Weather Scenarios. Tailoring Weather Scenarios to Suit Your Needs

Transcription

1 Weather Scenarios Tailoring Weather Scenarios to Suit Your Needs

2 Tailoring Weather Scenarios 1. Need to ensure we have the right weather for the analysis 2. Need to maintain a level of consistency Amongst forecasts Amongst zones 3. Effort to communicate weather concepts in a clear way

3 Analytical Requirements Outage scheduling Capacity planning Generation mix Communications Transmission planning Regulatory requirements Business planning and finance Conservation analysis Deterministic and probabilistic analysis One approach cannot meet all these needs

4 System Determines Needs Large diverse area 2 time zones & 10 transmission zones Internal transmission constraints 20,000 km of high voltage transmission Demand Dual peaking Minimums & Peaks Generation Baseload and intermittent Interconnections and inter-regional issues

5 Diversity and Coincidence Ontario is a large geographic area System diversity System broken into 10 transmission zones Diverse weather conditions Diverse weather responses Heating load Cooling load Diverse economy Primary industries Energy intensive industries Financial and services sector

6 January 13 th C (30.6 F) 7.6 C (45.7 F) C (2.1 F) 17.4 C (63.3 F) 12.1 C (53.8 F) 10.7 C (51.3 F)

7 January 28 th C (-6.7 F) -2.9 C (26.8 F) C (7.0 F) 11.0 C (51.8 F) 12.6 C (54.7 F) 4.7 C (40.5 F)

8 September 10 th C (57.4 F) 26.1 C (79.0 F) 22.5 C (72.5 F) 34.2 C (93.6 F) 32.9 C (91.2 F) 32.8 C (91.0 F)

9 Clarity of Weather Scenarios Need to communicate clearly what the weather scenarios represent. Industry familiar with concepts of Normal and Extreme but require something tangible. Ever try to explain to management that the peak day is based on a THI of 32.5 C

10 IESO Approach How do we maintain consistency across the zones? How do we make sure that the weather concepts are understandable? Keep weather date specific!

11 Estimating Daily Weather Impacts Using our daily peak models Estimate over a short but stable time horizon Using the model coefficients build the daily peak weather impact of your historic data We use 31 years of weather history Let s look at constructing a Normal month

12 Demand Model Demand = f (weather, calendar, economic) Demand = f (temperature, humidity, cloud cover, wind speed, calendar, economic) Demand t = α + β 1 Temp t + β 2 Humidity t + β 3 Wind t + β 4 Cloud t ε τ Demand t = 13, *Temp t + 152*Humidity t - 13*Wind t - 102*Cloud t ε t Weather Impact t = 419*Temp t + 152*Humidity t - 13*Wind t - 102*Cloud t Use the model to quantify the weather impact

13 Weather Impact for July 23 rd 1982 June 23 rd, ,787 MW = 26.9 C % + 14 km/hr + scattered cloud WF f( Temperature, Humidity, Wind Speed & Cloud Cover)

14 July Weather Impacts Day , ,589-6,522-3, , , , , , , ,641-2,071-1, , ,571-1,227 3,107-2,152-5,384-4, ,213-3,199 1,115-2, ,229-4,294-1,112-3,113-2, ,509-4,356 3,618-3, ,591-4,194-2,171-2,109-2, ,826-4,786 4,990-3,861-1,204 5,146-1,595-3,261-2,167-2, ,944-5,770 3,056-4, , ,083-2,102-3, ,549-5,245 2,596-4,914-2,613 5,568-1,011-3,721-3,328-5, ,314 3,108-4,922-1,493 3, ,268-4, ,728 3,425-5,831-1,888 4,306-2, ,520-2, ,894 2,180-6,446-1,034 3,151-2,297-1,713-5, ,397-4,464 1,464-5,172-1,240 2,142-5,637-2,271-5, , , ,422-7,467-3,777-4, ,527-2,016 4, ,132 1,251-5,069-3,769-4,827-1, ,312-1,903 4, , ,822-3,970-4,584-1, ,727-2,514 4,307-2,082-1,588 1,558-3,044-5,208-5,009-1, ,509-3,158 5,619-4,576-2,194 1,784-2,749-3,386-5,829-3, ,881-3,044 3,760-3,142-2,444 2,104-2,630-2,629-4,682-2, ,001-2,678 1,824-4,691-2,437 2,472-2,567-2,720-3, ,459-1,079 1,848-5, ,076-3,040-3,971-5,174-1, , ,430-5, ,549-5,532-4,145-2, , ,778-5, ,655-3,427-1,985-2,773-1, , ,448-5, ,125-2, ,171-2, ,573-1,970 3,399-5,144-2,120 2,099-3,523-1,015-5,709-2, ,154-2,597 3,062-3,010-2,295 3,876-4,730-2,472-3,520-3, , ,557-1, ,365-3,892-3, , , ,275 4,264-4,305-5,085-1,288-3, ,765-1, ,710-4,832-5,513-1,757-3, ,089 1,976-3, ,180-3,068-4,938-1,756-3, ,388 1,721-1, ,185-4,348-5,581-2,293-4,684

