Transcript Document

Adoption of Electricity Demand
Response Technologies
Jason Black
([email protected])
MIT, TMP ‘05
Technology, Management and Policy
Graduate Consortium
Jason Black
June 27, 2005
1
Motivation
Price and Demand
100
60,000
90
Demand
Price($/MWh)
70
40,000
60
50
30,000
Price
40
Demand (MW)
50,000
80
20,000
30
20
10,000
10
0
0
1
6
11
16
21
Hour
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June 27, 2005
2
Motivation
• Why No Demand Response?
– Inelastic Demand?
– Lack of Technology?
– Externalities?
– Stakeholders?
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3
Demand Overview
Residential Demand Percentages
36.1
Core
55.3
Deferrable
Energy Based
8.7
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Thermal Storage Example
AC Consumption
4.00
3.50
3.00
Control
2.00
No Control
1.50
1.00
0.50
0.00
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Indoor Temp
78.000
Output (KWh)
No Control
31.48
Control
31.48
Cost ($)
1.93
1.32
77.000
76.000
Temp
Controlled
75.000
No Control
88% reduction in
Peak Demand
74.000
33% reduction in
Costs
73.000
22
23
24
20
21
17
18
19
14
15
16
12
13
9
10
11
6
7
8
4
5
72.000
1
2
3
KW
2.50
Hour
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System Dynamics Model
• Dynamic analysis of large scale adoption of
demand response technology
• Determine feedback effects between supply
and demand
• Provide estimate of direct benefits
• Utilize scenarios to compare potential
policies
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System Dynamics Model Assumptions
• Wholesale prices passed directly to
consumers
• Enabling Technology Installed by Utility
• Adoption via Word of Mouth or Marketing
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System Dynamics Model
Technology
Adoption
Jason Black
Smart
Users
Demand
Price
Supply
Generation
Capacity
Generation
Investment
Normal
Users
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Base Scenario Diminishing Returns
Savings
$6
4.5
3
1.5
0
0
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90
180
270
360
450
540
Time (Month)
630
720
810
900
Savings : Base
$/(Month*customer)
Adopters : Base
Customers
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Base Scenario Supply and Demand
Aggregate Supply Curve
$140
$120
$100
Price ($/MW)
Initial
$80
Final
$60
Initial
Demand
Final
Demand
$40
$20
$0
0
10000
20000
30000
40000
50000
60000
MW
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Base Scenario - Results
•
•
•
•
40% Adoption
18% Reduction in Peak Demand
16% Reduction in Generation Capacity
3% Reduction in Prices
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System Impacts
• Economic
• Environment
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Economic
• Long term price reductions (3%)
• Reduce capacity requirements (16%)
• Reduce Reserve and Ancillary Service
Requirements
• System Savings >> System Costs
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Economic
• Free riders (75% of benefits to system)
• Diminishing returns to initial adopters
• Steeper supply curve
– Potential for higher volatility
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Environment
• Reduction in infrastructure capacity
– Fewer peak plants needed (dirty and
inefficient)
• Increase in generator utilization
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Environment
• May favor coal plants over “peaky” renewables
– Solar peak coincident
• May increase demand long term
• Shifting as a substitute for efficiency investments
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Stakeholder Analysis
• Regulators and System Operators
– Capture
– Status Quo Bias
– Focus on Large Consumers
• Utilities (Transmission and Distribution)
– Long Term Revenue Losses
– Short Term Efficiency Incentives
• Generators
– Revenue Losses
• Consumers
– Risk Averse
– Externalities
– Lack of Information
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Mechanisms for Change
1. Spot pricing for energy and ancillary
services
2. Mandate enabling infrastructure
3. Subsidy for control costs
4. Information campaign to educate
consumers
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Conclusions
• Inelastic Demand does not necessarily mean
unresponsive Demand
– Large potential for Demand Response
• Externalities prevent adoption
– Stakeholder Interests
– Free Riders
– Significant indirect benefits
• Potential emergent dynamics
– Volatility
– Instability
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Thank You
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Backup Slides
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Thermal Storage
• Use temperature to “store” electricity
–
–
–
–
–
Controlled cycling of thermal loads
Pre-heat or pre-cool
Maintain consumer comfort
Minimize costs
Reduce peak loads
• Shift consumption from peak to off peak
periods
• Allows arbitrage
