Short-term forecasting in the context of smart grids€¦ · Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation

Short-term forecasting in the context of smart grids

Georges Kariniotakis Prof., HdR, Head of Renewable Energies & Smart Grids Group MINES ParisTech, Centre PERSEE [email protected]

Thematic Semester of Statistics for Energy Markets Workshop #1. Forecasting for Renewable Energy Production EDF Lab, Saclay, 02 February 2018

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Context

• This translates to:

• Wind: 251-392 GW of installed capacity (in 2016: 154 GW). • Solar PV: 230-367 GW (in 2016: 101 GW)

• Ambitious targets for the integration of renewables in EU by 2030

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• Transition towards a more and more “weather-dependent” power system.

Challenges

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• The development of smart grid technologies aims among others at facilitating renewables (RES) integration

• Some new challenges: • Need for cost-efficient hedging over increased uncertainties (i.e.

storage) • A high share of demand should become active to ‘fit’ generation • RES power plants should be able to provide flexibilities (i.e.

ancillary services) in a similar way as conventional plants • Appropriate market schemes should be developed to trade

flexibilities at different spatial scales • The usage of the electrical grid should be improved (i.e. through

dynamic line rating) • …

Challenges

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Predictive management and forecasts

• To address these challenges, it is needed to develop appropriate tools for predictive management: • for different system configurations/spatial scales:

• house/customer > microgrid/feeder > regional/national • for different actors:

• aggregators, producers, retailers, TSOs, DSOs… • for different functions:

Source Figure: Hong et Fan (2016), “Probabilistic electric load forecasting: a tutorial review”, Journal of Forecasting

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Predictive management and forecasts

• All these functions require different types of short term (few minutes – few days ahead) forecasts for: • Renewable generation (PV, wind, run-of-the-river hydro…) • Electricity & heat demand • Demand/generation flexibility potential • Electricity prices • Dynamic line rating • …

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More and more data… Plus and minus

• Smart grid: Available information increases

o Is this an opportunity for reducing uncertainties through improvement of forecasting capabilities?

• Local demand: Emerging approaches based on smart meter data, consumers presence information etc.

• New usages like electric cars: Big data and predictive analytics • Numerical Weather predictions: Improvements thanks to

increasing computing capacities • Wind and PV forecasting: improvements through the use of

new sources of data (i.e. spatio-temporal approaches) • Availability of open data • ….

+

+

+

+

+

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More and more data... Plus and minus

• High uncertainties in RES production and demand at local level that cannot necessarily be explained by more data

• Large errors in RES forecasting come from Numerical Weather Predictions. Necessity to go back to the fundamentals…

• Smart meters deployment is often suboptimal w.r.t. forecasting requirements in smart grids (i.e. data delivery once per day).

• Curse of dimensionality is a limit for classical forecasting techniques

• Need for adapted models able to handle large amounts of inputs without overfitting

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-

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• Smart grid: Available information increases

o Is this an opportunity for reducing uncertainties through improvement of forecasting capabilities?

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Some more challenges…

• Use of forecasts in applications: • Extensive research on probabilistic approaches for various power

system functions. • Use of different types of forecasts (pdfs, scenarios, quantiles…) • Various optimization techniques (i.e. stochastic optimization based on

scenarios, robust optimization based on intervals…).

• “Mismatch” between research and end-users’ business practice: decision making tools at end-users are still mainly deterministic

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Some more challenges…

• Use of forecasts in applications: • Predictability as a decision factor in the investment phase of RES

plants and storage devices. • Dimensioning of storage – temporal correlation of errors • Long term estimation of revenue from trading (evolution of electric. prices)

• Power system expansion, integration studies (i.e. 100% RES) • Multi-annual simulation of RES production and forecast errors.

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State of the art in RES forecasting

Wind power forecasting - State of the art

1990 2002

"Deterministic" (spot) approaches

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Statistical/time-series approaches

Artificial intelligence

Physical modelling

Empiric/hybrid implementations into operational forecast tool

Projects: Anemos (FP5), www.anemos-plus.eu (FP6), www.safewind.eu (FP7)

1990

Probabilistic view

2002 Anemos

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Mapping of state of the art

1st benchmarking (Anemos competition)

Physical modelling

Statistical models, AI, Data mining,…

Combination of models

First probabilistic approaches/ensembles

Upscaling

Evaluation standardisation/protocol

International collaboration


Projects: Anemos (FP5), www.anemos-plus.eu (FP6), www.safewind.eu (FP7)

1990


Probabilistic view

2002 Anemos 2008 Anemos.plus/SafeWind

New generation of tools

Diversified predicted

information

Portfolio of products

THE STATE OF THE ART

14 Projects: Anemos (FP5), www.anemos-plus.eu (FP6), www.safewind.eu (FP7)



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Alternative forecasting products

Risk indices

Probabilistic Forecasting

Ramps forecasting

Scenarios, Ensembles

Variability/texture predictions Predictability maps

Spatio temporal methods

Alarming/warning tools for large errors

Weather patterns

analysis

New generation of tools

Probabilistic view

1990


2002 Anemos 2008 Anemos.plus/SafeWind

On going R&D

?

