A Survey of Wind Power Ramp Forecasting

doi:10.4236/epe.2013.54B071

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Energy and Power Engineering, 2013, 5, 368-372

doi:10.4236/epe.2013.54B071 Published Online July 2013 (http://www.scirp.org/journal/epe)

A Survey of Wind Power Ramp Forecasting*

Tinghui Ouyang, Xiaoming Zha, Liang Qin#

School of Electrical Engineering, Wuhan University, Wuhan, China

Email: #[email protected]

Received January, 2013

ABSTRACT

At home and broad, more wind power is being installed in electricity markets, the influence brought by wind power

become more important on power system stability, especially the fluctuation, the uncertainty in wind power production

and multi-time scale of the wind. In order to forecast ramp events before the power system encountering failure, so that

the operator can adopt some limited controlling strategy. This paper introduces the present status of the wind power

ramp prediction at home and abroad. And it gives out four kinds of definitions of ramp events, which are used by many

scholars, then provides various forecasting error algorithm. In the aspect of prediction models, it comes up with physi-

cal models and statistical models, and enumerates various examples of different models. Finally, it prospects the ten-

dency of the model improvement about the wind power ramp events forecasting, which would be significant for ramp

research.

Keywords: Ramp Events; Forecasting; Physical Models; Statistical Models

1. Introduction

It brings both opportunities and challenges after the wind

power accessing the grid. The use of fossil fuel has been

increasing for many years in the world and it generates

lots of greenhouse g ases. As a result, environmental p ol-

lution brought us a serious weather problem. In order to

reduce the environmental pollution, we should use re-

newable energy that does not pollute the environment

any more, such as wind power. Recently, wind power

energy, which is eco-friendly, is in the spotlight as the

potential energy [1]. However, wind power brought great

threaten to the system stability because of fluctuation and

uncertainty of the wind. Especially, a typical long term

trend with large positive or negative change in a short

period, we call it a ramp. In order to get a reasonable and

efficient prediction model to forecast ramp events, each

country has done a lot of research, but so far, few can

achieve very good prediction effect.

Ramp events bring much stress to countries all over

the word. More and more countries have wind farm to

provide power output and face the ramp events at the

same time. In Europe, Denmark offers 22%, Spain sup-

plies 6%, and Germany provides 5%. In Asia, Korea an d

Japan also has wind farm, and Korea provides 83%of

total wind power generation in there [2]. In America,

according to statistics, Texas has happened 59 ramp

events, including 35 up-ramp events and 24 down-ramp

events just in 20 05-2006, wh ich bring s great challen ge to

the regional power system's safety. Though many coun-

tries are on the wind power generation, but wind power

output is still caught short in the world. However, with

increasing wind penetration, the size of the ramp events

has also increased. For example, the installed wind ca-

pacity in the Bonneville Power Administration (BPA) is

currently over 2000 MW and is expected to increase to

over 3000 MW. At this capacity, the wind ramps can be

quite large changing by nearly 1000 MW in an hour [3].

So many countries studied the ramp prediction to help

the system operators make well informed scheduling

decisions and keep the generation and the load balanced.

In Australia, The Wind Power Prediction Tool (WPPT)

has been installed for th e first time, to forecast the power

output from the 65 MW Roaring 40s Renewable Energy

P/L Woolnorth Bluff Point wind farm [4].

In China, the wind ramp problem also exists. Since the

wind power became the world’s fastest growing energy,

China adopts the “mass (tens of KW to tens of millions

of KW) shall focus development”, “far distance high

voltage delivery” as the way of wind power development.

In 2010, the capacity of Chinese installed wind power

was 41.827 million KW, and the new installed capacity

is 16 million KW. These two both rank the first in the

world [5]. Unfortunately, because of the high concentra-

*Project supported by the State Key Development Program for Basic

Research of China (Grant No.2012CB215101)

#Corresponding Author.

T. H. OUYANG ET AL. 369

tion of wind access in China, the fluctuation of wind

power brings the potential risk to power grid.

The first section puts forward ramp definition and pre-

diction accuracy metrics comparing with general predic-

tion. The second section summarizes various methods

used in wind power prediction and ramp forecasting. The

last section is conclusion and the tendency on wind ramp

prediction.

2. Differences and Relations

2.1. Ramp Definition

Ramp prediction is a new research field and also differ-

ent from general prediction. Although it is easy to iden-

tify ramps visually, which is defined by a change in

power output that has a large enough amplitude for a

relatively short period of time, there is no consensus on

the accepted formal definition of a ramp event. But it

mainly contains two points: on the one hand is a small

time scale, which is used in most of the literature by 30

minutes, 1 hour, 4 hours; on the o ther hand is the sh arply

power change, which is generally calculated by the per-

centage of installed capacity. At present, many scholars

should take three main characteristics into account to

define a ramp event: direction, duration, and magnitude.

According to the three characteristic, different people

comes up with different definition, four kinds of which

are generally accepted by most scholars [6].

