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Mercati energetici e metodi manuele.aufiero@milanomultiphysics.com quantitativi nicholas.bonfanti@milanomultiphysics.com 18 ottobre 2018 Previsioni di fonti rinnovabili non programmabili: un modello integrato per l'idroelettrico ad acqua

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Previsioni di fonti rinnovabili non programmabili: un modello integrato per l'idroelettrico ad acqua fluente Milano Multiphysics -- ENTSO-E

Mercati energetici e metodi quantitativi 18 ottobre 2018

Smart solutions for complex problems manuele.aufiero@milanomultiphysics.com nicholas.bonfanti@milanomultiphysics.com

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MMP - ENTSO-E

SMHI data for Hydro database

Main goals of the developed approach ➔ Assess suitability of SMHI data for

the production of hydro databases

➔ Investigate the possibility of

modeling hydro plant inflows without geographic information

➔ Study an automated procedure for

automated regression of RoR inflow

➔ Generation of synthetic hydro time series for adequacy studies

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MMP - ENTSO-E

SMHI data for Hydro database

Main goals of the developed approach ➔ Perform additional analyses:

◆ Automatic identification of regulated inflows impact on RoR ◆ Non-linear correction of inflow/power curve ◆ Robust detection of plant maintenance effects

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SLIDE 4

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing

◆ Selection of complete datasets of plant

production data

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SLIDE 5

MMP - ENTSO-E

SMHI data for Hydro database

Considered datasets: RoR plants (Italy)

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SLIDE 6

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing

◆ Selection of complete datasets of plant

production data

◆ SMHI inflow data normalization

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SLIDE 7

MMP - ENTSO-E

SMHI data for Hydro database

Considered datasets: SMHI inflows (Italy)

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SLIDE 8

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing

◆ Selection of complete datasets of plant

production data

◆ SMHI inflow data normalization ◆ Preliminary verification of the

SMHI/hydro spatial correlation

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SLIDE 9

Case study: Italy

Testing SMHI data for hydro database

Verification of the SMHI/hydro correlation

Simple sanity check for high quality RoR plant dataset with high correlation

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MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD SMHI/RoR correlation

Zone-wise aggregated RoR correlation

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SLIDE 11

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing ➔ Dimensionality reduction

◆ Proper orthogonal decomposition of

SMHI inflow data

Reduction of the input dimensionality from ~1000 to ~50-150 variables

➔ Drastic reduction of cpu requirement ➔ Avoid regression overfitting

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MMP - ENTSO-E

SMHI data for Hydro database

SVD of SMHI inflow data

Derivation a reduced set of input variables as linear combination of

  • riginal SMHI inflows
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SLIDE 13

Case study: Italy

Testing SMHI data for hydro database

Model order reduction (SVD) of SMHI data

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SLIDE 14

Case study: Italy

Testing SMHI data for hydro database

Model order reduction (SVD) of SMHI data

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Case study: Italy

Testing SMHI data for hydro database

Model order reduction (SVD) of SMHI data

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SLIDE 16

Case study: Italy

Testing SMHI data for hydro database

Model order reduction (SVD) of SMHI data

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SLIDE 17

Case study: Italy

Testing SMHI data for hydro database

Model order reduction (SVD) of SMHI data

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SLIDE 18

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation

◆ Plan by plant SMHI/hydro power

regression by least squares minimization

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SLIDE 19

MMP - ENTSO-E

SMHI data for Hydro database

Plant-by-plant SMHI/power transfer function

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SLIDE 20

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation

◆ Plan by plant SMHI/hydro power

regression by least squares minimization

◆ p-value based elimination of least

significant regressors

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SLIDE 21

MMP - ENTSO-E

SMHI data for Hydro database

p-value based regression simplification

Output example of the linear fit function, removing the least significant regressors.

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SLIDE 22

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation ➔ Post-processing

◆ Identification of spurious RoR

(influenced by regulated hydro)

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SLIDE 23

MMP - ENTSO-E

SMHI data for Hydro database

The production data has been analysed with a Fourier transform to show its spectrum.

➔ Yearly dynamics ➔ Seasonal dynamics ➔ Weekly dynamics (expected for regulated

plants, but not for “true” RoRs)

Fourier analysis

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SLIDE 24

MMP - ENTSO-E

SMHI data for Hydro database

Identification of spurious RoR

Fourier transform of hydro power production

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SLIDE 25

MMP - ENTSO-E

SMHI data for Hydro database

Identification of spurious RoR

Fourier transform of hydro power production

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SLIDE 26

MMP - ENTSO-E

SMHI data for Hydro database

Identification of spurious RoR

Fourier transform of hydro power production

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SLIDE 27

MMP - ENTSO-E

SMHI data for Hydro database

Identification of spurious RoR

Fourier transform of hydro power production

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SLIDE 28

MMP - ENTSO-E

SMHI data for Hydro database

Main steps of the statistical analysis

➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation ➔ Post-processing

◆ Identification of spurious RoR ◆ Zone-wise aggregation of RoR power ◆ Regression validation with

independent input dataset

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SLIDE 29

MMP - ENTSO-E

SMHI data for Hydro database

Part of the data is not used in the regressor training. This data is used to validate the regressors and check the presence of

  • verfitting.

