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Smart Meters, Demand Response and Energy
Efficiency
GRIDSCHOOL 2010
MARCH 8-12, 2010  RICHMOND, VIRGINIA
INSTITUTE OF PUBLIC UTILITIES
ARGONNE NATIONAL LABORATORY
Rick Hornby
Synapse Energy Economics
[email protected]  617 661 3248
Do not cite or distribute without permission
MICHIGAN STATE UNIVERSITY
Introduction
•
•
•
Investments in smart meter infrastructure (SMI) are typically justified based upon
projected savings in distribution service costs, electricity supply costs and sometimes
include externalities such as reductions in emissions of greenhouse gases (GHG). The
justifications often mention, but rarely quantify, other categories of benefits such as
improvements in distribution service reliability.
Projected savings in electricity supply costs are based on projected reductions in
electric demand (demand response or DR) and electric energy (energy efficiency or
EE) that will be enabled by smart meters and the unit $ value of those reductions.
This session will address the key issues associated with those projections
i.
ii.
iii.
iv.
v.
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What is the difference between DR and EE?
What are the relative values of DR and EE?
How do the differences between Mass Market Customers and Medium to Large C&I
Customers affect the ability to achieve DR and EE?
Why are projections of DR from mass market customers via dynamic pricing (DP)
enabled by smart meters uncertain?
Why are projections of EE from mass market customers via feedback enabled by
smart meters uncertain?
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Introduction - Smart Meter Infrastructure
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I.
DR Versus EE
- electricity use varies by time period throughout the year
Hourly Demand ME 2006 Chronological
2,500.0
2,000.0
MW
1,500.0
Series1
1,000.0
500.0
0.0
1
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412 823 1234 1645 2056 2467 2878 3289 3700 4111 4522 4933 5344 5755 6166 6577 6988 7399 7810 8221 8632
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I.
DR Versus EE
Illustrative Load Duration Curve (8,760 hours)
10,000
9,000
peak demand is rate of use in
hour with highest use, in MW or
kW
8,000
7,000
Load Duration Curve plots actual
electricity use from hour with
highest use to hour with lowest
use
Load (MW)
6,000
5,000
4,000
3,000
energy is area under the curve,
in MWh or kWh
2,000
1,000
0
1
877
1753
2629
3505
4381
5257
6133
7009
7885
Hours
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I.
DR Versus EE
The Quantity and Cost of Physical Resources are Driven by Load Duration
Curve
10,000
Capacity is a function of projected peak demand. To
ensure reliable service the total MW of capacity must
equal peak demand plus a reserve margin. Capacity
must be in place or reserved in advance of actual
demand. Therefore capacity costs do not typically vary
with actual demand, and thus are considered fixed.
9,000
8,000
7,000
Load (MW)
6,000
5,000
4,000
Generation is a function of actual electric
energy use. The actual quantity generated
matches the actual quantity used.Therefore
generation costs typically vary with actual use,
and thus are considered variable.
3,000
2,000
1,000
0
1
877
1753
2629
3505
4381
5257
6133
7009
7885
Hours
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II. Relative Values of DR and EE
Reductions in electricity use, both demand and energy,
translate into direct quantity savings and indirect price
mitigation savings. (Customers who reduce receive
direct quantity savings, all customers receive indirect price
mitigation savings.)
Direct quantity savings equal the quantity of reduction
demand and energy multiplied by the corresponding
prices:
Quantity Saving ($) = (demand reduction in Kw* $/kW)
+(energy reduction in kWh * $/kWH)
Indirect Price Mitigation savings equal the total quantity
of demand and energy being used multiplied by the
reduction in price due to the reduction in quantity, e.g.
