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UNIVERSITY OF CALIFORNIA SANTA BARBARA
ENERGY MANAGEMENT IN OPERATIONS
March 1, 2018
Jordan Sager
Energy Manager, UCSB Physical Facilities
3/5/2018 1
Overview
• Campus Energy Use Profile
• Regional Power Grid
• Electric Utility Infrastructure and Onsite Energy Resources
• Building HVAC Analytics Deployment
3/5/2018 2
3/5/2018 3
3/5/2018 4
3/5/2018 5
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Thou
sa
nd
s o
f S
qua
re F
eet
UCSB Growth in Building Square Footage Since 1990
3/5/2018 6
3/5/2018 7
3/5/2018 8
UC Santa Barbara Rankings
• #37 among National Universities
• #8 in Top Public Schools
3/5/2018 9
3/5/2018 10
-
100
200
300
400
500
600
700
800
Energ
y C
onsum
ed (
tbtu
)
UCSB Annual Grid Energy Use by Fuel Since 1990
Electricity
Natural Gas
3/5/2018 11
-
20
40
60
80
100
120
140
Energ
y U
se
In
ten
sity (
kB
tu/s
f-yr)
UCSB Grid Energy Use Intensity Since 1990
3/5/2018 12
-
5
10
15
20
25
30
35
40
45
Energ
y C
onsum
ed (
tbtu
)
UCSB Monthly Grid Energy Use by Fuel Source (2017)
Electricity
3/5/2018 13
-
5
10
15
20
25
30
35
40
45
Energ
y C
onsum
ed (
tbtu
)
UCSB Monthly Grid Energy Use by Fuel Source (2017)
Electricity
Natural Gas
3/5/2018 14
$7,266,059.82
$526,437.53
$1,293,514.58
$1,887,218.01
$249,568.67
$263,500.95
UTILITY EXPENSES BY COMMODITY - 2017
ELECTRIC - GRID
ELECTRIC - ONSITESOLAR
NATURAL GAS
POTABLE WATER
RECLAIMED WATER
SEWER
SCE Electrical Service
• http://www.arcgis.com/home/webmap/viewer.html?webma
p=e62dfa24128b4329bfc8b27c4526f6b7
3/5/2018 15
SCE 220/66KV System
3/5/2018 16
Electric Utility Infrastructure
3/5/2018 17
Electric Utility Infrastructure
3/5/2018 18
3/5/2018 19
Onsite Energy Generation
3/5/2018 20
3/5/2018 21
Onsite Energy Generation
3/5/2018 22
3/5/2018 23
3/5/2018 24
3/5/2018 25
3/5/2018 26
3/5/2018 27
3/5/2018 28
3/5/2018 29
Onsite Energy Generation
3/5/2018 30
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
EL
EC
TR
ICA
L D
EM
AN
D (
KW
)
2016
TH
UR
S
FR
I
SA
T
SU
N
MO
N
TU
ES
WE
DS
UCSB Grid Electrical Load: Final Week in September
313/5/2018
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
EL
EC
TR
ICA
L D
EM
AN
D (
KW
)
2016
2017
UCSB Grid Electrical Load: Final Week in September
SA
T
SU
N
MO
N
TU
ES
WE
DS
TH
UR
S
FR
I
3/5/2018 32
Period Summer Winter6 - 9 Weekday Off Peak Off Peak
9-12 Weekday Mid Peak Mid Peak
12-14 Weekday On Peak Mid Peak
14-16 Weekday On Peak Mid Peak
16-18 Weekday On Peak Mid Peak
18-20 Weekday Mid Peak Mid Peak
20-22 Weekday Mid Peak Mid Peak
22-6 Weekday Off Peak Off Peak
Weekend Off Peak Off Peak
• Current TOU Periods
Electric Utility Time of Use Rates
3/5/2018 33
Period Summer Winter6 - 9 Weekday Off Peak Off Peak
9-12 Weekday Mid Peak Mid Peak
12-14 Weekday On Peak Mid Peak
14-16 Weekday On Peak Mid Peak
16-18 Weekday On Peak Mid Peak
18-20 Weekday Mid Peak Mid Peak
20-22 Weekday Mid Peak Mid Peak
22-6 Weekday Off Peak Off Peak
Weekend Off Peak Off Peak
• Current TOU Periods • Proposed TOU Periods
Period Summer Winter6 - 9 Weekday Mid Peak Mid Peak
9-12 Weekday Mid Peak Off Peak
12-14 Weekday Mid Peak Off Peak
14-16 Weekday Mid Peak Off Peak
16-18 Weekday On Peak Mid Peak
18-20 Weekday On Peak Mid Peak
20-22 Weekday On Peak Mid Peak
22-6 Weekday Off Peak Mid Peak
Weekend Mid Peak Mid Peak
Electric Utility Time of Use Rates
3/5/2018 34
3/5/2018 35
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
EL
EC
TR
ICA
L D
EM
AN
D (
KW
)
HOUR OF DAY
24-Hour Demand Profile
2016 20170
0:0
0
00:0
0
12:0
0
16:0
0
20:0
0
04:0
0
08:0
0
-
5
10
15
20
25
30
-
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,0001
MW
, 2 M
Wh
1 M
W, 4
MW
h
1 M
W, 6
MW
h
1 M
W, 8
MW
h
2 M
W, 4
MW
h
3 M
W, 6
MW
h
2 M
W, 1
2 M
Wh
4 M
W, 8
MW
h
5 M
W, 1
0 M
Wh
6 M
W, 1
2 M
Wh
4 M
W, 1
2 M
Wh
3 M
W, 1
8 M
Wh
4 M
W, 1
6 M
Wh
8 M
W, 1
6 M
Wh
4 M
W, 2
0 M
Wh
4 M
W, 3
2 M
Wh
4 M
W, 2
4 M
Wh
10 M
W, 2
0 M
Wh
5 M
W, 3
0 M
Wh
6 M
W, 3
6 M
Wh
10 M
W, 4
0 M
Wh
20
MW
, 40
MW
h
8 M
W, 4
8 M
Wh
10 M
W, 6
0 M
Wh
20
MW
, 60
MW
h
Sim
ple
Pa
yb
ack
Pe
rio
d (
ye
ars
)
2019
An
nu
al B
ill S
avin
gs ($
)
Battery Configuration
Bill Savings and Developer Payback Period Trends
Bill Savings Simple Payback (Secondary Axis)
3/5/2018 36
0
5
10
15
20
25
30
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
$400,000
$450,000
