UNIVERSITY OF CALIFORNIA SANTABARBARA ENERGY …UNIVERSITY OF CALIFORNIA SANTABARBARA ENERGY...

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

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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

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UC Santa Barbara Rankings

• #37 among National Universities

• #8 in Top Public Schools

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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

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-

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

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-

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

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-

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

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$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

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SCE 220/66KV System

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Electric Utility Infrastructure

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Electric Utility Infrastructure

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Onsite Energy Generation

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Onsite Energy Generation

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Onsite Energy Generation

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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

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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

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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

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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

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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)

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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

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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

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SCE 220/66KV System

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60

40

20

0

-20

-40

-60

-80

MW

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$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

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Building System Analytics

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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

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• 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

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https://tap.ecorithm.com/

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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

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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

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Tuesday, June 27, 2017

Space / Equipment

Adjacency Information

Floorplans

Equipment Schedules

Riser Diagrams

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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

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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%

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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

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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

QUESTIONS

jordan.sager@ucsb.edu

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