smart meters, demand response and energy efficiency
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Smart Meters, Demand Response and Energy Efficiency
GRIDSCHOOL 2010MARCH 8-12, 2010 RICHMOND, VIRGINIA
INSTITUTE OF PUBLIC UTILITIESARGONNE NATIONAL LABORATORY
Rick HornbySynapse Energy Economics
[email protected] 617 661 3248Do not cite or distribute without permission
MICHIGAN STATE UNIVERSITY
Hornby - 02GridSchool 2010
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 projectionsi. What is the difference between DR and EE?ii. What are the relative values of DR and EE? iii. How do the differences between Mass Market Customers and Medium to Large C&I
Customers affect the ability to achieve DR and EE?iv. Why are projections of DR from mass market customers via dynamic pricing (DP)
enabled by smart meters uncertain?v. Why are projections of EE from mass market customers via feedback enabled by smart
meters uncertain?
Hornby - 03GridSchool 2010
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
0.0
500.0
1,000.0
1,500.0
2,000.0
2,500.0
1 412 823 1234 1645 2056 2467 2878 3289 3700 4111 4522 4933 5344 5755 6166 6577 6988 7399 7810 8221 8632
MW Series1
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I. DR Versus EE Illustrative Load Duration Curve (8,760 hours)
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
1 877 1753 2629 3505 4381 5257 6133 7009 7885
Hours
Lo
ad (
MW
)
peak demand is rate of use in hour with highest use, in MW or kW
Load Duration Curve plots actual electricity use from hour with highest use to hour with lowest use
energy is area under the curve, in MWh or kWh
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I. DR Versus EEThe Quantity and Cost of Physical Resources are Driven by Load Duration
Curve
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
1 877 1753 2629 3505 4381 5257 6133 7009 7885
Hours
Lo
ad (
MW
)
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.
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.
Hornby - 07GridSchool 2010
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)
Hornby - 08GridSchool 2010
II. Relative Values of DR and EE – Quantity10% Reduction During 60 Hours of highest use (Critical peak)
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
1 877 1753 2629 3505 4381 5257 6133 7009 7885
Hours
Lo
ad (
MW
)
10% Reduction During Top 60 Critical Peak Hours
6,000
6,500
7,000
7,500
8,000
8,500
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Hours
Lo
ad (
MW
)
Reference Case 10% Peak reduction Case
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
Hornby - 09GridSchool 2010
II. Relative Values of DR and EE – Quantity 2% Reduction in 8,760 Hours
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
1 877 1753 2629 3505 4381 5257 6133 7009 7885
Hours
Lo
ad (
MW
)
Reference Case
2% Annual Reduction Case
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.
Hornby - 010GridSchool 2010
II. Relative Values of DR and EE – Quantity2% reduction in 8,760 hours saves far more energy, and associated emissions,
than 10% reduction in 60 hours of highest use
-
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
10% Peak reduction Case
2% Annual Reduction Case
Hornby - 011GridSchool 2010
II. Relative Values of DR and EE – Price Mitigation
$3.00
$4.00
$5.00
$6.00
$7.00
$8.00
$9.00
20,000 22,000 24,000 26,000 28,000 30,000 32,000
MW bid
FC
M i
n $
/kW
-mo
nth
Cumulative SupplyBids
Installed CapacityRequirement
Cumulative SupplyBids +525 MW ofDSM
New Lower Forecast Market Price
Existing MW - Price TakersNew Peakers
Existing MW bidders + 525 MW DSM Bidders
Reducing demand via “DSM bids” reduces capacity prices (demand can be met at a lower point on the supply curve)
Hornby - 012GridSchool 2010
II. Relative Values of DR and EE
$-
$20.00
$40.00
$60.00
$80.00
$100.00
$120.00
$140.00
$160.00
Utility A Utility B
SupplyDistribution
Illustrative Residential Monthly Bills for 1,000 kwh
Hornby - 013GridSchool 2010
II. Relative Values of DR and EE re Monthly Bills
$-
$20.00
$40.00
$60.00
$80.00
$100.00
$120.00
$140.00
$160.00
Utility A Utility B
EnergyDemand - SupplyDemand - DistributionCustomer
Illustrative Cost Drivers / Causation - Residential Monthly Bills for 1,000 kwh
EE
DR
EE
DR
Hornby - 014GridSchool 2010
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
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Customers Peak Demand Annual Energy
Medium and Large C & I
Residential & small C & I
Mass Market Customers
Hornby - 015GridSchool 2010
III. Mass Market Customers Have Different Characteristics from Medium to Large C&I Customers
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
Residential & small C & I Medium and Large C & I
kWh
/ m
on
th
In this utility Mass Market customers have a much lower average use per month than medium and large C&I Customers
Hornby - 016GridSchool 2010
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).
Hornby - 017GridSchool 2010
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)
Value of avoided capacity
CPR or CPP @ 60
hours
Value of reducing 1 kW for 5 hours
$ per kW-year $/kWh $new Gas - fired Combustion Turbine (CT) - CONE 100 1.67$ 8.33$ new Gas CT less its energy revenues (net CONE) 60 1.00$ 5.00$ existing peaking capacity 30 0.50$ 2.50$
CONE is "Cost of New Entry"
Marginal (Avoided) Generating Capacity for 15 years
Hornby - 018GridSchool 2010
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.• 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.
Hornby - 019GridSchool 2010
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)
0-10% 11-20% 21- 30% 31- 40% 41- 50% 51- 60% 61- 70% 71- 80% 81- 90% 91- 100%
Per cent of customers
kw/c
ust
om
er
largest 10% of customers have demand 260% of rate class average
next largest 10% of customers have demand 160% of rate class average
Rate Class Average
50% of customers have demand much less than average
Hornby - 020GridSchool 2010
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.
Hornby - 021GridSchool 2010
Contact
Synapse Energy Economics617 661 3248
www.synapse-energy.com
Rick Hornby (ext 243)[email protected]