This document summarizes the use of lithium-ion battery energy storage systems in Queensland's regional electricity distribution network. It discusses how a system called GUSS (Grid Utility Support System) was developed to provide energy storage and help regulate voltages on long rural feeder lines. Near real-time monitoring of the batteries provides insights into their performance and how they impact the network. Sensor data from the batteries and surrounding infrastructure is analyzed to identify issues and optimize the systems. The data collected also helps engineers understand how the batteries age over time in different usage conditions.

1. Analytics for utility-operated
lithium-ion energy storage
Monitoring the performance of grid-connected
batteries in the Queensland distribution network
Dr David Ingram 7 December 2017

2. Overview
● Regional electricity supply in Queensland and the need for upgrades
● Lithium ion battery terminology
● The Grid Utility Support System (GUSS)
● Remote data collection and sensor fusion
● Examples of how access to near-real-time data solved engineering problems
● Future opportunities
● Conclusions
2

3. Acknowledgments
● Ergon Energy for permission to use
photographs and graphs
● Former colleagues/managers
 Steve Richardson
 Michelle Taylor
 Jason Hall
● Data sources
 Spatial data from qldspatial.qld.gov.au
 Ergon Energy for sub-transmission and
distribution substation and feeder data
 Geoscience Australia for transmission line data
3
Michelle, Jason and
Steve at the 2016
Australian Engineering
Excellence Awards

4. Supply to regional Queensland
and the need for energy storage
4

5. Regional supply in Queensland
● Ergon
 728 000 customers
 118 000 km of distribution
 1 836 500 km2
 5.2 customers/km
● Single Wire Earth Return (SWER)
 25 000 customers
 62 500 km of lines
 500 400 km2
 0.4 customer/km
5

6. SWER networks
● Characteristics
 12.7 kV and 19.1 kV
 Long spans with high tension
 Highly resistive, R/X is 5 to 10
 Long distances
 Point-of-load transformers
● Issues
 Increased load
 Sensitive electronic devices
 Voltage regulation
6
Photo by Bernard S. Jansen (Wikimedia Commons),
CC BY 2.5

7. Example of multiple SWERs on one feeder
● “Alpha” Feeder
 1890 circuit km
 29 000 km2
 ~900 connections
 2.3 MVA peak
● 8× SWER Systems
7
Satellite background from ESRI Global Imagery

8. Isolation & distance
take on new meaning
in remote parts of
Queensland
8

9. Grid Utility Support System
(GUSS)
9

10. Introducing GUSS
● 25 kVA inverter
● 4 quadrant (±P, ±Q) operation
● 100 kWh of energy storage
● Skid-mounted
● Remote control and alarming
● On-board data collection
● INMARSAT/3G modem
● Now in Overhead Construction
Manual
10
Inverter Controls
Battery
Photo courtesy of Ergon
Energy

11. Commissioning
philosophy
● New approach
● Used Maryborough workshops
● Remote access tools used
● Proved communication & data
processing systems worked
● Minimised on-site time
11
Photo courtesy of Ergon
Energy

12. New technology
often benefits from a
new approach
12

13. Installation
locations
● Thirteen sites
● 9x 25 kVA and 4x 50 kVA
● 12.7 kV and 19.1 kV
SWER
● Mix of 3G and Satellite
communications
13

14. First
installation
● “Weir” on a Charters
Towers feeder
● 50 kVA (2× GUSS)
● 80% along the feeder
● Satellite modems
14
Satellite background from ESRI Global Imagery
GUSS

15. Twin GUSS installation
15
Photo courtesy of Ergon
Energy

16. Lithium-ion battery terminology
16

17. Building a battery system
17
StringModuleCell
System
Photos courtesy
of Ergon Energy

18. The need for balance
● Safety critical function
● Cell voltages must be similar
● Worst-case limits battery capacity
● Automatic balancing through discharging
● Intelligent monitoring
 SMU monitors all cells in module
 BMS monitors all modules
● Series/parallel combo of 784 cells
● Total voltage of 706 V
18

19. Lithium-ion behaviour
19
Energies 2012, 5(8), 2952-29
doi:10.3390/en5082952
CC BY 3.0 Noshin Omar, Mohamed Daowd, Peter van den Bossche, Omar Hegazy, Jelle Smekens, Thierry

20. Remote data collection
20

21. Required skills
21

22. Reasons for monitoring
● Monitor string balance
● Provide advanced warning
● Collect engineering data in
parallel with operating data
● Fine tuning of advanced
control algorithms
● Assess effect on network
● Verify that contract technical
specification is met
22
Photo courtesy of Ergon
Energy