Price mitigation saving ($) = (Total demand * reduction in
capacity price $/kW) +(total energy * reduction in energy
price $/kWh)
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II. Relative Values of DR and EE – Quantity
10% Reduction During 60 Hours of highest use (Critical peak)
A 10% reduction in use in the 60 hours with highest use
could reduce capacity obligation and costs by 10% if
sustained. It would reduce electricity generation in those
60 hours and the associated costs and emissions
10,000
9,000
8,000
10% Reduction During Top 60 Critical Peak Hours
7,000
Load (MW)
8,500
Load (MW)
6,000
5,000
8,000
7,500
7,000
6,500
6,000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
4,000
Hours
Reference Case
10% Peak reduction Case
3,000
2,000
1,000
0
1
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877
1753
2629
3505
4381
Hours
5257
6133
7009
7885
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II. Relative Values of DR and EE – Quantity
2% Reduction in 8,760 Hours
10,000
A 2% reduction in use in every hour could
reduce capacity obligation and cost by 2%, if
sustained. It would reduce electricity
generation by 2% in all hours and associated
energy costs and air emissions in 8,760 hours.
9,000
8,000
7,000
Load (MW)
6,000
Reference Case
2% Annual Reduction Case
5,000
4,000
3,000
2,000
1,000
0
1
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877
1753
2629
3505
4381
Hours
5257
6133
7009
7885
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II. Relative Values of DR and EE – Quantity
2% reduction in 8,760 hours saves far more energy, and associated emissions,
than 10% reduction in 60 hours of highest use
800,000
700,000
600,000
500,000
400,000
10% Peak reduction Case
2% Annual Reduction Case
300,000
200,000
100,000
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II. Relative Values of DR and EE – Price Mitigation
Reducing demand via “DSM bids” reduces capacity prices
(demand can beIllustrative
met at a lower
the525
supply
curve)
FCMpoint
priceon
with
MW of
DSM bids
$9.00
New Lower Forecast
Market Price
FCM in $/kW-month
$8.00
$7.00
Cumulative Supply
Bids
$6.00
Installed Capacity
Requirement
$5.00
$4.00
Existing MW - Price Takers
Existing MW bidders
+ 525 MW DSM
Bidders
Cumulative Supply
Bids +525 MW of
DSM
New
Peakers
$3.00
20,000
22,000
24,000
26,000
28,000
30,000
32,000
MW bid
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II. Relative Values of DR and EE
Illustrative Residential Monthly Bills for 1,000 kwh
$160.00
$140.00
$120.00
$100.00
Supply
Distribution
$80.00
$60.00
$40.00
$20.00
$-
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Utility A
Utility B
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II. Relative Values of DR and EE re Monthly Bills
Illustrative Cost Drivers / Causation - Residential Monthly Bills for 1,000 kwh
$160.00
$140.00
$120.00
Energy
Demand - Supply
Demand - Distribution
Customer
E
E
$100.00
$80.00
E
E
$60.00
D
R
$40.00
D
R
$20.00
$-
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Utility A
Utility B
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III. Mass Market Customers Have Different Characteristics
from Medium to Large C&I Customers
In this Utility Mass Market Customers Account For 98 Per Cent of Customers but only 68
Percent of Demand and Energy
100%
90%
80%
70%
60%
Mass
Market
Customers
50%
Medium and Large C & I
Residential & small C & I
40%
30%
20%
10%
0%
Customers
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Peak Demand
Annual Energy
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III. Mass Market Customers Have Different Characteristics
from Medium to Large C&I Customers
In this utility Mass Market customers have a much lower average
use per month than medium and large C&I Customers
16,000
14,000
12,000
kWh / month
10,000
8,000
6,000
4,000
2,000
0
Residential & small C & I
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Medium and Large C & I
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IV. Why Projections of DR from mass market customers via DP
Enabled By Smart Meters are Uncertain
• DR for Mass Market customers is not new. Many utilities have many years
experience offering direct load control (DLC) programs to those customers. Under
these programs the customer allows the utility to cycle the operation of certain major
loads during critical peak periods, e.g. 5 hours, on a limited number of afternoons
each summer, e.g. 12. The loads are typically central air conditioning, water heating
and pool pumps. In exchange the customer receives a one-time incentive, e.g. $50,
and a programmable controllable thermostat (PCT).
