Presentation is loading. Please wait.

Presentation is loading. Please wait.

Energy production forecasting based on renewable sources of energy

Similar presentations


Presentation on theme: "Energy production forecasting based on renewable sources of energy"— Presentation transcript:

1 Energy production forecasting based on renewable sources of energy
Good afternoon ladies and gentlemen. Thank you for inviting me at this conference. This is a privilege and also a pleasure. I would like to thank Prof. Miguel Tufino and Rolando Jimenetz for this opportunity and their kindness. S. Leva Politecnico di Milano, Dipartimento di Energia Via La Masa 34, Milano, Italy

2 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. Introduction: the energy production forecasting and the role of RES set up by the international energy agency The energy forecasting from RES Weather forecasting The PV forecasting and error definitions, some examples The wind forecasting, some examples Conclusions The goal of this speech is analyzing how, starting from meteo forecast, we can predict in term of hourly-curve the energy production by RES for one day – two days, a week ahead. Here you can find the outline of this presentation. First of all I will introduce same data about the energy production forecasting and the role of RES following the international energy agency scenario. Then the role of res in Italy well be presented. This can allow us to understand how the grid is changing and the importance of forecasting the energy produced by unpredictable sourses like PV and Wind. Speacking about forecasting we’ll start on “weather forcasting”. Then, the core of this presentation, PV forecasting. In particolar a brief review of the forcasting models communly used and of same error definitions will be presented. Furthermore some examples coming from real plants in Italy will show underlying the positive results but also the limits of the RES energy forecast. Then few minutes about wind forecasting. Finally some conclusions.

3 Introduction: the energy production forecasting and the role of RES in the world and in Italy
The energy forecasting from RES Weather forecasting The PV forecasting and error definition, some examples The wind forecasting, some examples Conclusions

4 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 Share of global energy demand The International Energy agency confirms that the demand for energy in the next years will grow especially in non-OECD countries. Global energy demand resis will be mainly due to the rise of living standars in China, India and Middle east countries. Global energy demand rises by over one-third in the period to 2035, underpinned by rising living standards in China, India & the Middle East

5 Introduction: the energy production forecasting and the role of RES
IEA predictions for the future (scenario "reference"): oil, gas, coal continue to dominate the energy (not just electricity) production Meeting the global demand for energy continue to be the key challenge to sustained industrialization. IEA prediction for the energy production confirms oil, gas and coal continue to dominate. We are speacking about energy production at all not just about electricity.

6 Introduction: the energy production forecasting and the role of RES
IEA predictions of how will be satisfied the demand of electricity in the world. «KING» COAL! And what about electricity? RES (wind and PV) will continue to have the largest growths in percentage. Nevertheless gas and coal will is still going to grow. It seem that Coal should continue to be the king of electricity production. Considering the IEA prediction of how will satisfied the demand of electricity in the world we can see that renewables (wind, PV) will continue to have the largest growths in percentage but the energy production from gas and coal will is still going to grow. Coal should continue to be the king of energy production.

7 Introduction: the role of RES in Italy
In five years the electricity generation by RES in Italy has doubled. Hydro Geothermal Bioenergy Wind Solar Now some words about the situation of Italy. Italy can be an interesting example of how the electricity generations is changing and how the electric system is moving to distributed system giving us new challenges. In Italy RES have experienced one of the largest growths: in the last five years the electricity generation by res is doubled. Thanks in particularly to PV and wind pushed by public policy (feed in tariff). Instead the production of energy by hydro has not changed at all. The data are really up to date: august 2013!

8 Introduction: the role of RES in Italy
Electricity generation in Italy in the first seven monthes of 2013 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 Bioenergy geothermal wind PV Thermoelectric fossil Hydro Furthermore, still speaking about Italy, we can see that wind+bio+PV production of energy is equal to the one produced by idro. This involves many aspects: 1. The number of plants producing electricity passes in a decade from 1 thousand to 550,000 2. The centralized system of generation tends towards a mixed system of generation: the usually called distributed generation or dispersed generation when the generation systems are very small in term of rated power. 3. A growing number of households and factories now are involved in electricity generation.