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Base Scenario
Graph for Average Demand
Graph for Smart Users
60,000
6M
45,000
4.5 M
30,000
3M
15,000
1.5 M
0
0
90
180
270
0
0
90
180
270
360
450
540
Time (Month)
630
720
Smart Users : Base
810
900
customers
360
450
540
Time (Month)
630
720
810
Average Demand[base] : Base
Average Demand[inter] : Base
Average Demand[peak] : Base
Average Demand[Cpeak] : Base
900
MW
MW
MW
MW
Graph for Average Price
Graph for Total System Capacity
200
60,000
150
45,000
100
30,000
50
15,000
0
0
0
0
90
180
270
Total System Capacity : Base
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360
450
540
Time (Month)
630
720
810
90
180
270
360
450
540
Time (Month)
630
720
810
900
900
MW
Average Price[base] : Base
Average Price[inter] : Base
Average Price[peak] : Base
Average Price[Cpeak] : Base
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$/(MW*hr)
$/(MW*hr)
$/(MW*hr)
$/(MW*hr)
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Scenarios
Adoption
%
 Demand
(%)
 Capacity
(%)
Adoption
Time
 Price
Index (%)
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Base
Subsidy
Marketing
Rebound
40
55
40
41
18
24
18
16
16
30
15
14
23
8
18
23
2.6
4.1
2.6
3.5
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Stability
• Correlated load shifts can cause
discontinuity
• Unpredictable shifting could increase
uncertainty
• Lengthened peak may increase equipment
failures
• Reduced capacity margins could increase
price volatility
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Scenarios
• Base Adoption
• Policy
– Subsidy
– Marketing Campaign
• Rebound Effect
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Scenarios
Adoption
%
 Demand
(%)
 Capacity
(%)
Adoption
Time
 Price
Index (%)
Jason Black
Base
Subsidy
Marketing
Rebound
40
55
40
41
18
24
18
16
16
30
15
14
23
8
18
23
2.6
4.1
2.6
3.5
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Base Adoption Scenario
• Direct benefits:
– Lower electricity prices (3%)
– 75% of direct benefits captured by system
• Indirect benefits
– Reduced capacity requirements (16%)
– Reduced reserve and ancillary service
requirements
– Reduced congestion
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Subsidy Scenario
• Policy: subsidize control costs
• Adoption increases to 55%
• Peak demand reduced by 24%
• Subsidy return ~ 500%
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Comfort Zones
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Appliance Control Sub-Model
Appliance
Characteristics
Environmental
Factors
e.g. Outside Temp
Comfort
Level
Individual
Demand
Price (Hourly)
Output – Individual
Demand Response
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System Dynamics Model
Generation
Capacity
Appliance
Control
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Tech.
Diffusion
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Market
Clearing
35
Technology Diffusion
Demand
Normal
Users
Smart Users
Adoption rate
Learning Effect
on Control cost
Savings from
Control
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Control
Attractiveness
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Control Cost
36
Technology Diffusion
•
•
•
•
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Consumer Choice Model
Adoption via Word of Mouth or Marketing
Learning Effects Reduce Control Costs
Diminishing Returns
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Market Clearing Model
• Determines Market Clearing Prices
• Supply and Demand as Inputs
• Aggregated by Segment
Segment
Base
Intermediate
Peak
Criticial Peak
Jason Black
MW
0 - 26000
26000-38000
38000-47500
47500-55000
MC ($/MWh)
0
20
50
90
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Capacity(MW) % of Hours
26,000
18%
12,000
67%
9,500
13%
7,500
1%
38
Market Clearing Model
Aggregate Supply Curve
140
120
100
Price ($/MWh)
C Peak
0.3 hrs/day
80
60
Intermediate
16 hrs/day
40
Peak
Peak
3.4 hrs/day
hrs/day
3.4
Base
20
4.6 hrs/day
0
0
10000
20000
30000
40000
50000
60000
Load (MWh)
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Generation Supply Model
+
Investment
Capacity
under
Construction
+
+
Available
Capacity
Completion Rate
+
Plant Retirement
+
Desired Capacity
Competitive
Equilibruim
+
Expected Revenues
per MW
B
+
Investment
Attractiveness
Profitability
Expected
Revenues
+
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Marketing Scenario
Smart Users
6M
4.5 M
3M
1.5 M
0
0
90
180
270
360 450 540
Time (Month)
Smart Users : Base
Smart Users : Marketing
Jason Black
630
720
810
900
customers
customers
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Marketing Scenario
• Policy: marketing campaign to promote DR
technology
• Increases rate of adoption
• Same number of adopters
• Benefits realized faster
• 150% return in 10 years
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Rebound Effect Scenario
• Lowering prices will increase demand in the
long term.
