Optimization of the forecasting of wind energy production Focus on the day ahead forecast

Transcription

1 Optimization of the forecasting of wind energy production Focus on the day ahead forecast Market Processes, EnBW Transportnetze AG Mirjam Eppinger, Dietmar Graeber, Andreas Semmig Quantitative Methods, University of Hohenheim Prof. Dr. Andreas Kleine Energie braucht Impulse

2 Contents Forecasting and wind energy production in context Empirical analysis of the wind course Selection criteria Clustering Modeling 2

3 Forecasting and wind energy production in context Renewable Energy Law Feed-in priority Grid connection priority Fixed remuneration Purpose of forecasting: Forecasting's are supposed to reduce uncertainties about certain future courses and also to minimize the risk of incorrect decisions. Decisions: A TSO has to decide how much energy has to be bought or sold. 3

4 MW MW Forecasting and wind energy production in context 1. Situation: Forecast > Wind energy in-feed Energy has to be bought 2. Situation: Forecast < Wind energy in-feed Energy has to be sold Time Time The action of a TSO has an impact on the prices of the day ahead and the intraday market. In-feed Forecast 4

5 Contents Forecasting and wind energy production in context Empirical analysis of the wind course Selection criteria Clustering Modeling 5

6 MW Empirical analysis of the wind course Observation period from November 2007 to October 2008 Time 6

7 Empirical analysis of the wind course High fluctuation of wind Differentiation of seasons does not lead to any result for the day ahead forecast Differentiation of months does not lead to any result for the day ahead forecast, i.e. within the months exist really different incompatible situations 7

8 Contents Forecasting and wind energy production in context Empirical analysis of the wind course Selection criteria Clustering Modeling 8

9 MW Selection criteria Identification of typical criteria of wind Cycle Definition: A cycle includes a decreasing and an increasing wind phase. I. II. III. Time 9

10 MW Selection criteria Average of wind in-feed Definition: In-feed quantity per cycle divided by the corresponding cycle time. I. II. III. Time 10

11 MW Selection criteria Volatility of wind in-feed Definition: Volatility is understood as the difference between the maximum and minimum values of wind in-feed (normalized by the related installed capacity). I. II. III. Time 11

12 Selection criteria Further proceeding: Identification of cycles Calculation of the average of wind in-feed per cycle Calculation of the corresponding volatility 12

13 Normalized volatility normierte Volatilität Selection criteria Graphic chart of cycles considering the other 2 criteria 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0, Duchschnittliche Leistung eines Zyklus Average of wind in-feed MW 13

14 Contents Forecasting and wind energy production in context Empirical analysis of the wind course Selection criteria Clustering Modeling 14

15 Normalized volatility normierte Volatilität Clustering 0,80 0,70 III. VI. 0,60 0,50 II. V. 0,40 0,30 0,20 I. IV. 0,10 0, durchschnittliche Leistung eines Zyklus Average of wind in-feed MW 15

16 Contents Forecasting and wind energy production in context Empirical analysis of the wind course Selection criteria Clustering Modeling 16

17 Modeling Actual forecast method: 4 different wind power predictions parameter-estimation on base of past-data (actual wind data, 4 predictions) Differentiation of 2 different situations 1. Data since 4 weeks before forecasting-time 2. Data since 12 months before forecasting-time The results of the parameter-estimations of the 2 different periods will be combined. 17

18 Modeling Benefit of the combination of forecasts: Using of different forecasting models (e.g. Artificial Neuronal Networks, physical methods etc.) Using of different weather services Minimizing the risk of individual forecast errors 18

19 Modeling Idea: Minimization of the forecast error = Improvement of the forecast quality Using the Root Mean Square Error (RMSE) as characteristic factor for the forecast error Model: min RMSE opt, q 1 N N t 1 x f x Pinst ist 2 (1). x f A a 1 k a x a (2) m (3) k (4) v A a 1 k a 0 1 mit a 1,2,3,4 ka 1 mit a 1,2,3,4 x f, xa 0 mit a 1,2,3,4 RMSE opt, q Optimized root mean squared error of Pinst Installed capacity Forecast of the wind in feed x f xist Actual wind in feed cluster q ka Parameter of forecast a xa Forecast of provider a 19

20 Modeling Calculation of the optimal cluster-parameters for the k a observation period in consideration of the identified cycles. Using of the calculated optimal parameters for the next 9 month (ex-post forecast) Calculating of the old and new RMSEs of the ex-post forecast period 20

21 Modeling Comparison of the RMSEs: Number Cluster RMSE new RMSE old of cycles I. 2,99% 3,47% 104 II. 4,50% 4,82% 16 III. 5,29% 4,87% 2 IV. 3,94% 3,07% 6 V. 5,53% 5,76% 24 VI. 4,85% 5,87% 1 21

22 Thank you for your attention! Energie braucht Impulse