15 July Data Sorted Ranked ,001-5,770 5,619-6,446-2,613 5,591-7,467-6,947-5,928-5, ,224-5,245 4,990-5,831-2,444 5,568-5,637-6,641-5,906-4, ,826-4,786 4,844-5,587-2,437 5,229-5,069-6,522-5,829-4, ,459-4,464 4,307-5,525-2,295 5,173-4,832-5,581-5,709-3, ,154-4,356 4,020-5,384-2,194 5,146-4,730-5,532-5,520-3, ,944-3,894 3,760-5,172-2,120 4,306-4,348-5,513-5,174-3, ,881-3,728 3,618-5,146-1,888 4,264-4,305-5,384-5,009-3, ,527-3,314 3,425-5,144-1,588 3,998-4,294-5,208-4,827-3, ,509-3,199 3,399-4,922-1,518 3,876-4,194-5,085-4,813-3, ,393-3,158 3,108-4,914-1,493 3,854-3,892-4,938-4,682-3, ,289-3,044 3,062-4,691-1,275 3,365-3,549-4,083-4,584-2, ,019-3,043 3,056-4,582-1,240 3,185-3,523-3,971-4,375-2, ,573-2,950 2,778-4,576-1,227 3,151-3,427-3,970-4,268-2, ,549-2,678 2,596-3,861-1,204 3,107-3,068-3,777-4,145-2, ,509-2,597 2,448-3,667-1,132 2,710-3,044-3,769-3,713-2, ,213-2,567 2,430-3,286-1,034 2,472-3,040-3,721-3,520-2, ,727-2,514 2,190-3, ,142-2,822-3,529-3,328-2, ,397-2,388 2,180-3, ,125-2,749-3,386-3,266-2, ,367-2,089 1,976-2, ,104-2,630-3,261-3,171-1, ,312-2,016 1,848-2, ,099-2,589-2,720-3,113-1, ,970 1,824-2, ,861-2,567-2,629-2,773-1, ,903 1,765-1, ,784-2,538-2,472-2,293-1, ,719 1,721-1, ,655-2,310-2,271-2,167-1, ,079 1,557-1, ,558-2,297-2,171-2,109-1, ,464-1, ,422-2,152-1,985-2, , ,251-1,595-1,713-2, , ,180-1,011-1,112-1, , ,015-1, ,

