Kasemsan Manomaiphiboon

Transcription

1 Wind Forecasting in Thailand Kasemsan Manomaiphiboon The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut s University it of Technology Thonburi (KMUTT) CIGRE-TNC Technical Seminar, Royal River Hotel, Bangkok, Jun. 20, 2014

2 Objective To give a broad & general overview of wind energy forecasting 2

3 Outline What is wind energy forecasting? Available methods Forecast evaluation EGAT-funded project 3

4 Wind Energy Forecasting Or called wind power forecasting To predict how wind power will be ahead of time 2 Main components to be coupled with: 1. Wind speed forecasting 2. Power curve of wind turbine 4

5 Coupling forecast (sub)hourly wind speed with power curve gives FORCAST ENERGY PRODUCED! 5

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7 Why need it? With a large wind-turbine system installed and help supplying power to a electricity grid Automatically deal with variability of wind power generation Winds are intermittent (not continuous). Humans are not in control of winds. Question: How to effective grid management by operators Answer: Wind speed forecasting 7

8 Wiki 8

9 Wind Speed Forecasting In a large sense, like weather forecasting But limited to just wind speed Generally, oriented for a particular site and at a particular hub height Not much of interest for wind direction Turbines normally turn towards wind direction. 9

10 Factors to influence winds: Synoptic weather Orography (i.e., terrain) Small-scale processes Most importantly, turbulence-associated Thus, not that t easy! 10

11 To make matter worse, cube law of wind power Wind Power Density (WPD, W m -2 ) = ρ 1 2 : ρ s 3 (as Air density, power per unit sweeping -3 kg m area) s : Wind speed, -1 m s 11

12 Given error in s = p% ( p = : p ρ s 2 + s ρ s ρ s p = % and ), error in WPD 100% 12

13 For example, p% = + 10%, WPD error + p % = 10%, WPD error 33% 27% p% % = + 26%, WPD error + 100% *** 13

14 Outline What is wind energy forecasting? Available methods Forecast evaluation EGAT-funded project 14

15 2 Major categories: 1. Statistical method: Time-series models 2. Physical method: Numerical weather prediction (NWP) models 2 Minor categories: 1. Spatial correlation models 2. Artificial intelligence models and others 15

16 Time-Series Models What is a time-series i model? dl? An algebraic function of historical values with coefficients as weights Conventional but widely used in wind speed forecasting Applied to real conditions with some success Found in other areas, e.g., finance & stock, disaster, climate, and environment 16

17 Classes of time-series model: AR: Autoregressive MA: Moving average ARIMA: Autoregressive (integrated) t moving average SARIMA: Seasonal ARIMA Com mplexity 17

18 AR( p ) : φ ( B ) x t = ω t MA ( q) : x t = θ ( B) ω t d ARIMA ( p, d, q) : φ ( B) (1 B) x t = θ ( B) ω t 2 p φ( B) = 1 φ 1 B φ 2 B L φ p B 2 q θ ( B ) = 1 + θ 1 B + θ 2 B + L + θq B B : Backshift operator θ i & φ i : Model coefficients 18

19 SARIMA ( p,d,q ) ( P,D,Q) 123 S = hr Non-seasonal component Seasonal component P ( S ) ( )( S ) D ( ) d ( S B φ B 1 B B x = Θ B ) θ ( B) t Φ 1 t Q ω 19

20 How to apply it: Acquire (sub)hourly historical wind speed data Choose a potential time-series model Fit the model Evaluate model validity Apply the fitted model for forecasting 20

21 Pros: Simplicity (relatively) At minimum, only wind speed data required Extensibility to be more complex Cons: Truly algebraic, i.e., not deal with or account for atmospheric theories or processes Forecasts as mean state (as opposed to full state = mean + fluctuation) 21

22 Example: Tower Data At a site in Rayong province, eastern Thailand Owned and operated by PCD Hourly monitoring i Wind: Ultrasonic anemometers Heights (m agl): (10), 50, and

23 Example of 1) historical fits and 2) Aug. 18 forecasts using ARIMA 23

24 Example of 1) historical fits and 2) Aug. 18 forecasts using ARIMA with de-diurnalization 24

25 NWP Models What is a NWP model? A complex model that dynamically solves a number of governing equations of air mass, momentum, heat, and moisture Predict in 3D over time Tool: Atmospheric research Professional weather forecasting Scales: Global, l regional, meso-scale, and locall Limited Area 25

26 McGuffie & Henderson-Sellers 26

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28 Limited-Area Models And many more models! 28

29 WRF-NCAR 29

30 Pros: Account for state-of-science atmospheric processes Applicable to coarse to fine spatial resolutions Cons: Computational resources Time for implementation Human factor (skill and experience) Uncertainty and imperfection in integrated science 30

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33 Outline What is wind energy forecasting? Available methods Forecast Evaluation EGAT-funded project 33

34 Forecast Evaluation To technically judge: How do different models perform? How do different setups in a model perform? How does a model predict against observations? Tools: Statistical metrics Graphic 34

35 Persistence Models What are they? Any very simple models that are used as comparative reference for other sophisticated models For example: Wind speed at the next hour is given as that at the current hour Wind speeds at the next 2 hours is 35

36 Essential Metrics Mean Bias (MB) Mean Error (ME) = = 1 N ( P ) i O i N i= 1 1 N N N i= 1 P i O i Root Mean Square Error (RMSE) = 1 N N i= 1 ( ) P i O i 2 Correlatio n : ρ = Cov ( P, O ) σ σ P O 36

37 Taylor Diagram 37

38 Monthly Mean Error 38

39 Monthly Correlation P1 ARIMA darima SARIMA VAR Correla ation Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. All 39

40 ME over Forecast Hours Mar. Aug. Annual 40

41 ME (%) over Forecast Hours Annual 41

42 ME over Wind Speed Ranges Annual 42

43 Outline What is wind energy forecasting? Available methods Forecast Evaluation EGAT-funded project 43

44 EGAT-Funded d Project To develop a wind speed forecasting system For an area over Khao Yai Tieng, near Lam Ta Khong Dam, Nkh Nakhon Ratchasima province An ensemble of statistical and NWP models Research duration of 24 months Big challenge: Strongly terrain-influenced winds 44

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46 Dynamical Downscaling 50 km (GFS) 9 km 3km 1 km

47 30 x 30 เซลล

48 Contributor to Many Slides William Mwangi Kaigwara Former MEng student at JGSEE Currently, engineer at Ministry of Energy and Petroleum, Kenya 48

49 Research Support 49

50 ขอบคณ ありがとう Thank You Merci