9 Introduction: the energy production forecasting and the role of RES in the world and in Italy
The energy forecasting from RES Weather forecasting The PV forecasting and error definition, some examples The wind forecasting, some examples Conclusion

10 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. In centralised system the produced energy is relatively independent on the plant location. 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 The energy forecasting from RES
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.

12 Time scale classification for RES Forecasting
horizon Range Applications 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 This table shows a possible time scale classification for forecasting of RES. This table has been set up with reference to wind but it can be also applied with reference to PV. For PV systems very short-term horizon it is not so important. Instead for wind systems and in particular farm it is really important. For the utility two applications are very important: the scheduling of power plants and the market. The typical time scales for start-up of conventonal power plant are between 20 min (for gas turbine) and 8 hours or more (for large coal or oil plants) long-term forecast Usually for electricity market you need medium term forecast: you have to sell today the energy for tomorrow.

13 Introduction: the energy production forecasting and the role of RES in the world and in Italy
The energy forecasting from RES Weather forecasting The PV forecasting and error definition, some examples The wind forecasting, some examples Conclusion

14 Forecasts of RES production is based on weather forecasts.
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. In order to make energy supply planning rational, forecasts of RES production have to be made based on the consideration of weather conditions. Although this step is orthogonal to a grid operators core activities (weather data is usually obtained from meteorological services), a basic understanding of the underlying principles is helpful when choosing a specific energy output model.

15 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 Meteorology remains a field of uncertainty.
Weather forecast Meteorology remains a field of uncertainty. 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.

17 The energy forecasting from RES Weather forecasting
Introduction: the energy production forecasting and the role of RES in the world and in Italy The energy forecasting from RES Weather forecasting The PV forecasting and error definition, some examples The wind forecasting, some examples Conclusion Any output from the weather models described above must then be converted into electric energy output. According to the underlying methodology, the existing solutions can be itemized into the categories of physical, statistical and hybrid methods.

18 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 Any combination of two or more of the previously described methods is a hybrid model. The idea is not new: we combine different models with unique features to overcome the single negative performance and finally improve the forecast. We can combine two different stochastic models or a stochastic model with a physical model. 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 The PV forecast: Physical Models.
Weather forecast PV energy forecast PV energy forecast Physical Algorithm Global Irradiation, Cloud cover, Temperature, ecc Plant Description; Monitoring System Measured data The usage of such models requires detailed technical knowledge about characteristics and parameters of all underlying components, thus making them more relevant for energy plant owners or producers than for grid operators We can improve the model adding temperature, or other meteorological parameters.

20 The PV forecast: Statistical Models
TRAINED NEURAL NETWORK Environmental temperature ANN are black box methods but they can consider many atmospherical paramiters to perform the energy forecast.

21 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 eh: Pm,h is the average power produced in the hour (or energy kWh) Pp,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]: Some of the error definitions used in energy forecasting come from maths (statistics) theory. Others are introduced by regulatory authority for market issues (in Italy AEEG=Autorità energia elettrica e gas. Authority for Electricity and Gas) The error definitions are really different among them and it is not easy to chose one of these. Also the technical papers present a lot of this indexes. Here we report some of the most important error definitions. AEEG=Italian Authority for Electricity and Gas

22 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!!!! Look at C (net capacity of the plant): it could be the rated power, the maximun observed power or the expected power!!!! It depends on the Author of the papers!

23 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]:

24 Some examples: Hybrid Models (ANN+Physical)
Physical data: Theoretical Solar Irradiance (clear sky), Sunrise-, Sunset-hour weather forecasts Plant data validation: Theoretical Solar Irradiance (clear sky) Speaking about hybrid model I’m going to present and discuss a model developed at the Department of Energy of the Politecnico di Milano. My research group has made in the last three years great efforts for developing models that can improve the forecast and that can done the energy forecast usefull for the grid and the electricity market. Also a big work has been done about the error analysis. In fact these topics are really important for the application of the power forecasting both on the technical and economic point of views. This forecasting method for PV plants based on Neural Network can be described by the three following steps: Input data: the historical data (for training process) are first of all analyzed verifying their consistence on the point of view of physical behavior of the system. In particular the irradiance is compared with the clear sky theoretical model with the aim to individuate sunset sunrise the maximum irradiation. Furthermore not useful data are erased. The ann process: the neural network has to be trained by using historical data set. Then starting from weather forecasting data and from clear sky data set the power forecasting for the day X+1 is performed. Analysis of the error: taking into account the error definition some evaluation can be made. Usually an assessment of the obtained results and a comparison between different methods. weather forecasts data analysis: evaluation of their reliability. Comparison between ANN forecasts and other methods Ensembled methods Error definitions Accuracy assessment of the obtained results

25 A. Hybrid Models (ANN+Physical) at SolarTech Lab
TRAINED NEURAL NETWORK Environmental temperature Clear Sky Physical Model 4.4kW, Milano, Italy Meteo dataset: Day, hour, Environmental temperature, Humidity, Rain, Pressure, Cloud cover 4.4kW, Milano, Italy

26 A. Some Results: Solar Tech Lab
NMAEp%= 3.08% Figure on the left: Red line: predicted power Blue line: mesured power Figure on the right: Green line: Global tilted irradiation 30° Blue line: Global horizontal irradiation Black line: theoretical global irradiation 30° Pink line: cloud cover forcasted Red line: measured power Looking at the second day (cloudy day) we can see, observing the pink line, that there was an error in the weather forecast. In fact the forecasted cloud cover (pink line) shows a riduction in the global radiation starting from sunrise to 12 and then during the night. Instead the measured radiation shows that cloud cover was high only from 9 to 12. NMAEp% = 30.1% pink line: there was an error in the weather forecast.

27 B. Hybrid Models (ANN+Physical) PV Plant in Cuneo
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 ANNs are stochastic methods then we can use more than one trials to make the forecast

28 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 This allows us to analyze the error due to the weather forecasting. In this figure we can see that there is a difference in the irradiation given by weather service and the one measured. This difference is very high in the second day. Here the meteo service forecasted a cloudy day in the morning. Instead we had a sunny day.

29 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. To better understand the entity of the error in this picture we can see the absolute hourly error based on expected irradiation (predicted) and on the measured irradiation. Hourly errors are sorted from largest to smallest to allow us an easier comparison. We can see that the hourly error could be huge: when weather forecast give very low or zero at all irradiations. The normalized error is good enough: about 13%. Solar Radiation forecastings are affected by a great error!

30 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. Ensemble methods reduce the error! The error based on the measured power is bigger than the one based on the predicted! The ANN are stochastic methods than different trials give different forecast. The forecast can be evaluated considering not a single trial but the average value of more trials. Hourly sample (from sunrise to sunset)

31 Some Results: Power Plant
It is very difficult to obtain a day error lesser than 10%, in particular in cloudy days. NMAEp% = 10 NMAEr% = 5.86 WMAEp% = 16.58 NMAEp% = 29.14 NMAEr% = 15 WMAEp% = 50 NMAEp% = 16 NMAEr% = 7.33 WMAEp% = 28.7

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

33 Introduction: the energy production forecasting and the role of RES in the world and in Italy
The energy forecasting from RES Weather forecasting The PV forecasting and error definition, some examples The wind forecasting, some examples Conclusion

34 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

35 Example: Castiglione Messer Marino Wind Farm
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.

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

37 Hybrid methods: computational fluid dynamics software
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.

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

39 Introduction: the energy production forecasting and the role of RES in the world and in Italy
The energy forecasting from RES Weather forecasting The PV forecasting and error definition, some examples The wind forecasting, some examples Conclusions

40 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!!! as the challenge in RES penetration has emerged just a couple of years ago.

41 THANK YOU! www.solartech.polimi.it Diapartimento di Energia
Via Lambruschini, 4 20133 Milano Tel (Centralino) 3709 (Leva) – 3810 (Manzolini)

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


Download ppt "Energy production forecasting based on renewable sources of energy"

Similar presentations


Ads by Google