16 July Normal - Median Ranked Normal 1 6,001-5,770 5,619-6,446-2,613 5,591-7,467-6,947-5,928-5,474 5, ,224-5,245 4,990-5,831-2,444 5,568-5,637-6,641-5,906-4,719 5, ,826-4,786 4,844-5,587-2,437 5,229-5,069-6,522-5,829-4,684 4, ,459-4,464 4,307-5,525-2,295 5,173-4,832-5,581-5,709-3,394 4, ,154-4,356 4,020-5,384-2,194 5,146-4,730-5,532-5,520-3,342 4, ,944-3,894 3,760-5,172-2,120 4,306-4,348-5,513-5,174-3,307 4, ,881-3,728 3,618-5,146-1,888 4,264-4,305-5,384-5,009-3,280 3, ,527-3,314 3,425-5,144-1,588 3,998-4,294-5,208-4,827-3,258 3, ,509-3,199 3,399-4,922-1,518 3,876-4,194-5,085-4,813-3,166 3, ,393-3,158 3,108-4,914-1,493 3,854-3,892-4,938-4,682-3,035 3, ,289-3,044 3,062-4,691-1,275 3,365-3,549-4,083-4,584-2,911 3, ,019-3,043 3,056-4,582-1,240 3,185-3,523-3,971-4,375-2,611 3, ,573-2,950 2,778-4,576-1,227 3,151-3,427-3,970-4,268-2,591 2, ,549-2,678 2,596-3,861-1,204 3,107-3,068-3,777-4,145-2,582 2, ,509-2,597 2,448-3,667-1,132 2,710-3,044-3,769-3,713-2,367 2, ,213-2,567 2,430-3,286-1,034 2,472-3,040-3,721-3,520-2,247 2, ,727-2,514 2,190-3, ,142-2,822-3,529-3,328-2,121 2, ,397-2,388 2,180-3, ,125-2,749-3,386-3,266-2,031 2, ,367-2,089 1,976-2, ,104-2,630-3,261-3,171-1,868 2, ,312-2,016 1,848-2, ,099-2,589-2,720-3,113-1,867 1, ,970 1,824-2, ,861-2,567-2,629-2,773-1,274 1, ,903 1,765-1, ,784-2,538-2,472-2,293-1,221 1, ,719 1,721-1, ,655-2,310-2,271-2,167-1,023 1, ,079 1,557-1, ,558-2,297-2,171-2,109-1,009 1, ,464-1, ,422-2,152-1,985-2, , , ,251-1,595-1,713-2, , ,180-1,011-1,112-1, , ,015-1, ,

17 July Extreme - Maximum Ranked Extreme 1 6,001-5,770 5,619-6,446-2,613 5,591-7,467-6,947-5,928-5,474 7, ,224-5,245 4,990-5,831-2,444 5,568-5,637-6,641-5,906-4,719 6, ,826-4,786 4,844-5,587-2,437 5,229-5,069-6,522-5,829-4,684 6, ,459-4,464 4,307-5,525-2,295 5,173-4,832-5,581-5,709-3,394 5, ,154-4,356 4,020-5,384-2,194 5,146-4,730-5,532-5,520-3,342 5, ,944-3,894 3,760-5,172-2,120 4,306-4,348-5,513-5,174-3,307 5, ,881-3,728 3,618-5,146-1,888 4,264-4,305-5,384-5,009-3,280 5, ,527-3,314 3,425-5,144-1,588 3,998-4,294-5,208-4,827-3,258 5, ,509-3,199 3,399-4,922-1,518 3,876-4,194-5,085-4,813-3,166 5, ,393-3,158 3,108-4,914-1,493 3,854-3,892-4,938-4,682-3,035 4, ,289-3,044 3,062-4,691-1,275 3,365-3,549-4,083-4,584-2,911 4, ,019-3,043 3,056-4,582-1,240 3,185-3,523-3,971-4,375-2,611 4, ,573-2,950 2,778-4,576-1,227 3,151-3,427-3,970-4,268-2,591 4, ,549-2,678 2,596-3,861-1,204 3,107-3,068-3,777-4,145-2,582 4, ,509-2,597 2,448-3,667-1,132 2,710-3,044-3,769-3,713-2,367 3, ,213-2,567 2,430-3,286-1,034 2,472-3,040-3,721-3,520-2,247 3, ,727-2,514 2,190-3, ,142-2,822-3,529-3,328-2,121 3, ,397-2,388 2,180-3, ,125-2,749-3,386-3,266-2,031 3, ,367-2,089 1,976-2, ,104-2,630-3,261-3,171-1,868 3, ,312-2,016 1,848-2, ,099-2,589-2,720-3,113-1,867 3, ,970 1,824-2, ,861-2,567-2,629-2,773-1,274 2, ,903 1,765-1, ,784-2,538-2,472-2,293-1,221 2, ,719 1,721-1, ,655-2,310-2,271-2,167-1,023 2, ,079 1,557-1, ,558-2,297-2,171-2,109-1,009 2, ,464-1, ,422-2,152-1,985-2, , , ,251-1,595-1,713-2, , , ,180-1,011-1,112-1, , , ,015-1, , , ,