Validation set

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SLIDE 30

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - SMHI/RoR correlation

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SLIDE 31

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - RoR Model verification

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SLIDE 32

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - RoR Model verification

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SLIDE 33

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - Past climatic year evaluation

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SLIDE 34

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - RoR statistics

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MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - ``Basin´´ statistics*

The subdivision in ``Basin´´ and ``Reservoir´´ plants was provided by TERNA in the input data

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SLIDE 36

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - ``Reservoir´´ statistics

The subdivision in ``Basin´´ and ``Reservoir´´ plants was provided by TERNA in the input data

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SLIDE 37

MMP - ENTSO-E

SMHI data for Hydro database

Italy - NORD - Total storage statistics

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SLIDE 38

MMP - ENTSO-E

SMHI data for Hydro database

Italy - CENTRO SUD - RoR Model verification

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SLIDE 39

MMP - ENTSO-E

SMHI data for Hydro database

Italy - RoR aggregated results

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MMP - ENTSO-E

SMHI data for Hydro database

Italy - CSUD - Total storage statistics

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SLIDE 41

MMP - ENTSO-E

SMHI data for Hydro database

Mean daily RoR power errors:

  • Italy: 0.168 GW [4.0% 3.2%]
  • NORD: 0.161 GW [4.3% 3.8%]
  • CSUD: 0.0199 GW [3.7% 2.5%]

Mean weekly Storage power errors:

  • Italy: 0.135 GW [3.0% 1.8%]
  • NORD: 0.128 GW [3.0% 2.2%]
  • CSUD: 0.0172 GW [3.0% 2.3%]

[absolute error / maximum power] [absolute error / installed power]

Italy - Mean Regression Errors

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MMP - ENTSO-E

SMHI data for Hydro database

France - Blind test

This reduced analysis on France hydro data has been performed in collaboration with RTE thanks to the invaluable support of Frédéric Bréant and Pierre Goutierre For testing purposes, automatic regression on confidential, non-disclosable RoR time series has been kindly performed by RTE adopting the algorithm code provided by Milano Multiphysics. No data or hydro plant information were provided to MMP or ENTSO-E by RTE for this test. The automatic regression code

  • nly produced graphical,

aggregated results for testing scopes.

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MMP - ENTSO-E

SMHI data for Hydro database

France - Blind test - Zone 5

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SLIDE 44

MMP - ENTSO-E

SMHI data for Hydro database

France - Blind test - Zone 7

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SLIDE 45

MMP - ENTSO-E

SMHI data for Hydro database

France - Blind test - Zone 9

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MMP - ENTSO-E

SMHI data for Hydro database

➔ SMHI inflow data proved to be a good dataset for the modeling of most RoR and Storage plants ➔ The developed modeling approach allows for the derivation of reliable and accurate transfer functions ➔ France ``blind´´ test confirms the possibility to adopt the procedure without geographical information on hydro plants ➔ Developed transfer functions can be applied to reanalysis climate data to provide synthetic hydro time series (correlated with other climatic variables) for adequacy studies

Conclusion

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MMP - ENTSO-E

SMHI data for Hydro database

Parallel activities and ongoing development

➔ Extension of the approach to produce hydro time series databases for all ENTSO-E members ➔ Adoption of derived transfer functions for short-term (week ahead) RoR hydro forecast with robust uncertainty estimates

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SLIDE 48

MMP - ENTSO-E

SMHI data for Hydro database

Parallel activities and ongoing development

➔ Extension of the approach to produce hydro time series databases for all ENTSO-E members ➔ Adoption of derived transfer functions for short-term (week ahead) RoR hydro forecast with robust uncertainty estimates

Thank you for the attention!

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SLIDE 49

MMP - ENTSO-E

SMHI data for Hydro database

Backup

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MMP - ENTSO-E

SMHI data for Hydro database

Considered datasets: RoR plants (Italy)

Hydro RoR plants with incomplete data in the 2010-2016 range are discarded to avoid spurious regressions 9 RoR power plants were discarded from the analysis due to a large fraction of unavailable data:

  • NORD: 7 discarded out of 123
  • ITALY: 9 discarded out of 163
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MMP - ENTSO-E

SMHI data for Hydro database

Normalization of SMHI input data

SMHI inflow time series were normalized to obtain zero-mean and unit standard deviation distributions:

X → X* = (X - mean(X))/std(X)

The normalization step ensures an

  • ptimal data decomposition in the

dimensionality reduction phase

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SLIDE 52

MMP - ENTSO-E

SMHI data for Hydro database

Normalization of SMHI input data

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SLIDE 53

MMP - ENTSO-E

SMHI data for Hydro database

Spain - Del Duero - SMHI/RoR correlation

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SLIDE 54

MMP - ENTSO-E

SMHI data for Hydro database

Spain - Del Duero - RoR Model verification

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SLIDE 55

MMP - ENTSO-E

SMHI data for Hydro database

Spain - Del Tajo - SMHI/RoR correlation

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SLIDE 56

MMP - ENTSO-E

SMHI data for Hydro database

Spain - Del Cantabrico - SMHI/RoR corr

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MMP - ENTSO-E

SMHI data for Hydro database

Spain - Del Cantabrico - Model verification

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MMP - ENTSO-E

SMHI data for Hydro database

Spain - Del Tajo - RoR Model verification

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SLIDE 59

MMP - ENTSO-E

SMHI data for Hydro database

Spain - RoR Model verification

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MMP - ENTSO-E

SMHI data for Hydro database

Spain - Mean Regression Errors

Mean daily RoR power errors:

  • Spain: 0.0532 GW (14.1%)
  • Del Cantabrico: 0.0118 GW (18.1%)
  • Del Duero: 0.00224 GW (19.5%)

Mean weekly RoR power errors:

  • Spain: 0.0481 GW (12.7%)
  • Del Cantabrico: 0.00991 GW (15.2%)
  • Del Duero: 0.00198 GW (17.2%)

(absolute error / average power)