Mathematical Sciences Research Challenges for the Next-Generation Electric Grid Summary of a Workshop (2015) / Chapter Skim
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5 Uncertainty Quantification and Validation
5 Uncertainty Quantification and Validation
Pages 43-53
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From page 43... ...
HOW WELL CAN WE MEASURE WHAT DIDN'T HAPPEN AND PREDICT WHAT WON'T? Miriam Goldberg, DNV GL Miriam Goldberg discussed measurement and verification for demand response.
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, copyright 2015 DNV GL. Figure 5-1 Bitmapped FIGURE 5.2 Example of a demand response for an individual resource.
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Examples include estimating the load that would have occurred without the DR event (statistical estimation of an unobservable parameter) and estimating the load that will occur with and without a future DR event (statistical forecasting)
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This information informs additional planning of how the system can be modified or improved. Settlement requires determining individual interval reductions by com paring the observed load with the agreed baseline.
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Baseline accuracy can be assessed only by comparing actual load with baseline load on non-event days, or for accounts that are not dispatched, or by comparing simulated load reductions from actual load with the calculated reduction from the baseline, according to Goldberg. This assumes similar behavior for non-event days or non-dispatched accounts.
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Then, the relative error (the average hourly error divided by the average hourly load) is computed, which is useful for representing accuracy across loads of varying sizes over a population of customers and various time intervals.
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. Goldberg explained that to predict what load capacity could be delivered if needed, a program dispatch operator needs to know the available reduction at each point in time and then track whether load reduction is happening by look ing at an asset's current load level relative to where it should end up.
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Overall, Goldberg said, managing DR uncertainty involves improving measure ment with better retrospective and forecasting models, accommodating response uncertainty in dispatch, and making participating loads more predictable. She observed that the predictability for enrolled DR participants can be im proved by shutting out the noise of customers with highly variable loads, by screen ing loads out of the program based on predictability criteria, and by requiring highly variable loads to give day-ahead predictions (and set penalties for over- or under-prediction)
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Goldberg concluded by summarizing the outstanding problems related to DR uncertainty and uncertainty reduction: • Estimating elasticity (as interrelated response curves) , including enroll ment in variously configured program/product offers and determining response to prices/event dispatch when enrolled as functions of customer characteristics, calendar, hour, weather, and prior responses; • Calculating capacity dynamically for time/weather-varying loads; • Using pattern recognition to improve forecasts and back-casts; • Projecting response trajectories through the duration of an event and after release with error bands; • Relating true aggregate system reduction to the nominal reductions calcu lated for financial settlement, on a dynamic basis, with error bands; and • Establishing baseline bias and variance as functions of customer charac teristics, event day type, and event duration.
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Eydeland explained that the dilemma is that a highly complicated model (includ ing models with stochastic convenience yield, stochastic volatility, regime switching, multiple jump processes, and various term structures) is needed to capture this com plex behavior, but such a model becomes unmanageable and useless.
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Another participant asked what impact the increased penetration of renewables might have on the price formation process. A simple approach suggested would entail a simple shifting of the bid stack by zero-marginal cost renewables.
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