23. Technical constraints
● Satellite data is expensive
● Need to be selective
● Couldn’t alter BMS or PCS
software
 Safety critical systems
● Pre-process of data before
transmission to reduce size
● Selection of file transfer protocols
 Effect of latency
 Compression
 Cyber-security
23
Inmarsat BGAN M2M
coverage
Photo from WideEye

24. Sensor fusion
● Multiple intelligent devices on board
 Energy meter
 Power Conversion System
 Battery Monitoring System
● Grid monitoring
 Isolation transformer monitors
 Series step regulators
 Automatic circuit reclosers
 Power quality meters
● Combine data for greater insights
24
Photos courtesy of Ergon
Energy
Cooper
Industries

25. Data sources and transfer method
● Report By Exception (RBE)
 Alarms and events
 PCS analogues beyond thresholds
 DNP 3.0
● Daily batch transfer by file
 String data
 Module data
 Cell data
 SCP
● Daily batch transfer by Metering
 Energy meter data
 Proprietary metering protocol
25

26. Workflow
● Daily download of data in shared DB
 Download each GUSS sequentially
 Feed data into corporate repository
● Process the data and generate graphs
 Series of charts with different focus
 Provide a summary website
 Reduces server workload
● Weekly and monthly processing
 Examine long term trends
 Data archiving
26

27. Automate whatever
you can, and let
people interpret
results
27

28. Battery monitoring
● System-level monitoring
 DC bus voltage
 State of Charge
 Battery string currents
 Range of cell voltages
 Maximum/minimum cell temps
● Module-level monitoring
 Voltage/temp variation from the
median
 Mean/median voltages &
temperatures
● Cell-level monitoring
 Voltage variation from the median
28
Photo courtesy of Ergon
Energy

29. Performance assessment
● Look at the “fleet” individually and as a whole
 Watch for abnormalities
● Too much data to look at in numerical form
 Visualisation is key
 Catch potential faults early
● Use data from multiple sources
 No one source has all the required information
● Visualise data in a variety of ways
29

30. Examples of performance
monitoring
30

31. Module-level voltage and temperature
31
Voltage variation Temperature variation
Voltage averages Temperature averages

32. Temperature variation within string
32
Temperature variation from the median

33. String 1 module with low voltage
33
String 1 voltage variation String 2 voltage variation
String 1
voltage average
String 2
voltage average

34. Keep an open mind
when looking at data
for the first time
34

35. Improvement to
customer voltages
● Fast transient response
● Real & reactive power
● Better for electronic devices
35
Line voltage
Inverter output

36. Cell voltages
● Potential module fault in String 2
● Module 19
● Cells 1-4, 8-14
36

37. Many ways
to visualise
● Potential module
fault in String 2
● Module 20
● Cell 9
● Outliers can be
masked by large
variation
● Expect this with
daily cycling
37

38. Weekly and monthly review
38
Temperatures
Battery capacity
Battery limits
Network performance

39. Future opportunities
39

40. Modelling of battery aging
● Building a library of operation
over a range of conditions
● No two sites are the same
● Cell-level and module-level
logging creates a deep dataset
● Applicable to three phase and
isolated use too
40

41. Optimising control system parameters
● Power system models give initial estimate
● Designed for autonomous and directed operation
● Control systems do interact
● Observed response can be used to tune power models
● Use power electronics to reduce wear on switched equipment
41

42. Data-constrained environments
● “Design for data”
 Make key items available
 Open protocols
 Enable automation
● Utility applications
 Automatic circuit reclosers
 Step voltage regulators
 Energy & power quality meters
● End-user applications
 Irrigation
 Load control
● Satellite
 Medium bandwidth
 Very high latency
 Expensive
● Internet of Things
 Low bandwidth
 Medium to high latency
 Power is limited
42

43. For further detail
Design and commissioning detail in:
● Transmission and Distribution World
● June 2017, pages 42-48
● www.tdworld.com/distribution/storage-has-vital-role
43

44. Conclusions
● Power electronics can solve capacity and voltage problems
● Use of communications reduces risk of deploying technology
● Remote data collections gives great insight into behaviour of network
● Consider all the constraints of selected communications network
● Better situational awareness improves decision making
● Early indication of problems gives opportunity to plan for repair
● Incorporate performance/condition monitoring at the design stage
44

45. Questions ?
45