18 July Mild - Minimum Ranked Mild 1 6,001-5,770 5,619-6,446-2,613 5,591-7,467-6,947-5,928-5,474 2, ,224-5,245 4,990-5,831-2,444 5,568-5,637-6,641-5,906-4,719 2, ,826-4,786 4,844-5,587-2,437 5,229-5,069-6,522-5,829-4,684 2, ,459-4,464 4,307-5,525-2,295 5,173-4,832-5,581-5,709-3,394 2, ,154-4,356 4,020-5,384-2,194 5,146-4,730-5,532-5,520-3,342 2, ,944-3,894 3,760-5,172-2,120 4,306-4,348-5,513-5,174-3,307 2, ,881-3,728 3,618-5,146-1,888 4,264-4,305-5,384-5,009-3,280 1, ,527-3,314 3,425-5,144-1,588 3,998-4,294-5,208-4,827-3,258 1, ,509-3,199 3,399-4,922-1,518 3,876-4,194-5,085-4,813-3,166 1, ,393-3,158 3,108-4,914-1,493 3,854-3,892-4,938-4,682-3,035 1, ,289-3,044 3,062-4,691-1,275 3,365-3,549-4,083-4,584-2,911 1, ,019-3,043 3,056-4,582-1,240 3,185-3,523-3,971-4,375-2,611 1, ,573-2,950 2,778-4,576-1,227 3,151-3,427-3,970-4,268-2,591 1, ,549-2,678 2,596-3,861-1,204 3,107-3,068-3,777-4,145-2,582 1, ,509-2,597 2,448-3,667-1,132 2,710-3,044-3,769-3,713-2,367 1, ,213-2,567 2,430-3,286-1,034 2,472-3,040-3,721-3,520-2,247 1, ,727-2,514 2,190-3, ,142-2,822-3,529-3,328-2, ,397-2,388 2,180-3, ,125-2,749-3,386-3,266-2, ,367-2,089 1,976-2, ,104-2,630-3,261-3,171-1, ,312-2,016 1,848-2, ,099-2,589-2,720-3,113-1, ,970 1,824-2, ,861-2,567-2,629-2,773-1, ,903 1,765-1, ,784-2,538-2,472-2,293-1, ,719 1,721-1, ,655-2,310-2,271-2,167-1, ,079 1,557-1, ,558-2,297-2,171-2,109-1, ,464-1, ,422-2,152-1,985-2, , ,251-1,595-1,713-2, , ,180-1,011-1,112-1, , ,015-1, ,

19 July Peak Weather Dates July Peak Date (July 9th, 1992) - Mild Scenario Station Temperature Relative Wind Speed ( C) Humidity (%) (Km/hr) Cloud Cover Kapuskasing % 19.8 Cloudy NorthBay % 12.0 Partly Cloudy Ottawa % 27.0 Cloudy ThunderBay % 9.3 Partly Cloudy Toronto % 30.5 Partly Cloudy Windsor % 25.3 Cloudy July Peak Date (July 6th, 1993) - Normal Scenario Kapuskasing % 24.0 Partly Cloudy NorthBay % 21.5 Cloudy Ottawa % 23.8 Cloudy ThunderBay % 21.3 Overcast Toronto % 28.8 Cloudy Windsor % 14.3 Cloudy July Peak Date (July 14th, 1995) - Extreme Scenario Kapuskasing % 13.3 Clear NorthBay % 18.2 Overcast Ottawa % 17.0 Clear ThunderBay % 8.0 Overcast Toronto % 17.3 Clear Windsor % 18.2 Clear

20 Normalization Periods Can normalize a number of different ways: Daily, Weekly, Monthly & Seasonal Use rank and sort approach on each Each normalization period has a different distribution

21 July Peak Various Normalizations 8,000 7,000 Extreme (0.0 %) Seasonal Normal (2.5%) 6,000 Monthly Normal (2.9%) 5,000 Weather Impact (MW) 4,000 3,000 Weekly Normal (13.4%) Monthly Mild (45%) 2,000 1, % 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% -1,000

22 Impacts More than Peak 8,000 7,000 6,000 5,000 4,000 MW 3,000 2,000 1, ,000 Monthly Normal Weekly Normal

23 Mapping - Scenarios IESO uses a contrived weekly profile for Weekly Normalization Ensures that peak is on a Wednesday to avoid most holidays All other scenarios are ranked and mapped off of the Weekly Normal profile Possibility of using historical profile Benefit contains the temporal flow of weather Calendar can have a negative impact.

24 Capturing Uncertainty We do analysis that is both deterministic and probabilistic as such we need a measure of uncertainty We can construct measures of uncertainty in the same way we construct weather scenarios Essentially, we trade certainty for uncertainty.