• Direct savings reduced by 29%*
• Generation capacity increases by 2%*
• Demand increases by 2.5%*
• Price index reduced
• Creates oscillations
*Compared to Base Scenario
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Rebound Effect Scenario
Rebound Effect
40,000
30,000
20,000
10,000
0
0
90
180
270
360
450
540
Time (Month)
Average Demand[inter] : Rebound
Average Demand[inter] : Base
Jason Black
630
720
810
900
MW
MW
June 27, 2005
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Legacy System
Demand
System
Conditions
Price (P)
System
Supply
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New Paradigm
Demand
System
Conditions
Price (P)
System
Supply
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Integrating Demand
• Two Parts
– Cost Allocation (Incentives)
– Active Participation (Response)
• Energy markets
• Ancillary services
• Reliability
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Thermal Storage Example
MineCac  i Pi  qi
s.t.
0  q  q max
i
T
min
 Ti  T
max
Where:
Tmin = Tideal - d, Tmax = Tideal + d
Tideal - Thermostat setpoint
d = Acceptable temperature deviation
qi - energy (kWh) consumed in hour i.
Pi - price of electricity ($/kWh) in hour i.
Ti+1 = eTi + (1- e)(To - *qi /A)
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Assumptions
•
•
•
•
•
Jason Black
Inelastic Demand (Short Term)
No Commercial or Industrial Response
No Transmission Constraints
No Fuel Price effects
10% Discount Rate
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Future Research
•
•
•
•
•
•
Jason Black
Integrate capital costs into model
Appliance effects
Stability simulations
Marketing research
Market design testing
Environmental impacts
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Electricity
• Network (commons problems)
• No storage
• Must balance supply and demand
continuously
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Sensitivity Analysis
Parameter
Uncertainty
Impact
Sensitivity of Adoption to Cost
High
High
Marketing Effect
High
Moderate (rate of adoption only)
Contact Rate
High
Moderate (rate of adoption only)
Strength of Learning Effect
Moderate
Moderate
Discount Rate
Moderate
Low – Dynamics, High - NPV
Investment function
Moderate
Low
Demand Elasticity
Moderate
Moderate
Capacity Pipeline Adjustment
Moderate
Moderate (Capacity Oscillations)
Investment time
Moderate
Low
Average Completion Delay
Low
Low
Plant Lifetme
Low
Low
Load shifted
Low
High
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Energy Management System
Meter
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Potential for Residential DR
• Thermal demand (>50% of household demand)
– Ac (>20%)
• >30% of total peak demand (including commercial AC)
– Hot water (~20%)
– Refrigeration (~10%)
• Deferrable demand (> 5%)
– Washer
– Dryer
– Dishwasher
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Contributions
• Dynamic analysis of system effects of large scale demand
response
– Direct
– Indirect
– Environmental
• Identify potential emergent problems with large scale demand
response
• Generalizable model for policy analysis
• Show potential for demand response
• Sampling function for distributed frequency control
(generalizable)
• Valuation of Reliability through Insurance
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Methodology
• Dynamic analysis of large scale DR
– Benefits, drawbacks, and barriers to implementation
• System dynamics
– Simulations
• Technical, policy, and market analysis
– Energy
– Ancillary services
– Reliability
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