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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, Italy
[email protected], www.solartech.polimi.it
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.
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
Sonia Leva
3
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
Sonia Leva
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
Global energy demand rises by over one-third in the period to 2035,
underpinned by rising living standards in China, India & the Middle East
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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
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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!
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Introduction: the role of RES in Italy
7
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!
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Introduction: the role of RES in Italy
Electricity generation in Italy in the first
seven monthes of 2013
Bioenergy
geothermal
wind
PV
Hydro
Thermoelectric
fossil
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• 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
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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
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.
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.
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The energy forecasting from RES
11
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.
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Time scale classification for RES Forecasting
Time
horizon
Very short-term
Short-term
Range
Applications
Few seconds to
30minutes ahead
- Control and adjustment actions
30 minutes to 6
hours ahead
- Economic Dispatch Planning
- Load Increment/Decrement
Decisions
- Generator Online/Offline Decisions
Medium-term
6 hours to 1 day
ahead
- Operational Security in Day-Ahead
- Electricity Market
- Unit Commitment Decisions
Long-term
1 day to 1 week
or more ahead
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- Reserve Requirement Decisions
- Maintenance Scheduling to Obtain
Optimal Operating Cost
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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
Sonia Leva
14
Weather forecast
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.
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Weather forecast models
Numerical Weather
Prediction (NWP)
Cloud Imagery
Statistical Methods
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).
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.
based on historical
observation data using
time series regression
models
European Center for Medium-Range
Weather-Forecasts Model (ECMWF)
Global Forecast System (GFS),
North American Mesoscale Model (NAM)
Satellite-based (METEOSAT),
Total Sky Imager,
3-6 hors
24h-48h
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ARIMA
Articial Neural Networks (ANN),
Fuzzy Logic (FL),
ARMA/TDNN
ANFIS
RBFNN
MLP
Long term
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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.
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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
Sonia Leva
The PV forecast: different Models.
18
Physical Models
Statistical Models
to describe the relation between
environmental data and power
are based on persistent prediction or on the time
series' history
-
Persistent prediction, Similar-days Model
-
highly sensitive to the weather
prediction
have to be designed
specifically for a particular
energy system and location
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.
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The PV forecast: Physical Models.
Weather forecast
Physical
Algorithm
PV energy
forecast
Global Irradiation, Cloud
cover, Temperature, ecc
Plant Description; Monitoring System
Measured
data
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PV energy forecast
The PV forecast: Statistical Models
TRAINED NEURAL NETWORK
Environmental
temperature
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Error Definitions
21
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:
eh  Pm,h  Pp,h
 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]:
Pm,h  Pp,h
e
e pu , p,h 

Pp,h
h
Pp,h
•absolute error based on the hourly output produced power
(m=measured) [AEEG]:
e
e pu , m, h 
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h
Pm, h
AEEG=Italian Authority for
Electricity and Gas
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Error Definitions
• Mean absolute error [AEEG et al]:
1 N
MAE   | Pm, h  Pp ,h |
N h 1
• Normalized mean absolute error NMAE, based on net capacity of
the plant C [AEEG et al]
1 N | Pm,h  Pp ,h |
NMAE%  
100
N h 1
C
C could be the rated power, the maximum observed or expected
power!!!!
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Error definitions
• Weighted mean absolute error WMAE% based on total energy
production [AEEG et al.]:
| Pm, h  Pp ,h |

h 1
WMAE% 
100
N
 h1Pm,h
N
• Normalized root mean square nRMSE, based on the maximum
observed power [Urlicht et al]:
nRMSE 
1
N
 h1 | Pm,h  Pp,h |2
N
max( Pm,h )
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Some examples: Hybrid Models (ANN+Physical)
 Plant data validation: Theoretical
Solar Irradiance (clear sky)
 Physical data: Theoretical Solar
Irradiance (clear sky), Sunrise-,
Sunset-hour
 weather forecasts
 weather forecasts data
evaluation of their reliability.
analysis:
 Comparison between ANN forecasts and
other methods
 Ensembled methods
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 Error definitions
Accuracy
assessment of the
obtained results
24
A. Hybrid Models (ANN+Physical) at SolarTech Lab
TRAINED NEURAL NETWORK
Environmental
temperature
Clear Sky
Physical Model
4.4kW, Milano, Italy
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A. Some Results: Solar Tech Lab
NMAEp%= 3.08%
NMAEp% = 30.1%
pink line: there
was an error in
the weather
forecast.
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B. Hybrid Models (ANN+Physical) PV Plant in Cuneo
27
• 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
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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
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B. Hybrid Models (ANN+Physical) PV Plant in Cuneo
Absolute hourly error based on expected global irradiation
(predicted) and on the measured global irradiation.
Error due to the weather forecasting: Absolute hourly errors of GI are sorted from
largest to smallest.
Solar Radiation forecastings are affected by a great error!
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Some Results: Power Plant
Absolute hourly error based on expected output power
(predicted) and on the measured output power.
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.
Ensemble methods
reduce the error!
The error based on the measured power is
bigger than the one based on the predicted!
Hourly sample (from sunrise to sunset)
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Some Results: Power Plant
NMAEp% = 10
NMAEr% = 5.86
WMAEp% = 16.58
NMAEp% = 29.14
NMAEr% = 15
WMAEp% = 50
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NMAEp% = 16
NMAEr% = 7.33
WMAEp% = 28.7
expected output power (predicted) and versus
measured output power.
Some Results: Power Plant
1 year: NMAEp = 12.15%, NMAEr%=7,34%
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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
Sonia Leva
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
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Example: Castiglione Messer Marino Wind Farm
35
 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.
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Some Results: Castiglione Messer Marino Wind Farm
Wind plant forecast
16
Pm,h
Pp,h
14
12
 1000 iterations:
NMAEp = 40.2 %
NMAEr= 14%
Power (MW)
10
8
6
4
2
0
84
86
88
90
Day
92
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94
96
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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.
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The most promising method: Hybrid methods
38
Plant Description
ANN
Historical Wind data
Historical Power data
Ground description
Physical algorithm
CFD Analysis
by GSE, ANEMOS.plus
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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
Sonia Leva
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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!!!
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THANK YOU!
www.solartech.polimi.it
Diapartimento di Energia
Via Lambruschini, 4
20133 Milano
e-mail: [email protected]
e-mail: [email protected]
Tel. +39 02 2399 3800 (Centralino)
3709 (Leva) – 3810 (Manzolini)
Sonia Leva
Absolute hourly error based on expected output power
(predicted) and on the measured output power.
Some Results: Power Plant
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