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Energy production forecasting based on renewable sources of energy S. Leva Politecnico di Milano, Dipartimento di Energia Via La Masa 34, 20156 Milano,

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Presentation on theme: "Energy production forecasting based on renewable sources of energy S. Leva Politecnico di Milano, Dipartimento di Energia Via La Masa 34, 20156 Milano,"— Presentation transcript:

1 Energy production forecasting based on renewable sources of energy S. Leva Politecnico di Milano, Dipartimento di Energia Via La Masa 34, 20156 Milano, Italy sonia.leva@polimi.it, www.solartech.polimi.it

2 2 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES set up by the international energy agency 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definitions, some examples 5.The wind forecasting, some examples 6.Conclusions Goal and outline The goal of this speech is to analyze how, starting from weather forecast, we can predict in term of hourly-curve the energy production by RES for one day – two days, a week ahead The goal of this speech is to analyze how, starting from weather forecast, we can predict in term of hourly-curve the energy production by RES for one day – two days, a week ahead.

3 3 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES in the world and in Italy 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definition, some examples 5.The wind forecasting, some examples 6.Conclusions

4 4 Sonia Leva the energy production forecasting and the role of RES Introduction: the energy production forecasting and the role of RES The IEA forecasts confirm that the demand for energy (not just electricity) will grow especially in non-OECD Global energy demand rises by over one-third in the period to 2035, underpinned by rising living standards in China, India & the Middle East Share of global energy demand

5 5 Sonia Leva IEA predictions for the future (scenario "reference"): oil, gas, coal continue to dominate the energy (not just electricity) production the energy production forecasting and the role of RES Introduction: the energy production forecasting and the role of RES

6 6 Sonia Leva IEA predictions of how will be satisfied the demand of electricity in the world. «KING» COAL! the energy production forecasting and the role of RES Introduction: the energy production forecasting and the role of RES

7 7 Sonia Leva the role of RES in Italy Introduction: the role of RES in Italy In five years the electricity generation by RES in Italy has doubled. Hydro Geothermal Bioenergy Wind Solar The data are really up to date: august 2013!

8 8 Sonia Leva the role of RES in Italy Introduction: the role of RES in Italy Number of plants producing electricity passes in a decade from 1 thousand to 550,000 Centralized system tends towards a mixed system of generation (distributed generation) A growing number of households and factories now are involved in electricity generation Electricity generation in Italy in the first seven monthes of 2013 Thermoelectric fossil Hydro geothermal Bioenergy wind PV

9 9 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES in the world and in Italy 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definition, some examples 5.The wind forecasting, some examples 6.Conclusion

10 10 Sonia Leva The energy forecasting from RES Distributed system: grid-connected RES installations are decentralized RESs energy production has a stochastic behavior. RESs are much smaller than traditional utility generators Today's available transformation and storage capabilities for electric energy are limited and cost-intensive. ability to forecast energy production Challenges of controlling and maintaining energy from inherently intermittet sources involves many aspects: efficicency, reliability, safety, stability of the grid and ability to forecast energy production.

11 11 Sonia Leva Forecasting of PV/wind is an estimation from expected power production of the plant in the future. For monitoring and maintenance purposes To help the grid operators to better manage the electric balance between power demand and supply and to improve embedding of distributed renewable energy sources. In stand alone hybrid systems energy forecasting can help to size all the components and to improve the reliability of the isolated systems. The energy forecasting from RES

12 12 Sonia Leva Time horizon RangeApplications Very short-term Few seconds to 30minutes ahead - Control and adjustment actions Short-term 30 minutes to 6 hours ahead - Economic Dispatch Planning - Load Increment/Decrement Decisions Medium-term 6 hours to 1 day ahead - Generator Online/Offline Decisions - Operational Security in Day-Ahead - Electricity Market Long-term 1 day to 1 week or more ahead - Unit Commitment Decisions - Reserve Requirement Decisions - Maintenance Scheduling to Obtain Optimal Operating Cost Time scale classification for RES Forecasting

13 13 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES in the world and in Italy 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definition, some examples 5.The wind forecasting, some examples 6.Conclusion

14 14 Sonia Leva Weather forecast This is an orthogonal step to a grid operator: weather data is usually obtained from meteorological services. The most influencing factor for output determination are: solar energy production: global irradiation forecast. wind energy production: wind speed amplitude and direction forecast, pressure forecast The use of precise weather forecast models is essential before reliable energy output models can be generated. Forecasts of RES production is based on weather forecasts.

