Transcript Document

How Energy Efficiency and Demand
Response can Help Air Quality
Presentation to the California
Electricity and Air Quality Conference
October 3, 2006
Mary Ann Piette
Demand Response Research Center
Lawrence Berkeley National Laboratory
drrc.lbl.gov
Sponsored by the California Energy Commission PIER Program
Presentation Outline
 Summary of Major Energy Efficiency and Demand
Response Goals in California
 Demand Side Management Framework
 Valuing Emissions
 Current Research – Automating Demand Response
 Findings and Future Research Needs
Integrated Demand Side Management Model
Self
Generation
Demand
Response
Price
Time of Use Load
and Bill Management
Reliability
Energy Efficiency
Energy Conservation
Analysis
Business Operations
Source – PG&E
California’s Aggressive Energy Efficiency &
Demand Response Goals
 Electricity - 16,000 GWh and 2 MW by 2010 - enough for 1.8
million homes and double past goals
 Natural Gas - savings doubled - savings in 2010 supply
250,000 to 300,000 homes
 Demand Response - Price-responsive DR goal = 5% of peak
by 2007 (~ 10 GW)
 Combined Savings - reduces CO2 by more than 9 million
tons/yr by 2013, equivalent to 1.8 million vehicles (40% of Bay
Area) off the road
Source – NRDC and Flex Your Power
Energy Efficiency, Load Management, DR
Efficiency and
Conservation
(Daily)
Customer
Motivation
1.
2.
Utility Bill Savings
Civic Duty/
Environmental
Protection
Building
Design
Efficient Shell,
Equipment & Systems
Building
Operations
Integrated
System Operations
Environmental
Factors
Initiation
Change in emissions
based on time of day
and season of kWh
reduction
Local
Demand
Response
(Dynamic
Event Driven)
Peak Load
Management
(Daily)
1.
2.
TOU Bill Savings
Peak Demand
Charge Bill Savings
1.
2.
3.
Dynamic Control
Capability
Low Power Design
1.
2.
Demand Limit
Shift
Economic (price)
Reliability
(emergency)
Civic Duty/Grid
Protection
1.
2.
3.
Demand - Limit
Shift
Shed
Change in emissions
based on time of day,
season, and net
change in kWh
Change in emissions
based on time of day,
season, and net
change in kWh
Local
Remote
Western Region Electricity Generation
Percent of Net Generation (GWh)
or Installed Capacity (MW)
Change or reduction in emission from efficiency and DR depend on electricity
generation mix during the time the electricity is reduced or shifted
40
35
30
25
GWh
20
MW
15
10
5
0
Coal
Gas
Hydro
Nuclear
Oil
Generation by Fuel Type
Source – Holland and Mansur, Is Real Time Pricing Green? Environmental
Impacts of Electricity Demand Variance, August 2004, CSEM
Environmental Adders Adjust Costs
For Emissions
Energy Value
Environment
T&D
Hot
afternoon
PX
Monday
Revenue Neutrality Adjustment
Tuesday Wednesday Thursday
Friday
Source - Energy and Environmental Economics, Inc. (E3),
CPUC Avoided Cost Methodology
Time Dependant Valuation in Building Codes
Considers when Electricity is Used
Emissions
{Voltage Level,
Hour, Year}
NOx $/MWh
{Hour, Year}
NOx Cost $/Ton
{Year}
*
*
1+Energy Losses
{Voltage Level,

TOU Period}
Emission Rate
Ton/MWh
{Hour}

PM10 $/MWh
{Hour, Year}
PM10 Cost $/Ton
{Year}
*
*
1+Energy Losses
{Voltage Level,
TOU Period}
PM10
Emission Rate
Ton/MWh
{Hour}

CO2 $/MWh
{Hour, Year}
CO2 Cost $/Ton
{Year}
*
*
1+Energy Losses
{Voltage Level,
TOU Period}
CO2 Emission
Rate Ton/MWh
{Hour}
Introduction to Automated Demand
Response in Large Buildings
 Provide large (>200kW) customers with electronic,
Internet-based price and reliability signals
 Automatically link price and reliability signals into the
facility control systems
 Customer’s program automated response customized
to facility and client / tenant needs
 Develop facility response strategies that ‘optimize’ load
reduction, economic savings and customer acceptance
Auto-DR System Communications
Price Server
System 2 Automation Server (DRAS)
Demand
Response
3
2
PG&E CPP Event
Utility
Initiation System
1
Polling Client &
Internet Relay
Software
components
Price Server
component
XML
Utility or IOU
Event Trigger
Internet
& private WANs
Internet
Relay
Internet
Gateway
Polling
Client
3
EMCS
Protocol
EMCS
Protocol
4
C
C
C
Electric Loads
3
Polling
Client
C
Client & Logic with
Integrated Relay (CLIR)
3
CLIR
Box
Internet
Relay
C
C
EMCS Protocol
EMCS Protocol
4
C
C
C
Electric Loads
4
4
C
C
C
Electric Loads
C
C
C
Electric Loads
Internet Relay
Test
Sites
C = EMCS Controllers
Automated Demand Response Results
 Significant short-term peak reductions demonstrated for
several dozen sites (avg. 10%)
 Cost to automate DR is minimal
 Minimal impact to occupants and tenants
 Persistent savings demonstrated over 4 summers of field
tests
 Automation reduces labor costs for participation
 Automation increases reliability to utilities and ISO
 Automation standardizes response strategies
 Vast majority of sites shed rather than shift electricity use
Automated DR Results from Previous Year
2004 Hot Weather Test:
5 sites
Aggregated Demand
Saving, Sept 8th
9000
7000
Whole Building Power [kW]
8000
6000
5000
4000
3000
2000
7000
6000
5000
4000
3000
2000
1000
Albertsons
B of A (B)
OFB
Roche
USCB
Total Savings
Baseline
23:00
22:00
21:00
20:00
19:00
18:00
17:00
16:00
15:00
14:00
13:00
12:00
11:00
9:00
10:00
8:00
7:00
6:00
5:00
4:00
3:00
2:00
1:00
ACWD
Echelon
Savings
B of A
Gilead 342
Baseline
2530 Arnold
Gilead 357
CPP BL
23:00
21:00
Chabot
Target
22:00
20:00
18:00
50 Douglas
IKEA
19:00
17:00
15:00
16:00
14:00
12:00
13:00
9:00
11:00
0
10:00
7:00
8:00
5:00
6:00
4:00
2:00
3:00
0:00
1:00
0
1000
0:00
Demand [kW]
2005 Auto-CPP Test:
9/29/2005
10
sites
Findings and Future Research Needs
 California has aggressive goals for energy efficiency and
demand response
 The majority of efficiency and demand response measures
provide direct reductions in emissions by displacing supply
 Continue advanced controls and automation research for key
market segments
 Lack of feedback hampers change in end-use
 Future Research
 Need better data on end-use operating strategies and motivations
to change energy use technologies and patterns
 Need better feedback to customers on energy use data, costs, and
emissions associated with consumption