$500,000
2 3 4 5 6 8
Year
s
$/y
ear
Battery Duration (Hours)
2019 Bill Savings (4 MW Battery)
Annual Bill Savings ($) Inremental Bill Savings ($) Simple Payback Period (yrs) - Secondary Axis
3/5/2018 37
CEC Grant Funding Opportunity 17-302
Demonstrate Business Case for Advanced Microgrids in
Support of California’s Energy and GHG Policies
• Collaboration between IEE, ECE, Facilities Management
• Energy storage, EV charging, utility demand response,
integration/optimization platform
• Solar-driven backup power to critical campus facilities
• Significant economic value during normal operations
• Research opportunities in DER integration
3/5/2018 38
3/5/2018 39
SCE 220/66KV System
3/5/2018 40
3/5/2018 41
60
40
20
0
-20
-40
-60
-80
MW
3/5/2018 42
$442 $452 $473
$431
$379
$424
$605
$-
$100
$200
$300
$400
$500
$600
$700
2019
$/kW
-yr
Implied Capacity Payment Needed ($/kW-yr)
2 MW, 4 MWh 4 MW, 8 MWh 3 MW, 6 MWh 4 MW, 12 MWh 1 MW, 4 MWh 4 MW, 16 MWh 1 MW, 6 MWh
3/5/2018 43
Building System Analytics
3/5/2018 44
Deployment of data management and analysis software on
building mechanical system data trended natively at the
Building Automation System
Also referred to as Fault Detection and Diagnostics (FDD)
Building System Analytics
3/5/2018 45
• Problem:
• Issues on previous projects commissioning / building management
projects:
• Data silos between stakeholders
• Incompatible workflows creating tasks to be replicated by multiple
members or not completed at all
• Too much data!
• Goal:
• Establish data architecture eliminating information silos
• Manageable/scalable workflow for prioritization of corrective
actions
3/5/2018 47
3/5/2018 48
Data Architecture
• The following analysis and reporting layers identified from
analyzing organizational workflows:
• BMS data collection
• HVAC equipment information
• Floorplan information
• FDD analysis
• Service Requests
• KPI Reporting / FDD Results
49
Gathering BMS Trend Data
3/5/2018
VPN Firewall
• 10,000 points trended on a 5-minute interval for deployed
buildings
Data Onboarding: Contextualizing Building Data
50
Equipment and point
hierarchies setup
correlating
systems/devices that are
physically interconnected
FPB_L12_1211
FPB_L12_1212AHU_01
DamperCommand
Occupied
AirFlow
Chiller_01.ChilledWater SupplyTemp
HP_L15_1501
Etc.
Mapping
Relationship Building
BMS data nomenclature differs building to building
Points ‘mapped’ to a clean, uniform
name for intake and use in analysis
Native Name from BMS DeviceType DeviceName PointName
Point1 City Center/TU_VAV/L12/_1211/DMPR COMD FPB FPB_L12_1211 DamperCommand
Point2 City Center/HP/L15/_1501/DAT HP HP_L15_1501 DischargeAirTemp
• Data onboarding allows buildings with dissimilar systems to be
uniformly represented within the analytics system
Tuesday, June 27, 2017
Spatial Referencing
51
Tuesday, June 27, 2017
Space / Equipment
Adjacency Information
Floorplans
Equipment Schedules
Riser Diagrams
52
Building Output (ex. Temperature)
Advanced Analytics
Tuesday, June 27, 2017
Automated Fault Detection and Diagnostics (AFDD)
Learning algorithms used to identify and diagnose faults in buildings or in any dynamical
system with the goal of bringing the system back to its intended operation
53
Education and Social Science Building
3/5/2018
Monthly TCI
Nov Dec Jan Feb Mar Apr May
42.7% 64.6% 77.6% 83.9% 80.2% 88.2% 87.8%
3/5/2018 54
Data Architecture
Native Name from BMS DeviceType DeviceName PointName
Point1City Center/TU_VAV/L12/_1211/DMPR COMD
FPB FPB_L12_1211 DamperCommand
Point2 City Center/HP/L15/_1501/DAT HP HP_L15_1501 DischargeAirTemp
Architectural
Information
Trending / Indexing
Web Reporting
KPI Tracking
Service Requests
Fault Detection
Service Requests
553/5/2018
Specific issues are matched to work
order descriptions
These reports:
• Identify the time and place of
the fault observation
• Contain supporting BMS data
assisting in issue
troubleshooting
• Have keys associated so
events are identifiable with
future issues
In practice technicians
troubleshoot issues effectively
compared to occupant
descriptions
3/5/2018 56
Deployment
Building Sensors Mapped
Points
Equipment
225-ESB 690 352 120
266-CNSI 2093 1024 205
275-GGSE 1765 1695 144
276-SSMS 2324 2253 196
• Remainder of campus contains about ~49000
sensors across 58 buildings