STATISTICAL CONTEMPLATION OF BALANCING ENERGY IN AUSTRIA

Transcription

1 STATISTICAL CONTEMPLATION OF BALANCING ENERGY IN AUSTRIA Activated quantities of secondary and tertiary balancing energy in the context of regression and time series analysis David Lun, Tara Esterl, Fabian Leimgruber AIT Austrian Institute of Technology GmbH Giefinggasse Vienna Austria T M tara.esterl@ait.ac.at IAEE 2017 Conference, 4 th September 2017, Vienna

2 RESEARCH QUESTIONS & METHODS Research questions Which factors influence the released quantities of positive and negative balancing energy in Austria? Compilation and evaluation of intradayforecasts of positive and negative balancing energy. Methods Linear Regression Tobit Model SARIMA Weighted k-nearest Neighbour Regression 04/09/2017 2

3 SUMMARY I. Brief introduction to balancing energy II. Examining the time series III. Influence of exogenous factors and forecast 04/09/2017 3

4 WHAT IS BALANCING ENERGY? Electricity grid connects producers and consumers electricity can t be stored in the grid Amount of extracted electricity has to equal supply for a functioning grid Indicator: Net frequency target: 50Hz Supply demand stability of grid endangered Balancing energy compensates fluctuations Primary Secondary Tertiary 04/09/ I II III

5 HOW DOES BALANCING ENERGY WORK? There can be too much or too little electricity supplied to a grid Further distinction Positive balancing energy Negative balancing energy Balancing energy is organized by the TSO, supplied by producers of the electricity market Static provision of power for a specified time frame Strict requirements for participants of the market for balancing energy 04/09/ I II III

6 SUMMARY I. Brief introduction to balancing energy II. Examining the time series III. Influence of exogenous factors and forecast 04/09/2017 6

7 SUPPLIED QUANTITY OF POSITIVE BALANCING ENERGY IN 2015 Quantitiy supplied refers to aggregated amount of secondary and tertiary control reserves Time series with ¼-hourly resolution and non-negative values Observations from 2015 Fixed boundaries (quantities contracted) Presentation: Examination reduced to positive balancing energy 04/09/ I II III

8 AUTOCORRELATION OF SUPPLIED QUANTITY OF POSITIVE BALANCING ENERGY High dependency on past realizations Peaks at multiplicities of 96 lags seasonal pattern? (96 ¼-hours equals a day) Stationary? ADF-Test: KPSS-Test: Why is stationarity so important? estimation procedures biased / inconsistent / inefficient 04/09/ I II III

9 SEASONAL COMPONENT: TIME OF THE YEAR DAY OF THE WEEK 04/09/ I II III

10 SEASONAL COMPONENT: TIME OF THE DAY Significant disparities Numerous values close or equal to zero and many outliers Big differences in volatility at different times of the day 04/09/ I II III

11 SUMMARY I. Brief introduction to balancing energy II. Examining the time series III. Influence of exogenous factors and forecast 04/09/

12 VARIABLES WITH POTENTIAL INFLUENCE ON SUPPLIED QUANTITY OF BALANCING ENERGY Load Feed-in from wind energy Feed-in from photovoltaics Unintentional deviation of electricity (import/export) Blackouts of power generating units Volume of intraday-market for electricity Time of the day, weekdays, holidays, Temperature, wind velocity, solar radiation, storage level of water reservoir / storage units, 04/09/ I II III

13 CLASSIC INSPECTION OF INFLUENCE: TEST OF STATISTICAL SIGNIFICANCE y t = x 1,t β 1 + x 2,t β ε t β i =0?? T-Test Error term / residuals heteroskedastic? Autocorrelated? White / Newey-West Time series non stationary? β biased / inconsistent 04/09/ I II III

14 METHOD TO QUANTIFY INFLUENCE Cross-validation Various model specifications estimated Exclude variables from model Estimate model with remaining variables Estimate model with single variable Sample for fitting the model: 11 months Sample for evaluating the model: 1 month Evaluation-criteria: RMSE / MAE / IOA / 04/09/ I II III

15 VARIABLES WITH BIGGEST INFLUENCE ON ACTIVATED POSITIVE BALANCING ENERGY Unintentional electricity deviation (include frequency containment reserve (Primary control reserves)) Forecast error from wind feed-in Feed-in from wind 04/09/ I II III

16 FORECAST Cross-validation Various model specifications and estimation procedures Regressors include lags of dependant variable (balancing energy) Linear Regression, Tobit Model, SARIMA, k-nearest Neighbour Regression Evaluation-criteria: RMSE / MAE / IOA / 04/09/ I II III

17 FORECAST: COMPARISON OF MODELS AND SPECIFICATIONS Linear Regression and Tobit Model deliver best results Yeo-Johnson-transformation produces lowest MAE Naive forecast performs comparatively good RMSE / MAE / IOA / 04/09/ I II III

18 CONCLUSION Most influental variables: Unintentional electricity deviation and forecast error from wind feed-in Quality of forecast is good in the short-term, but the quality of the forecast diminishes rapidly More complex models: linear interdependent equations, SARIMAX, temporary integrated Alternatively: Forecasts of safety levels ( dynamic day-ahead dimensioning), distribution Unfortunately omitted variables Temperature, wind velocity, solar radiation, storage levels, 04/09/ I II III

19 STATISTICAL CONTEMPLATION OF BALANCING ENERGY IN AUSTRIA Activated quantities of secondary and tertiary balancing energy in the context of regression and time series analysis David Lun, Tara Esterl, Fabian Leimgruber AIT Austrian Institute of Technology GmbH Giefinggasse Vienna Austria T M tara.esterl@ait.ac.at IAEE 2017 Conference, 4 th September 2017, Vienna