available power and energy of the 

battery on a daily basis

• Approach: Forecast future demand and PV 

production, then optimize peak load reduction 

within available battery energy and power 

constraints. 

• Notes:

o Perfect forecasts are assumed

o Peak load reduction optimization uses a 48 hour time 

horizon and a memory only to the beginning of day. 
12

Example Peak Shaving Algorithm Results

Example only. This is not demand data used throughout the rest of this study.
13

Residential Load Data

• XCEL provided data from 58 houses in Boulder, 

CO

o >1 year of data at 1 minute resolution for each house 

on each transformer

• Preliminary analysis suggests that CES service 

value is greatest when one unit serves many 

houses, so we aggregate the demand for all 58 

houses into one single demand profile. 

No. of Houses Average daily 

Energy

Average 

Power

Peak Annual 

Power

58 834 kWh 34.7 kW 88.5 kW
Introduction

System Model

Duty Cycle

Results

Conclusions
15

Minimum Daily SOC vs. Year

0 2 4 6 8 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Year

Minimum Daily SOC

Example 2: New battery with a 

17.7 kWh available energy window

(80% DOD @ BOL)

System reaches 0% SOC 

in less than 3 years. 

Duty cycle becomes less 

aggressive.

0 2 4 6 8 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Year

Minimum Daily SOC

Example 1: New battery with a 

13.3 kWh available energy window

(60% DOD @ BOL)

System takes more than 

10 years to reach 0% 

SOC. Duty cycle is 

consistent through life.
16

Ability to Maintain a Consistent Duty Cycle

80% DOD 60% DOD 40% DOD

New Used New Used New Used

Greenhouse 1.8 0.8 6.8 3.8 10.0+ 10.0+

Phoenix Vault 2.8 0.2 9.8 5.8 10.0+ 10.0+

Shaded Greenhouse 2.8 0.8 8.8 4.8 10.0+ 10.0+

Greenhouse 2.8 0.2 10.0+ 6.8 10.0+ 10.0+

Los Angeles Vault 3.8 0.2 10.0+ 7.8 10.0+ 10.0+

Shaded Greenhouse 3.8 0.2 10.0+ 7.8 10.0+ 10.0+

Greenhouse 3.8 0.2 10.0+ 7.8 10.0+ 10.0+

Minneapolis Vault 4.8 0.2 10.0+ 9.8 10.0+ 10.0+

Shaded Greenhouse 4.8 0.2 10.0+ 9.8 10.0+ 10.0+

Years to first 5% thermodynamic SOC crossing

All 40% DOD cases and 

some new-battery 60% 

DOD cases maintain a 

consistent duty cycle 

through all 10 years.

No 80% DOD cases can 

maintain a consistent 

duty cycle through all 10 

years.
17

Average Daily Temperature Phoenix Los Angeles Minneapolis

Effect of DOD and 

battery age on 

thermal response 

are small

Greenhouse shows 

significantly elevated 

average temperatures 

in Los Angeles

Minimal impact of thermal 

configuration on average 

temperatures in 

Minneapolis

Greenhouse Shaded Greenhouse Vault
18

Capacity

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

Relative Capacity

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

Relative Capacity

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

Relative Capacity

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10

0.4

0.5

0.6

0.7

0.8

0.9

1

Phoenix Los Angeles Minneapolis

80% DOD 60% DOD 40% DOD

Greenhouse Shaded Greenhouse Vault

 New batteries Used Batteries

Time (yrs) Time (yrs) Time (yrs)
19

Power & Max Temperature

• Case: Los Angeles, 

60% DOD, Shaded 

Greenhouse, year 1

• Very few power peaks 

outside of +/- 20 kW 

are observed

• Temperature spikes 

correlate with power 

spikes, which could 

induce operational 

limitations

0 50 100 150 200 250 300 350 400

-6

-4

-2

0

2

4

x 104

Day of Year

Inverter Power (W, negative = discharge)

0 50 100 150 200 250 300 350 400

5

10

15

20

25

30

35

40

Day of Year

Battery Temperature (deg C)
Introduction

System Model

Duty Cycle

Results

Conclusions
21

Conclusions

• Greenhouse effects can notably increase battery 

degradation (especially in warm sunny climates)

• Simple solar shading can work just as well as the best 

vault designs with respect to reducing degradation (up to 

~8% additional capacity retention relative to our 

greenhouse scenario)

• Constraining inverter power could control temperature 

spikes and reduce hardware cost with minimal impact on 

peak-shaving duty cycle

• Difference in new and used battery capacity after 10 years 

of CES service varies by only 6 to 10%, suggesting there 

may be little difference in revenue generated by new and 

used systems.
22

Future Work

• Instrument real-world CES 

installations to improve and validate 

thermal models

• Study the ability of active cooling 

systems to extend battery life

• Quantify impacts of battery 

degradation on service value
23

Acknowledgements 

• This activity is funded by the DOE 

Vehicle Technologies Office, Energy 

Storage Technology and Southern 

California Edison

• We appreciate the support provided 

by DOE program managers

o David Howell

o Brian Cunningham

• Special thanks to Xcel Energy for 

providing residential load profiles

• Technical questions regarding this 

project should be directed to Jeremy 

Neubauer at 

jeremy.neubauer@nrel.gov