15 15 Sonia Leva Weather forecast models Numerical Weather Prediction (NWP) Complex global NWP models are used to predict a number of variables describing the dynamic of the atmosphere and then to derive the weather at a specific point of interest. Post processing techniques are applied to obtain down scaled models (1.5 km). European Center for Medium-Range Weather-Forecasts Model (ECMWF) Global Forecast System (GFS), North American Mesoscale Model (NAM) 3-6 hors Cloud Imagery Influence of local cloudiness is considered to be the most critical factor for estimation of solar irradiation. The use of satellite provide high-quality medium term forecast. Satellite-based (METEOSAT), Total Sky Imager, 24h-48h Statistical Methods based on historical observation data using time series regression models ARIMA Articial Neural Networks (ANN), Fuzzy Logic (FL), ARMA/TDNN ANFIS RBFNN MLP Long term

16 16 Sonia Leva Time horizon is a crucial aspect. Sunshine and wind speed can only be predicted with accuracy a few days in advance. The number and type of variables describing the physics and dynamic of the atmosphere are fundamental topics. Cloudy index or irradiation are two indexes that can impact on the forecast in a different way. Meteorology remains a field of uncertainty. Weather forecast

17 17 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES in the world and in Italy 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definition, some examples 5.The wind forecasting, some examples 6.Conclusion

18 18 Sonia Leva Models. The PV forecast: different Models. Physical Models to describe the relation between environmental data and power -highly sensitive to the weather prediction -have to be designed specifically for a particular energy system and location Statistical Models are based on persistent prediction or on the time series' history Persistent prediction, Similar-days Model Stochastic Time Series Machine Learning Artificial neural network (ANN) learn to recognize patterns in data using training data sets. They need historical weather forecasting data and PV-plant measured data for their training Hybrid Models are any combination of two or more of the previously described methods. They could be two different stochastic models or a stochastic model and a physical model.

19 19 Sonia Leva Physical Models. The PV forecast: Physical Models. PhysicalAlgorithm Plant Description; Monitoring System PV energy forecast Weather forecast Global Irradiation, Cloud cover, Temperature, ecc Measured data PV energy forecast

20 20 Sonia Leva Statistical Models The PV forecast: Statistical Models TRAINED NEURAL NETWORK Environmental temperature

21 21 Sonia Leva Error Definitions In order to correctly define the accuracy of the prediction and the relative error it is necessary to analyze different definitions of error. The starting point reference is the hourly error e h : P m,h is the average power produced in the hour (or energy kWh) P p,h is the prediction provided by the forecasting model From this basic definition, other error definitions have been inquired: Absolute error based on the hourly output expected power (p=predicted) [AEEG]: absolute error based on the hourly output produced power (m=measured) [AEEG]: AEEG=Italian Authority for Electricity and Gas

22 22 Sonia Leva Error Definitions Mean absolute error [AEEG et al]: Normalized mean absolute error NMAE, based on net capacity of the plant C [AEEG et al] C could be the rated power, the maximum observed or expected power!!!!

23 23 Sonia Leva Error definitions Weighted mean absolute error WMAE % based on total energy production [AEEG et al.]: Normalized root mean square nRMSE, based on the maximum observed power [Urlicht et al]: 23

24 24 Sonia Leva weather forecasts data analysis: evaluation of their reliability. Comparison between ANN forecasts and other methods Ensembled methods Plant data validation: Theoretical Solar Irradiance (clear sky) Error definitions Accuracy assessment of the obtained results Hybrid Models (ANN+Physical) Some examples: Hybrid Models (ANN+Physical) Physical data: Theoretical Solar Irradiance (clear sky), Sunrise-, Sunset-hour weather forecasts

25 25 Sonia Leva A. Hybrid Models (ANN+Physical) at SolarTech Lab TRAINED NEURAL NETWORK Clear Sky Physical Model Environmental temperature 4.4kW, Milano, Italy

26 26 Sonia Leva NMAEp%= 3.08% NMAEp% = 30.1% A. Some Results: Solar Tech Lab pink line: there was an error in the weather forecast.

27 27 Sonia Leva 285kW PV plant, Cuneo (Italy) Meteo dataset: Day, hour, Environmental temperature, wind direction, wind speed, global solar irradiation Goals: Analysis of the error due to the weather forecasting Ensembles method: use more than one trials of stochastic methods to make the forecast Absolute hourly error based on predicted power vs measured power B. Hybrid Models (ANN+Physical) PV Plant in Cuneo

28 28 Sonia Leva B. Hybrid Models (ANN+Physical) PV Plant in Cuneo Error due to the weather forecasting: difference between the irradiation given by weather service and the irradiation measured

29 29 Sonia Leva Solar Radiation forecastings are affected by a great error! B. Hybrid Models (ANN+Physical) PV Plant in Cuneo Error due to the weather forecasting: Absolute hourly errors of GI are sorted from largest to smallest. Absolute hourly error based on expected global irradiation (predicted) and on the measured global irradiation.

