Transcript Presentation Title - Alberta Electric System Operator

The Demand Response Baseline
Alberta Electric System Operator – Demand Response Working Group
December 9, 2008
Kenneth D. Schisler
Senior Director, Regulatory Affairs and Public Policy
EnerNOC: Overview
EnerNOC is a leading technology-enabled, commercial & industrial-focused demand
response and energy management solutions provider
EnerNOC provides demand-side
capacity, energy, and ancillary services
to an aging North American electricity
grid
Our technology-enabled commercial
and industrial demand response
solutions operate on a national scale in
both regulated and restructured markets
– 1,760 MW under management*
– 3,400 sites under management*
– Nearly 10% of the Fortune 500**
Assets roughly equivalent to 17
100-MW peaking power plants, with
less environmental impact
Active EnerNOC DR markets
In addition to demand response,
EnerNOC offers a suite of energy
management solutions including
efficiency and procurement
EnerNOC Office
*As of 9/30/08
**As of 6/30/08
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Why Baselines Matter
A properly designed baseline is perhaps the most important
determinant of success for any DR program.
To measure DR performance, actual facility load must be
compared with ‘business as usual’ load or what the load would
have been but for the curtailment measures.
‘Business as usual’ is estimated using a baseline methodology.
DR performance measurement is entirely dependent on this
theoretical baseline figure.
Dozens of calculation methodologies have arisen representing a
broad range of qualities.
Ultimately, a well-designed baseline should benefit all
stakeholders, including end-use participants, aggregators,
utilities, grid operators and ratepayers.
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Four Key Baseline Qualities
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Accuracy
Baseline must provide customers with credit for
no more and no less than their actual curtailment.
Integrity
Baseline calculations must not encourage nor be
influenced by manipulation.
Simplicity
Baseline and performance calculations must be
simple enough for all stakeholders to calculate.
Alignment
Baseline design must facilitate performance in line
with goals and the interests of all stakeholders.
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Baseline Qualities – Finding the Right Balance
Consider a regression calculation:
Accuracy - Maximized
– Statistical analysis of extensive data delivers regression
equation and parameters incorporating day-of
conditions.
Integrity - Maximized
– Complexity minimizes opportunity for manipulation or
gaming.
Simplicity - Compromised
– Substantial resources required to meet data and
analysis needs while complexity limits availability of
real-time performance info.
Alignment - Compromised
– Lack of transparency between actual end-user actions
and baseline calculation undermines DR performance.
When considering different approaches, an effective baseline
methodology must balance the four key qualities.
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Calculating ‘Business as Usual’
While baseline calculations include a wide range of potential
components, we believe the following three are most important:
Baseline shape
– Does the baseline reflect the dynamic nature of demand across
granular time intervals, or is it flat?
Baseline window and exclusion rules
– Does baseline data come from a time window that sufficiently
approximates normal operations across ‘like’ days?
Baseline adjustments
– Does the baseline capture the day-of realities inherent in a
customer load profile, or is it based entirely on historic data?
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Baseline Shape – Profile Example
Average Performance:
Profile
Performance
CBL Performance
Meter
Baseline
Committed Capacity
Notification
400 kW
Event Start
Event End
Profile Baseline
– Also called a Customer
Baseline or CBL.
– Mimics the dynamic shape of
customer demand across
granular time intervals.
– Baseline (red line) closely
follows actual demand (blue
line) leading up to the event.
– Performance is calculated as
the difference between baseline
and actual demand measured
at the meter, in this case 65kW.
350 kW
Performance:
65 kW
300 kW
Demand
250 kW
Nomination:
50 kW
200 kW
150 kW
100 kW
50 kW
4-hr Event
window
0 kW
0:00
2:00
4:00
6:00
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
0:00
Time
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Baseline Shape – Static Example
Average Performance:
Static
APMD Performance
Performance
Meter
Baseline
Committed Capacity
Notification
400 kW
350 kW
300 kW
Nomination:
50 kW
Event Start
Event End
Performance:
83 kW
Static Baseline
– Sometimes called Average Peak
Monthly Demand (APMD).
– Average of the peak demand
from each month in a delivery
season (e.g. previous summer).
– Result is a flat baseline
regardless of time interval.
– Baseline (red line) in this case
far greater than the actual preevent load (blue line).
– Performance is calculated as the
difference between baseline and
actual demand measured at the
meter, in this case 83kW.
Incidental
Performance: 32 kW
Demand
250 kW
200 kW
150 kW
100 kW
50 kW
4-hr Event
window
0 kW
0:00
2:00
4:00
6:00
8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
0:00
Time
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Baseline Shape – What We’ve Learned
A static baseline (such as APMD) maximizes simplicity, with
a single flat baseline across time intervals.
