Building Blocks for Premise Level Load Reduction EstimatesERCOT Loads in SCED v2 SubgroupJuly 21, 2014
Overview • Control Methodology • -Building blocks for reduction estimates • -Control by specific device • -Deployment Parameters • Estimation Accuracy • -Device or Measurements at installation • -Customer classes & pre-qualification • Premise Level Deployment Estimation • -Whole Day reductions or Curtailment Period reductions
Control Methodology • Solution Building Blocks • Aggregations of Residential Load • After deployment, load reduction can be increased or decreased • Control of Individual Devices • Tracking of Device Status • Thermostats or Load Control switch on major energy-consuming appliances • Sub-metering accuracy 1% • Stored Interval Data
Premise Level Reduction Possibility • Premise Reduction Methods • Data sources can be used to estimate premise level reductions during curtailment events • Amount reduced at each device • Baseline of ‘normal’ usage for comparison • Data can be used to estimate energy reduction (if any) for day of curtailment • Baseline with device data • Baseline with meter data
Estimating Load Drop • Load drop is simply calculated as Baseline – Actual Consumption • Interval meters provide high degree of accuracy in determining Actual Consumption • Baseline always open to scrutiny • Baseline calculated based on set of assumptions about historic energy usage • Baselines make adjustments • Weather • Daylight • Season • Baselines don’t consider • Customer specific issues • what is happening today at the home • Age of home • Insulation
Baselines are highly scientific • Refer to ERCOT Default Baseline Methodologies for ERS • Very accurate in aggregate • Very accurate for large loads • Lose accuracy when applied to individual residential loads
Data Options for Estimation • Two Options for Estimations • 1. Devices installed to monitor consumption - Measured Consumption • - Sub-meter at 1% accuracy level • 2. Appliance measurements taken at thermostat installation • - Estimated Consumption • - Thermostat monitors system status • - Similar accuracy to device, dependent on measurements at install
Minimizing Peak-Time Rebate Payment ErrorsMarch 2013Dries BerghmanFreeman, Sullivan & Co.
Overview • What is Peak-Time Rebate? • Problems with PTR • Baseline accuracy • Impact accuracy • Payment accuracy • Program design suggestions • Q&A
PTR: Peak-Time Rebate • Program for residential and small commercial (SMB) customers • PTR is a “pay for performance” program: individual consumers are paid an incentive to reduce use during peak on event days • The incentive is based on difference between metered load during event and an estimate of what the customer would have used in absence of the event • That estimate is known as a baseline • PTR is a “carrot-only” program: customers are paid when they reduce load, but are not fined when they increase load. This means customers are free to participate if they want to.
Sounds great in theory, but there are problems in practice Minimizing payment error tends to be a utility’s main concern, but it is directly influenced by baseline and impact error. Baselines are rarely accurate for individual customers on individual days. Baseline inaccuracies can wipe out customer impacts – or make them appear larger than they really are. The smaller the customer impact, the more difficult it is to detect. The only reason we estimate baselines is to calculate impacts, and impact error is always larger than baseline error. Think of this as “signal” (impact) vs. “noise” (baseline). Customers can do nothing and still get paid. Because customers are paid for decreases, but not fined for increases, payment errors tend to disadvantage the utility (but not always). Payments made in error can account for a very large portion of the total amount paid to customers.
Problem 1: Is the baseline accurate? Each dot represents one customer’s baseline for a given day on which the true load is known. Baselines (and payments) are calculated for individual customers on individual days It is very difficult to estimate individual customer load on any given day, even if the average baseline is right on average (as it is in the graph).
There are many different types of baselines • Most utilities use day-matching baselines • E.g. “3/5”: out of the previous 5 non-event weekdays, average load for the three days with the highest load • Some also use weather-matching baselines • E.g. “maximum temperature”: if the event day has a high temperature of 92°F, average the load for all days in the past year with highs between 90°F and 95°F • Can also mix day-matching and weather-matching • Average the load for the five days in the previous three months that were closest in temperature to the event day • Regressions • Often the most accurate, but difficult to implement • What works for one utility or program may not work for another; it is important to determine what works best
Problem 2: Impact errors are larger than baseline errors Baseline is simulated for a day on which we know the actual load – lets us make a comparison between actual load/baseline and true/estimated impact Graph shows 25% load impact for the average customer To calculate true impact, subtract red line from green line; to calculate estimated impact, subtract blue line from green line
Questions? Contact Information Dries Berghman Sr. Analyst Freeman, Sullivan & Company email@example.com 415-777-0707 415-948-2308 (direct)
Thoughts on legal and regulatory hurdles • Concerns on LSEs billing customers for curtailed energy • Policy issue at PUCT on billing for energy that one did not consume • If ERCOT/PUCT decides on LMP-G It must be designed at an aggregated level as a bilateral transaction between a DR CSP and the REP without direct billing of energy not consumed to retail end user
An Alternative to LMP - VG • Payment of full LMP is simplest solution • LMP – Proxy G • Determine Proxy G annually • ERCOT pays CSP LMP • CSP calculates load drop in aggregate for each REP • CSP pays Proxy G to REP • In one month, payment to small REP may be inaccurate • Over time, payments to all REPs will be accurate
Questions / Discussion • Frank Lacey • Vice President, Regulatory and Market Strategy • Comverge, Inc. • firstname.lastname@example.org • Todd Horsman • VP Regulatory and Delivery • Consert / Landis+Gyr • email@example.com