2018


16 Projects: Anemos, Anemos.plus, SafeWind: +250 papers in journals and conferences


R&D in wind power forecasting

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Very active research on: • probabilistic methods, flexibility forecasting • use of remote sensing measurements • spatiotemporal forecasting, • ensembles, • optimal use of forecasts • …

Dedicated IEA Wind Task 36

R&D in solar forecasting

Very active research in the last years:

• Spatiotemporal forecasting for the very short-term (0-6h)

• Use of satellite images (0-6h)

• Use of sky images by cameras (0-1h)

• Probabilistic forecasting, Flexibility forecasts

• Combined PV & Wind

• Demonstrations (i.e. NICE GRID, SENSIBLE…)

• Optimal use of forecasts

…

Source: Solar Training 2016, OIE- Transvalor

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R&D highlights @ MINES ParisTech/PERSEE

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Spatio-temporal wind power forecasting

• Improvement of short-term predictability (0-3h) of wind production using off-site data. Case of Denmark

RMSE Improvement of spatio-temporal model over reference

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Spatio-temporal PV forecasting • Improvement of short-term predictability (0-6h) of PV

production using off-site data, NWPs & satellite images

Source: PhD G. Agoua, MINES ParisTech/PERSEE

• Pixel: ~5 km • Updates: 15 min

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Spatio-temp model

ST: Off-site PV measurements

SAT: Satellite images

NWPs: Weather forecasts

Z: Wheather measurements


Reference AR model

AR: On site PVmeasurements

vs

• Around 1330 available explanatory variables for each site.

• ~60 selected trough Lasso technique. • Autoregressive type of model

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Horizon ( x 15 min)

Improvement RMSE (%Pmax)

ST vs AR ST(Z) vs AR

ST + SAT vs AR ST+NWP vs AR

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Spatio-temp model

ST: Off-site PV measurements

SAT: Satellite images

NWPs: Weather forecasts

Z: Wheather measurements


Reference AR model

AR: On site PVmeasurements

vs

Aggregated wind & PV forecasting

24 Source: PhD S. Camal, MINES ParisTech

• Probabilistic forecasts for the provision of system service offers by a Virtual Power Plant (VPP).

• Demonstration with a 100 MW VPP (FR, DE, PT)

Deterministic Price Forecast, Reserve: Optimal Quantile

Deterministic Price Forecast, Reserve: 1% quantile

Perfect Price knowledge, Reserve: Optimal Quantile

+8% +14%

+30%

Virtual Power

Plant

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• Motivation: Why forecast demand for local scale? o Input in HEMS (Smart Home Energy Management Systems) o For flexibily offers by aggregators o For microgrids management o …

• Objective: Probabilistic forecasts o pdfs, quantiles, scenarios o Backup models for case of problematic input

• Model input:

o Electricity demand of previous day and previous week o Temperature forecast o Hour of the day

Household demand forecasting

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• Test-case: 226 households at Evora (PT) o Demonstration project SENSIBLE o Input: smart meter data (15 min) and NWPs o Use case: aggregator offering flexibility to the day-ahead market

Google Maps

Test case: 226 households

EVORA


Source: PhD A. Gerossier, MINES ParisTech

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• Test-case: 226 households at Evora (PT) o Demonstration project SENSIBLE o Input: smart meter data (15 min) and NWPs o Use case: aggregator offering flexibility to the day-ahead market

Example of forecasts for a household. Average performance is 29% for houses

with good quality data or 34% otherwise

— Measure. — Forecast — Pred. interval 30–70% — Interval 10–90%



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• Smaller error when predicting aggregated demand (high average power) o High error (~30%) for single house demand prediction o Aggregating demand decreases prediction error (~5%) o Optimal size for aggregation: 100kW (saturation point).

Comparison with test case

at USA with 175 houses:

• 66% with PV panels

• 30% with elec. vehicles



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• Scenario prediction: Co-prediction of demand, PV production & EV charging cycles (model: gradient boosting trees, Input: measures+NWPs)

• Case at US: 175 houses 66% with PV panels, 30% with EVs



Dynamic Line Rating forecasting

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• The maximal current admissible through a line, is usually set according to some restrictive hypothesis on weather conditions

• Depending on weather characteristics (i.e. low wind, high temperature), a high current may imply a dangerous situation, the line being strongly deformed.