30 30 Sonia Leva Some Results: Power Plant ANN are stochastic methods: Different trials give different forecasting curves. Ensemble: power/energy forecast is calculated considering the hourly average value of different (here 10) trials. Absolute hourly error based on expected output power (predicted) and on the measured output power. Hourly sample (from sunrise to sunset) Ensemble methods reduce the error! The error based on the measured power is bigger than the one based on the predicted!

31 31 Sonia Leva Some Results: Power Plant NMAEp% = 10 NMAEr% = 5.86 WMAEp% = 16.58 NMAEp% = 10 NMAEr% = 5.86 WMAEp% = 16.58 NMAEp% = 29.14 NMAEr% = 15 WMAEp% = 50 NMAEp% = 29.14 NMAEr% = 15 WMAEp% = 50 NMAEp% = 16 NMAEr% = 7.33 WMAEp% = 28.7 NMAEp% = 16 NMAEr% = 7.33 WMAEp% = 28.7

32 32 Sonia Leva Some Results: Power Plant expected output power (predicted) and versus measured output power. 1 year: NMAEp = 12.15%, NMAEr%=7,34%

33 33 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES in the world and in Italy 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definition, some examples 5.The wind forecasting, some examples 6.Conclusion

34 34 Sonia Leva Wind Forecasting Forecasting of wind is an estimation from expected power production of the wind turbines in the future. This power production is expressed in kW or MW depending on the nominal capacity of the wind farm. Forecasting methods described for PV can be applied Error definitions described for PV are used Kalman or Kolmogorov-Zurbenko are usually adopted to better extimate the wind speed eliminating the effects of noise and systematic errors Hybrid approaches (ANN + CFD computational fluid dynamics software) can improve the accuracy of the forecasting 34

35 35 Sonia Leva Input parameters: Inviromental temperature [°C] Atmospheric pressure [hPa] Wind speed intensity [m/s] Humidity [%] Cloud coverage [%] Performance parameters WMAE NMAE Implemented feed-forward ANN with details on input, output, and hidden layers. Example: Castiglione Messer Marino Wind Farm

36 36 Sonia Leva Some Results: Castiglione Messer Marino Wind Farm 1000 iterations: NMAEp = 40.2 % NMAEr= 14%

37 37 Sonia Leva The use of tools of CFD (computational fluid dynamics software) can improve the predictive capability of forecasting systems. The computational cost greatly limits its practical applicability for wind farms with a large number of wind turbines. Expensive measurement systems (see anemometer towers) to model the field. Hybrid methods: computational fluid dynamics software

38 38 Sonia Leva The most promising method: Hybrid methods ANN Physical algorithm CFD Analysis Historical Wind data Historical Power data Ground description Plant Description by GSE, ANEMOS.plus

39 39 Sonia Leva 1.Introduction: the energy production forecasting and the role of RES in the world and in Italy 2.The energy forecasting from RES 3.Weather forecasting 4.The PV forecasting and error definition, some examples 5.The wind forecasting, some examples 6.Conclusions

40 40 Sonia Leva Conclusions The meteorological services have an important influence on the power forecasting system for PV and wind energy. The input data analysis is very important and cost-intensive Hybrid forecasting method are the most promising methods both for PV and Wind energy forecasting PV. Clear sky data are very useful to reduce error. Wind. The use of special filters (eg Kalman or KZ) may be useful for the removal of systematic errors of the forecasts of wind speed provided by the NWP and used as input to statistical methods. The performance of the forecasting models are strongly related to the time horizon of the forecast and to the characteristics of the land on which the plant/farm is placed. The need for energy forecasting from RES is a recent topic!!!

41 41 Sonia Leva www.solartech.polimi.it Diapartimento di Energia Via Lambruschini, 4 20133 Milano e-mail: sonia.leva@polimi.itsonia.leva@polimi.it e-mail: giampaolo.manzolini@polimi.itgiampaolo.manzolini@polimi.it Tel. +39 02 2399 3800 (Centralino) 3709 (Leva) – 3810 (Manzolini) THANK YOU!

42 42 Sonia Leva Some Results: Power Plant Absolute hourly error based on expected output power (predicted) and on the measured output power.


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