While simple, the flat shape of the APMD approach
presents problems:
– Inaccuracy versus the natural variations in demand over
the course of a multi-hour event; resulting in…
– … misalignment of customer actions and rewards.
APMD creates other issues as well:
– Potential for overstated performance due to reliance on
peak demand numbers for all data points.
– Historic nature of data can facilitate manipulation:
• Consider a closed facility receiving credit based on
comparison to prior year’s demand.
Profile baselines represent the true shape of a customer’s demand curve
while appropriately balancing accuracy, integrity, simplicity and alignment.
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Baseline Window and Exclusion Rules
High ‘X’ of ‘Y’ Baseline Methodology
– ‘Y’ represents the Baseline Window, for example a two week period.
– ‘X’ represents the exclusion rule, for example the 5 highest weekday, nonholiday, non-event demand days within the Baseline Window.
There is growing consensus around the balanced qualities of a High 5 of 10
approach for resource adequacy or capacity DR programs.
– A 10 day window is short enough to reflect the most recent operating conditions,
but long enough to prevent undue emphasis on extreme short-term variations,
and long enough to limit manipulation.
– Because DR events occur on the highest demand days, a baseline that
averages all of the window’s recent demand history will consistently understate
performance. Exclusion rules mitigate this understatement.
For Economic or Energy Programs (responsive to price, not peak demand), we
recommend a High 4 of 5 approach.
– Contemporary data best mimics the economic conditions driving the event.
– Because peak demand is not the primary consideration, less exclusion is
required. Furthermore, because such a program uses a shorter Baseline
Window, the exclusion of few days emphasizes integrity.
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Baseline Window and Exclusion Rules – What We’ve
Learned
Exclusion rules can be applied at the individual or the portfolio level
Applying exclusion rules at the portfolio level is problematic.
– All constituents become dependent on portfolio calculations in order to
evaluate performance, undermining simplicity.
– From the customer’s perspective, randomly assigned days drive performance,
blurring the relationship between actions and incentives, undermining
alignment.
Actual data from a March 2008 DR event
employing a High 3 of 10 method:
– 91% of participants high 3 demand
days did not align with portfolio’s high
3 demand days.
Customers with Individual Top Demand Days
Matching Portfolio Top Demand Days
Number
% of Total
3 Days Matching
20
9%
2 Days Matching
103
45%
1 Day Matching
71
31%
0 Days Matching
37
16%
Total
231
* Data from actual EnerNOC event in CA, March 2008
Applying Exclusion Rules at the individual customer level is critical.
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Baseline Adjustments
Customer demand is often heavier on event-days than on ‘like’ days alone.
– For example, the first day of the season that requires air conditioning is likely to
exhibit a different demand profile than prior cooler days.
Capturing day-of realities in a customer baseline is essential to delivering accurate
performance calculations.
A recent LBNL study of baselines compared actual demand with High 3 of 10
baselines with (“BLP3”) and without (“BLP3n”) adjustments, concluding that
“applying a morning adjustment factor significantly reduces the bias and improves
the accuracy of all baseline load profiles”.1 This is consistent with our experience.
Lawrence Berkeley National Laboratory, “Estimating Demand Response Load Impacts: Evaluation of
Baseline Load Models for Non-Residential Buildings in California”, January 2008, page 25
1
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Baseline Adjustments – What We’ve Learned
Adjustment Period
Adjustments can reflect demand conditions
symmetrically (up and down) or
asymmetrically (only adjusted up).
Event Start
Anticipatory Action
Event End
From an alignment perspective, downward
adjustments represent real concerns:
Symmetric Baseline
Event from 2-4pm, anticipatory action reduces
performance from 700kW to less than 100kW
Upward adjustments prevent the perverse outcomes associated with
downward corrections for day-of demand conditions.
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HE23
HE21
HE19
HE17
HE15
HE13
HE11
HE9
HE7
HE5
HE3
– Arguably most important, anticipatory
actions are penalized (see graph).
Actual Demand
HE1
– When day-of adjustment calculation
includes time intervals subsequent to
notification (such as with day-ahead
events), participants can end up
focusing on counter-productive behavior
such as keeping load online.
Asymmetric Baseline
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Recommended Baseline Methodology – A Curtailment
Service Provider’s Perspective
Profile baseline reflecting dynamic nature of customer load curve.
Exclusion rules applied at the individual customer level.
Application of upward adjustment to reflect day-of event conditions.
Accuracy
Integrity
Simplicity
Alignment
The balance between accuracy, integrity, simplicity and alignment in a
baseline methodology is critical to the success of a DR program.
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Questions?
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