SYSTEM OPERATIONS FORECASTING Dr. Frank A. Monforte Itron s Forecasting Brown Bag Seminar September 13, 2011
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2011 BROWN BAG SEMINARS» Modeling Error Variances -- Understanding CH, ARCH and GARCH models - March 29, 2011» What is a Good Model? - June 21, 2011» System Operations Forecasting - September 13, 2011» Variables For Each Time Horizon - December 13, 2011» All at noon, Pacific Time» All are recorded d and available for review after the session.
INDUSTRY TRENDS Increase Accuracy Increase Detail
INDUSTRY TRENDS» Day-Ahead and Real-time markets require a greater level of geographic detail Market prices are determined by models of the transmission/generation network The network models require a high level of geographic g detail to account for capacity bottle necks This in turn has driven the need for forecasting at a greater level of geographic g detail
INDUSTRY TRENDS» Forecast Errors are Magnified The market place understands ds the linkage between ee the load forecast and market prices This puts the load forecasts and load forecast frameworks under the microscope As a result, the industry standard for forecast accuracy has risen significantly The rub is greater geographic detail means noisier load data which weakens forecast accuracy
INDUSTRY TRENDS» Push to be Green Wind d&soa Solar Generation eato Demand Response Electric Vehicles, Smart Grid» All of which means More metering points More forecasting techniques, e.g. Physical vs. Persistence More weather concepts, more stations
INDUSTRY TRENDS» Greater geographic detail reveals embedded generation Drives the need for forecasting by Point of Delivery e Industrial production patterns emerge Net transmission flows into/out of a zone are visible How do you forecast something that is not measurable?
INDUSTRY TRENDS Increase Accuracy Increase Detail
SYSTEM OPERATIONS FORECASTING» The net effect of these trends are Forecasting more than loads - Wind Generation forecasting - Solar Generation forecasting - Demand Response forecasting - Embedded Generation forecasting Move toward automating the forecast process - More meters means more work - Forecasts need updating in real-time - Integration with multiple forecast platforms o Load, Wind, Solar, Demand Response
WHAT DOES MORE ACCURACY MEAN» More Accuracy Means: For Near-term forecasting the stream of 5, 15, 30, 60 minute ahead forecasts need to be more accurate - Requires accurately bridging the gap between current load conditions and load conditions one to two hours ahead For Balance-of-the-Day forecasting the stream of 4, 8, 12 hour ahead forecasts need to be more accurate - Requires a balance between reliance on autoregressive terms and current day weather forecasts For Day-Ahead forecasting the stream of 24, 48, 72, 96, 120, 144, 168 hour ahead forecasts need to be more accurate - Requires a greater reliance on weather driven models and weather forecasts
WHAT DOES MORE ACCURACY MEAN» More Accuracy Means: For Near-term forecasting the stream of 5, 15, 30, 60 minute ahead forecasts need to be more accurate - Requires accurately bridging the gap between current load conditions and load conditions one to two hours ahead - Ways to bridge the gap o Autoregressive load level model launching off last actual o Ramp rate model stitching off the last actual o Autoregressive load level model launching off last actual then a Ramp rate model stitching off the load level forecast
WHAT DOES MORE ACCURACY MEAN» More Accuracy Means: For Balance-of-the-Day aa e ay forecasting the stream of 4, 8, 12 hour ahead forecasts need to be more accurate - Requires a balance between reliance on autoregressive terms and current day weather forecasts - Ways to achieve this balance is to blend the forecast from two models o Hour Ahead model is a function of calendar, solar, weather, autoregressive terms o Day Ahead model is a function of calendar, solar, weather only o A weighting scheme is then used to form a weighted average forecast o Determining the cross over point requires judgment
WHAT DOES MORE ACCURACY MEAN» More Accuracy Means: For Day-Ahead forecasting the stream of 24, 48, 72, 96, 120, 144, 168 hour ahead forecasts need to be more accurate - Requires a greater reliance on weather driven models and weather forecasts - Ways to achieve greater day-ahead d forecasts o Day-Ahead models are a function of calendar, solar and weather o Use multiple weather forecasts and form a consensus load forecast o Option A is to weight the weather forecasts together and feed the consensus weather forecast into the Day-ahead model o Option B is to feed the Day-ahead model with each weather forecast and then weight the separate load forecasts
WHAT DOES MORE ACCURACY MEAN» More Accuracy Means: A Forecast Framework instead of a Forecast Model A Forecast Framework combines the Best Near-term forecast with the Best Balance-of-the-Day forecast with the Best Day- Ahead forecast Day Ahead Day Ahead Load Level Ramp Rate Hour Ahead Day Ahead Day Ahead Day Ahead
WHAT DOES MORE DETAIL MEAN» With more detail comes more noise in the metered data» More noise means models that rely on autoregressive ess e terms will produce a sequence of noisy forecasts Smooth = Stability Noise = Instability
WHAT DOES MORE DETAIL MEAN» Smoothing the Actual Loads before feeding to the model adds Stability Without Smoothing With Smoothing» Smoothing provides stability, but not accuracy!