• However, with the same high current but under favourable weather characteristics, the cooling being more important, such deformation may not be observed. -> set a Dynamic Line Rating depending on actual or expected weather characteristics

Source: PhD R. Dupin, MINES ParisTech

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Example of the evolution of DLR and SR for a 110kV line.


• Comparison of Static Line Rating and Real-Time Line Rating


• Forecasting the DLR may have several benefits : o setting of the future Available Transfer Capacities.

• Such forecasts should have high « reliability ». o The frequency of events where the DLR set points are superior to

observations should be low (~1%-2%).

o For some applications, the models should be able to provide reliable forecasts for extreme levels of probability (~0.1%-1%).

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Models using machine learning

methods:

Kernel Density Estimator, Quantile

Regression forest, Gradient Boosting

Trees, etc.

• Exponential interpolation + clustering

• Extreme Value Theory

Machine learning methods

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• Traditional machine learning methods provide good low quantile forecasts for level of probability superior to 1% (yellow).

• For levels of probability inferior (red), tools like the Extreme Value Theory can be used.

Tail modeling



• The actual RES forecasting technology is quite mature (~1985 - ….).

• However, still in some situations large forecast errors may have an important impact on power system operation

o R&D objectives: Improve RES predictability (wind, solar) at different temporal (i.e. 5 min – 10 days) and spatial scales (local, regional,…)

o Challenge: Complexity of the considered phenomena at different temporal and spatial scales.

• Forecasting of local electricity consumption & heat demand is an emerging R&D topic thanks to the availability of data from smart meters etc.

• Tendency towards techniques able to handle efficiently large amount of data (not necessirily «big data») that become available.

• However, the aim is large-scale application and a trade-off should be found between accuracy, plug&play capabilities, model chain complexity…

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Conclusions

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Thank you for your attention

First book on RES forecasting by

ELSEVIER/WP – published June 2017.

Acknowledgements:

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Some recent papers • Andrea Michiorri, Jesus Lugaro, Nils Siebert, Robin Girard, Georges Kariniotakis. Storage sizing for grid connected hybrid

wind and storage power plants taking into account forecast errors autocorrelation. Renewable Energy, Elsevier, 2018, 117, pp.380-392. 〈10.1016/j.renene.2017.10.070〉. 〈hal-01626067〉

• Fei Teng, Romain Dupin, Andrea Michiorri, Georges Kariniotakis, Yanfei Chen, et al.. Understanding the Benefits of Dynamic Line Rating under Multiple Sources of Uncertainty. IEEE Transactions on Power Systems, Institute of Electrical and Electronics Engineers, 2017, This article has been accepted for publication in a future issue of this journal, but has not been fully edited. 〈10.1109/TPWRS.2017.2786470〉. 〈hal-01686328〉

• Ricardo J. Bessa, Corinna Möhrlen, Vanessa Fundel, Malte Siefert, Jethro Browell, et al.. Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry. Energies, MDPI, 2017, 10 (9), pp.1402. 〈10.3390/en10091402〉. 〈hal-01589969〉

• Alexis Gerossier, Robin Girard, Georges Kariniotakis, Andrea Michiorri. Probabilistic Day-Ahead Forecasting of Household Electricity Demand. CIRED 2017 - 24th International Conference on Electricity Distribution, Jun 2017, Glasgow, United Kingdom. pp.0625, 2017. 〈hal-01518373〉

• Simon Camal, Andrea Michiorri, Georges Kariniotakis, Andreas Liebelt. Short-term Forecast of Automatic Frequency Restoration Reserve from a Renewable Energy Based Virtual Power Plant. The 7th IEEE International Conference on Innovative Smart Grid Technologies - ISGT Europe 2017, Sep 2017, Torino, Italy. 2017. 〈hal-01615232〉

• Xwégnon Ghislain Agoua, Robin Girard, Georges Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy , IEEE, 2017, 9 p. 〈10.1109/TSTE.2017.2747765〉. 〈hal-01581946〉

http://dx.doi.org/10.1016/j.renene.2017.10.070



https://hal-mines-paristech.archives-ouvertes.fr/hal-01626067





http://dx.doi.org/10.1109/TPWRS.2017.2786470








http://dx.doi.org/10.3390/en10091402

http://dx.doi.org/10.3390/en10091402

http://dx.doi.org/10.3390/en10091402






https://hal.archives-ouvertes.fr/hal-01518373










http://dx.doi.org/10.1109/TSTE.2017.2747765








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Some recent papers • Romain Dupin, Andrea Michiorri, Georges Kariniotakis. Dynamic line rating day-ahead forecasts - cost benefit based

selection of the optimal quantile. CIRED 2016 workshop - Electrical networks for society and people , Jun 2016, Helsinki, Finland. 2016. 〈hal-01398440〉