25 July Normal - Median Standard Deviation Ranked Normal 1 6,001-5,770 5,619-6,446-2,613 5,591-7,467-6,947-5,928-5,474 5, ,224-5,245 4,990-5,831-2,444 5,568-5,637-6,641-5,906-4,719 5, ,826-4,786 4,844-5,587-2,437 5,229-5,069-6,522-5,829-4,684 4, ,459-4,464 4,307-5,525-2,295 5,173-4,832-5,581-5,709-3,394 4, ,154-4,356 4,020-5,384-2,194 5,146-4,730-5,532-5,520-3,342 4, ,944-3,894 3,760-5,172-2,120 4,306-4,348-5,513-5,174-3,307 4, ,881-3,728 3,618-5,146-1,888 4,264-4,305-5,384-5,009-3,280 3, ,527-3,314 3,425-5,144-1,588 3,998-4,294-5,208-4,827-3,258 3, ,509-3,199 3,399-4,922-1,518 3,876-4,194-5,085-4,813-3,166 3, ,393-3,158 3,108-4,914-1,493 3,854-3,892-4,938-4,682-3,035 3, ,289-3,044 3,062-4,691-1,275 3,365-3,549-4,083-4,584-2,911 3, ,019-3,043 3,056-4,582-1,240 3,185-3,523-3,971-4,375-2,611 3, ,573-2,950 2,778-4,576-1,227 3,151-3,427-3,970-4,268-2,591 2, ,549-2,678 2,596-3,861-1,204 3,107-3,068-3,777-4,145-2,582 2, ,509-2,597 2,448-3,667-1,132 2,710-3,044-3,769-3,713-2,367 2, ,213-2,567 2,430-3,286-1,034 2,472-3,040-3,721-3,520-2,247 2, ,727-2,514 2,190-3, ,142-2,822-3,529-3,328-2,121 2, ,397-2,388 2,180-3, ,125-2,749-3,386-3,266-2,031 2, ,367-2,089 1,976-2, ,104-2,630-3,261-3,171-1,868 2, ,312-2,016 1,848-2, ,099-2,589-2,720-3,113-1,867 1, ,970 1,824-2, ,861-2,567-2,629-2,773-1,274 1, ,903 1,765-1, ,784-2,538-2,472-2,293-1,221 1, ,719 1,721-1, ,655-2,310-2,271-2,167-1,023 1, ,079 1,557-1, ,558-2,297-2,171-2,109-1,009 1, ,464-1, ,422-2,152-1,985-2, , , ,251-1,595-1,713-2, , ,180-1,011-1,112-1, , ,015-1, ,

26 Uncertainty Considerations Weather impacts may not be normally distributed Complications in the shoulder months when both heating and cooling may be in play Weather Impact (MW) 1,800 1,600 1,400 1,200 1, Jul Jul Jul Jul-08 Weekly Normal Uncertainty Monthly Normal Uncertainty Seasonal Normal Uncertainty 3-Aug-08

27 Weather Scenarios IESO uses a variety of scenarios depending on use and purpose Probabilistic for Reserve Requirements Normal Weather Normal + Load Forecast Uncertainty (LFU) Weather LFU is used to build distributions around Normal Deterministic for Analysis and Study Mild Weather Extreme Weather Actual historic years 1977, 1990 & 1994 for Winter 1990, 1999 & 2002 for Summer Various normalization periods (Daily, Weekly, Monthly & Seasonal)

28 Analysis & Scenarios Analysis Outage Scheduling Capacity Planning Generation Mix Communications Business Planning Transmission Planning Regulatory Requirements Management Energy and peaks Revenues Considerations Probabilistic analysis Capacity constraints Peak considerations & imports Peak considerations Need to carry imports Multi-regional studies Percent of time analysis and risk analysis Scenario Monthly/Weekly Normal and Uncertainty Seasonal Normal Monthly/Weekly Normal Seasonal Normal Monthly Normal and Mild Weather Extreme Weather Historical weather year Historical weather years

29 Other Issues Normalization period? IESO uses 31 for Normal and uncertainty Climate Change and the urge to use shorter periods Zonal versus System perspective Currently our weather scenarios are from a system perspective Certain work requires a zonal perspective Uncertainty Shoulder periods

30 Questions?