• DR via DP enabled by the equivalent of Smart Meters is not new. Some utilities and
curtailment service providers have been offering this to large C&I customers for
several years.
• What is new is DR from Mass Market customers via DP enabled by Smart
Meters. Under these rate offerings customers who elect to reduce their use during
these critical peak periods relative to their normal levels will either receive a rebate or
avoid paying a premium rate. (DP designed as a rebate is called Critical peak rebate,
DP designed as a premium rate is called Critical Peak Pricing).
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IV. Why Projections of DR from mass market customers via DP
Enabled By Smart Meters are Uncertain
1.
Uncertainty re the long-term value of avoided capacity due to uncertainty re
marginal source of capacity. Electricity use may grow more slowly in the
future due to loss of manufacturing and improvements in efficiency. New
transmission projects may allow regions with excess existing capacity to
serve regions that need new capacity. New renewable capacity will be
added to comply with renewable portfolio standards, regardless of need for
capacity. The lower the avoided costs of capacity the lower the value to
prospective participants. (applies to all DR)
Marginal (Avoided) Generating Capacity for
15 years
new Gas - fired Combustion Turbine (CT) - CONE
new Gas CT less its energy revenues (net CONE)
existing peaking capacity
Value of
avoided
capacity
Value of
CPR or
reducing
CPP @ 60
1 kW for
hours
5 hours
$ per kW-year
$/kWh
100
$
1.67
60
$
1.00
30
$
0.50
$
$
$
$
8.33
5.00
2.50
CONE is "Cost of New Entry"
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IV. Why Projections of DR from mass market customers via DP
Enabled By Smart Meters are Uncertain
2.
Uncertainty re the percentage of mass market customers who will elect to reduce use during critical
peak periods on a sustained basis, year after year, and the magnitude of those reductions.
•
•
•
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The mass market customers with the best value proposition are those whose demand is high in summer
months. That demand is primarily for central air conditioning and pool pumps.
In many regions, only about 50 % of mass market customers have that high demand. Of those, 20% to 30%
may be already on DLC.
Thus, only about 35% of total mass market customers may have a very attractive value proposition.
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IV. Why Projections of DR from mass market customers via DP
Enabled By Smart Meters are Uncertain
Illustrative distribution of kw/customer in residential rate class (NJ utility)
largest 10% of customers
have demand 260% of rate
class average
kw/customer
next largest 10% of customers
have demand 160% of rate
class average
50% of customers have demand much less than average
Rate Class Average
0-10%
11-20%
21- 30%
31- 40%
41- 50%
51- 60%
61- 70%
71- 80%
81- 90%
91- 100%
Per cent of customers
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V. Why Projections of EE from mass market customers via
Feedback Enabled By Smart Meters are Uncertain
• EE from Mass Market customers via feedback is relatively new.
• 2009 report by the Electric Power Research Institute (EPRI) concludes that
“residential electricity use feedback” can be an effective tool but “Further research is
necessary on such points as “participation levels, the persistence of feedback effects,
the relative value of different types of feedback, dynamic pricing interactions, and
distinguishing the effects of feedback among different demographic groups.”
Residential Electricity Use Feedback: A Research Synthesis and Economic
Framework. EPRI, Palo Alto, CA: 2009. 1016844 (Feedback Research Synthesis).
Available at http://www.opower.com)
• Feedback can be, and is being, provided using monthly usage data from existing
meters as well as hourly usage data from new smart meters. It is not yet clear
whether feedback based on hourly usage data from new smart meters leads to
materially greater EE than feedback from monthly usage data.
• ACEEE expected to release an evaluation of this approach in 1st Quarter 2010.
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Contact
Synapse Energy Economics
617 661 3248
www.synapse-energy.com
Rick Hornby (ext 243)
[email protected]
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