SAVISTKY-GOLAY SMOOTHING Apply a series of local polynomial regressions
WHAT DOES MORE DETAIL MEAN» More noise also comes in the form of Spikes and unexpected Level Shifts Spikes = Instability Level Shift = Instability
WHAT DOES MORE DETAIL MEAN» Data Filtering removes spikes leading to stability Filter Removes Spike Filter Removes Shift» Removing Load Shifts depends on whether the goal is Load Following or Not
KALMAN FILTER Lˆ t ˆ~ K L O Lˆ~ t t t t L K t 2 e,t 2 2 e, t u, t
WHAT DOES MORE DETAIL MEAN» Removing spikes improves accuracy IF the spikes are ignored in comparing Actual vs. Forecast Filter Removes Spike Filter Removes Shift» Removing Load Shifts improves stability, but ruins accuracy. The Tradeoff depends on whether the goal is Load Following or Forecast Stability.
A UNIFIED FRAMEWORK
GOING GREEN INTRODUCES NEW FORECAST CHALLENGES
WIND GENERATION FORECASTING
FORECAST METHODS» Two general approaches to forecasting the generation output from a Wind Turbine Physical Models combine global meteorological forecasts with detailed modeling of the local topography of a wind farm to derive hub height wind speed forecasts. These forecasts then feed into an engineering-derived power curve. Statistical models derive the historical relationship between wind generation and atmospheric conditions. When autoregressive terms are added you get a Persistence Model. 26
PERSISTENCE MODEL SPECIFICATION
WIND GENERATION POWER CURVE PARAMETERS
ENGINEERING POWER CURVE EQUATION
WIND DIRECTION INTERACTIONS
ADJUSTING FOR TEMPERATURE CONDITIONS
PERSISTENCE MODEL SPECIFICATION
LOAD MANAGEMENT» Issue: Load Management are programs to reduce load during peak hours. Programs are dispatchable and are expected to increase in the coming years. Baseline Actual
HOW DO YOU FORECAST DEMAND RESPONSE?» Traditional Method Add up the contract amounts Ex post compare contracted load shed to realized load shed by lining up Actual Loads to a Like Day Shape In many instances, a Like Day Shape is constructed by averaging prior day load shapes (modeling is rarely used).» Challenges What people contract for is not always what they do, need to adjust for actual behavior Like Day Shapes may not be the best shape for the day in question How do you account for weather & market conditions?
ACTUAL VS. LIKE DAY BASELINE
HOW DO YOU FORECAST DEMAND RESPONSE?» Model Based Approaches Use statistical models to forecast customer loads with & without demand response. The delta is the forecasted demand response. Models incorporate weather & economic conditions. Models produce forecasts based on realized not contractual demand response. The same models can be used retroactively to evaluate actual demand response behavior.
HOW DO YOU FORECAST DEMAND RESPONSE?» Challenges to Model Based Approaches Individual dua customer modeling can be very difficult. Load data with demand response events are needed to capture the customer s response behavior. The behavior of new customers is assumed to be like other customers of similar type. Direct load control easiest to model. The others involve behavior which may or may not be able to model. More complicated than Like Day Approaches, but will be more accurate.
MODEL BASED APPROACH Load Shed
REBOUND EFFECT: INCLUDE OR IGNORE? Rebound Cooling
SOLAR GENERATION
ENGINEERING BEHIND SOLAR POWER MODELS Solar Generation h = Solar Insolation(kWh/m 2 ) h x Efficiency(W Out/Peak W) h x Installed Capacity (m 2 ) Solar Insolation = f(location, Time of Year, Time of Day, Cloud Cover) Efficiency = f(time of Year, Temperature, Angle, etc.)
SOLAR SHAPES
SOLAR DRIVERS» Hours of sunlight» Cloud Cover» Solar Radiation» Temperature
ELECTRIC VEHICLES Issue: Electric Vehicles are emerging on the issues list as new electric and plug-in hybrid vehicles are coming to the market. Any vehicle that plugs into the electric grid has the potential to impact the forecaster job.
ELECTRIC VEHICLES Source: Putrus, Ghanim A.; Suwanapingkarl, P.; Johnston, D. A.; Bentley, E. C.; Narayana, M., Impact of electric vehicles on power distribution networks. 2009
QUESTIONS? Press *6 to ask a question Remaining 2011 HANDS-ON WORKSHOPS» Fundamentals of Sales & Demand Forecasting September 22-23, Boston» Fundamentals of Short-Term and Hourly Forecasting September 28-30, San Diego» Forecasting 101 - October 24-26, San Diego OTHER FORECASTING MEETINGS» 2011 Itron Users' Conference - September 18-20, Phoenix For more information and registration: www.itron.com/forecastingworkshops Contact us at: 1.800.755.9585, 1.858.724.2620 or forecasting@itron.com com