• Alexis Bocquet, Andrea Michiorri, Arthur Bossavy, Robin Girard, Georges Kariniotakis. Assessment of probabilistic PV production forecasts performance in an operational context. 6th Solar Integration Workshop - International Workshop on Integration of Solar Power into Power Systems, Nov 2016, Vienna, Austria. Energynautics GmbH, pp.6 - ISBN 978-3-9816549-3-6, 2016, Proceedings 6th Solar Integration Workshop. 〈hal-01409042〉

• Arthur Bossavy, Robin Girard, Georges Kariniotakis. An edge model for the evaluation of wind power ramps characterization approaches. Wind Energy, Wiley, 2015, 18 (7), pp.1169-1184. 〈10.1002/we.1753〉. 〈hal-01108808〉

• Simone Sperati, Stefano Alessandrini, Pierre Pinson, Georges Kariniotakis. The " Weather Intelligence for Renewable Energies " Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation. Energies, MDPI, 2015, 8 (9), pp.9594-9619. 〈10.3390/en8099594〉.〈hal-01199212〉

• Arthur Bossavy, Robin Girard, Georges Kariniotakis. Forecasting ramps of wind power production with numerical weather prediction ensembles. Wind Energy, Wiley, 2013, 16 (1), pp.51-63. 〈10.1002/we.526〉. 〈hal-00682772〉

• Robin Girard, K. Laquaine, Georges Kariniotakis. Assessment of wind power predictability as a decision factor in the investment phase of wind farms. Applied Energy, Elsevier, 2013, 101, pp.609-617. 〈10.1016/j.apenergy.2012.06.064〉. 〈hal-00734082〉











http://dx.doi.org/10.1002/we.1753








http://dx.doi.org/10.3390/en8099594
















http://dx.doi.org/10.1016/j.apenergy.2012.06.064








• Founded in 1783

• 2395 staff • 1,114 permanent staff including 286 research academics

• 391 PhD students (100/y), 890 other students

• 18 research centers

• 30 Mi€ per year contractual research budget (ARMINES)

• 1st engineering school in France in contractual volume research

• 5 sites: Paris, Évry, Fontainebleau, Palaiseau, Sophia Antipolis.

www.mines-paristech.fr 38

Two centuries of learning

MINES ParisTech

MINES ParisTech @ Sophia Antipolis

39

Centre PERSEE Centre for Processes,

Renewable Energies and Energy Systems

www.persee.mines-paristech.fr

MINES ParisTech > Centre PERSEE

Energy transition

40

Mobility Demand

Storage

Sustainable

fuels RES

Grids

Mat

eri

als

Materials for energy

Inte

grat

ion

Renewable energies & smart grids

Pro

cess

es

Sustainable technologies & processes



41

Forecasting Multi energy

hybrid systems Smart grids

• Wind

• Solar

• DLR

• Demand

• …

Dimensioning/ design

Optimal operation and

management

Modelling/simulation

Predictive management

Long-term planning

RES market integration

Research axis « Renewable energies & smartgrids »: Development of

methods and tools to facilitate the integration of distributed generation and

renewable energies (RES) into power systems and electricity markets.

Ob

jective

3 r

ese

arc

h th

em

es

http://www.persee.mines-paristech.fr

On-going PhDs in our group

42

Ghislain AGOUA (3a)

Romain DUPIN (3a)

Alexis GEROSSIER (2a)

Thomas CARRIERE (2a)

Simon CAMAL (2a)

Adrian CORREA (2a)

Spatio-temporal

methods for

short term

PV forecasting

Forecasting of

dynamic line rating

and impacts on

power system

management

Short-term

forecasting of local

electric

consumption

Capacity forecast of

ancillary services offered by

renewable energy plants

Technical & economical

optimization of the coupling

between a PV plant and

storage towards its

valorization on the electricity

markets at the 2020 horizon

Predictive management of

storage devices in the smart

grids context

…prosumers, ancillary services, virtual power plant, auto-consumption, home energy management system

On-going PhDs in our group

43

Thibaut BARBIER (3a)

Pacco BAILLY (1a)

Etta GROVER-SILVA (3a)

Antoine ROGEAU (2a)

Optimal integration of

renewable energy in

smart distribution grids

Electricity demand modeling

using large scale databases to

simulate different prospective

scenarios

Multi-scale modelling

approach for the

management of future power

systems

Methods for decision makers to

enable the energy transition

at a local/regional scale

…planning, demand & RES long term scenarios , electricity markets, storage placement, stochastic OPF…

Alberto VÁZQUEZ RODRÍGUEZ (1a)

Thomas HEGGARTY (1a)

Optimal techno-

economic mix of

power system

flexibility solutions

Modelling &

technico-economic

optimization of operation

strategies of storage

systems with cycling

constraints

Documents

Short-term forecasting in the context of smart grids€¦ · Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation