STATISTICAL CONTEMPLATION OF BALANCING ENERGY IN AUSTRIA Activated - - PowerPoint PPT Presentation

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STATISTICAL CONTEMPLATION OF BALANCING ENERGY IN AUSTRIA Activated - - PowerPoint PPT Presentation

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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

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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 2 | 1210 Vienna | Austria T +43 50550-6077 | M +43 664 8157810 tara.esterl@ait.ac.at | www.ait.ac.at IAEE 2017 Conference, 4th September 2017, Vienna

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RESEARCH QUESTIONS & METHODS

Research questions

  • Which factors influence the released

quantities of positive and negative balancing energy in Austria?

  • Compilation and evaluation of intraday-

forecasts of positive and negative balancing energy.

Methods

  • Linear Regression
  • Tobit Model
  • SARIMA
  • Weighted k-Nearest Neighbour

Regression

04/09/2017 2

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SUMMARY

  • I. Brief introduction to balancing energy
  • II. Examining the time series
  • III. Influence of exogenous factors and forecast

04/09/2017 3

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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

04/09/2017 4

I II III

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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

04/09/2017 5

I II III

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SUMMARY

  • I. Brief introduction to balancing energy
  • II. Examining the time series
  • III. Influence of exogenous factors and forecast

04/09/2017 6

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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

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I II III

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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

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I II III

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SEASONAL COMPONENT: TIME OF THE YEAR – DAY OF THE WEEK

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I II III

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SEASONAL COMPONENT: TIME OF THE DAY

  • Significant disparities
  • Numerous values close or

equal to zero and many

  • utliers
  • Big differences in volatility

at different times of the day

04/09/2017 10

I II III

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SUMMARY

  • I. Brief introduction to balancing energy
  • II. Examining the time series
  • III. Influence of exogenous factors and forecast

04/09/2017 11

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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, … I II III

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„CLASSIC“ INSPECTION OF INFLUENCE: TEST OF STATISTICAL SIGNIFICANCE

𝑧𝑢 = 𝑦 1,𝑢 β1 + 𝑦2,𝑢β2 + ⋯ + ε𝑢 → β𝑗 =0 ?? → T-Test Error term / residuals heteroskedastic? Autocorrelated? → White / Newey-West Time series non stationary? → β biased / inconsistent

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I II III

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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 /

…

04/09/2017 14

I II III

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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

04/09/2017 15

I II III

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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 / …

04/09/2017 16

I II III

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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 / …

04/09/2017 17

I II III

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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, …

04/09/2017 18

I II III

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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 2 | 1210 Vienna | Austria T +43 50550-6077 | M +43 664 8157810 tara.esterl@ait.ac.at | www.ait.ac.at IAEE 2017 Conference, 4th September 2017, Vienna