Definition 1: A ramp occurs when the magnitude of

the increase or decrease in the power signal in the inter-

val is greater than a predefined threshold value

: tΔ

val







val

Ptt PtP Δ

Definition 2: A ramp occurs when the difference be-

tween the maximum and the minimum power output

measured in that interval is greater than a threshold

value : tΔ

val









val

maxPt,ttminP t,ttP ΔΔ

Definition 3: A ramp occurs when the absolute value

of the filtered signal f

in the interval exceeds a

given threshold value : tΔ

val



th thN

h1 val

pp PRP











Definition 4: A ramp occurs when the ratio between

the absolute difference of the power in the interval

is greater than a predefined reference value : tΔ

val

PRP



val

Ptt PtPRP

 

These four definitions have different applications. The

first three definitions mainly consider the change of the

amplitude, that is to say, a ramp occurs when wind

power amplitude exceeds a predefined threshold value in

a certain interval of time. The last definition uses the

wind power rate to indicate a ramp. Each of them em-

phasizes in different aspects, and has its advantages and

disadvantages.

It is necessary to improve the definition of ramp

events according to the actual demand. Although the four

definitions can define a wind power ramp event, system

operators mainly take care of the influence after wind

power access to the electric grid. That is to say, How

seriously the wind power fluctuates can be regarded as a

quenchless ramp event for the electric power system.

Therefore, due to the actual demand, combination of grid

structure and power system operation mode is required to

further meet the need of the system when defining a

ramp.

2.2. Prediction Accuracy Metrics

Ramp prediction has something to do with classical pre-

diction in the place of the prediction error. Like the clas-

sical prediction, approaches based on data mining cause

the ramp prediction to a regression problem. The output

is a real number, while the predictive accuracy is a func-

tion of the difference between the forecasted value and

the observed value [6]. According to the conventional

wind power prediction, researchers propose many met-

rics to measure prediction accuracy. But Potter et al.

(2009) observe that such as mean square error (MSE),

root mean square error (RMSE), and other MSE-based

metrics which tend to over-penalize large errors, are not

appropriate for ramp forecasting assessment. At last,

researchers conclude three metrics, that is, the absolute

error (AE), the mean absolute error (MAE), and the

standard deviation (Std) of the absolute error [7-9].

 



AEty ty t

AE t

MAE N

AE tMAE

Std N1















where: is the predicted value, is the ob-

served (measured) value, N is the number of test data

points for the prediction model. The difference is that











yt is the amplitude of wind power for general predic-

tion while power ramp rate (PRR) in ramp prediction.

Moreover, the data set is usually divided into training

and test data sets in ramp prediction models.

T. H. OUYANG ET AL.

370

3. Forecasting Models

3.1. Classification

For wind power forecasts, there exist many prediction

models, which might be grouped under two different ap-

proaches: Physics-based models and St atisti cal m odel s.

Physics-based models, which are based on the physi-

cal characteristics of the weather, are parametric models.

These models aim at translating and refining numerical

weather prediction (NWP) forecasts into the wind power

facilities’ sites and modeling local wind profiles. More-

over, they usually use theoretical power curve, or esti-

mated power curve, to forecast wind power output. For

example, Greaves et al. (2009) and Focken and Lange

(2008) use NWPs to produce forecasts of the power

curves of the wind generation facilities.

Statistical models are widely used forecasting models,

which use historical wind power measurements, mete-

orological data, either NWPs or historical measurements,

and machine learning algorithms to induce a predictive

model. So the following introduce some kinds of models

used in wind power ramp prediction.

3.2. Classical Prediction Models

The traditional physical models using for wind speed

forecasting and wind power predictions are based on the

weather data [10]. They generally make use of global

databases of meteorological measurements and atmos-

pheric models. However large computational systems are

needed to calculate to achieve accurate results [11].

There are still many different methods for different prac-

tical problems using a physical model. For example,

computational fluid dynamics (CFD) is used as an alter-

native method to the power law to adjust for the local

conditions of the physical terrain [12]. Model output sta-

tistics (MOS) are often used to avoid systematic fore-

casting errors and to correct the predicted power output

for unknow n s [ 13 ] .

The statistical methods forecasting the wind power

production need a vast amount of data to be analyzed and

the meteorological processes are not explicitly repre-

sented. Generally a statistical relationship is developed

between the weather forecast or prediction and the po-

tential power output from the wind farm. So the link is

determined and used to forecast the future power output.

Different from physical methods, most statistical meth-

ods involve only one-step to convert the input variables

into power output, which are called as ‘b lack box’.

There are a number of time series analysis methods

used in wind prediction, including autoregressive (AR),

moving average (MA), autoregressive moving average

model (ARMA) and autoregressive integrated moving

average model (ARIMA), the Box-Jenkins methodology,

the use of the Kalman filter and so on. But Torres et al.

[14] found it was possible to get 20% error reduction

compared to persistence to forecast average hourly wind

speed for a 10h forecast horizon at a number of locations

using nine years of historical data using an ARMA mod-

el.

Some soft computing (or machine learning) ap-

proaches, as well as classical time series analysis, are

also the valid way to forecast the wind power production.

Such as artificial neural networks (ANN), fuzzy systems,

besides other models, like, gray predictors and support

vector machines (SVM) have been applied in prediction

for many years. Because they learn from the relationship

between the predicted wind and forecasted power output

using historical time series, so we call these methods

learning approaches, which are also often referred to as

artificial intelligence (AI) methods or 'gray box' methods.

Nowadays, a number of studies have already applied the

neural models to forecast wind. Welch at al [15] com-

pares three types of neural networks (namely MLP, si-

multaneous recurrent neural network (SRN) and Elman

recurrent neural network) trained using particle swarm

optimization (PSO) for short-term prediction of wind

speed.

Recently, Wind speed and power output were fore-

casted with an accuracy respectively 11.2% and 12.2%

better than persistence in terms of MAE by using a grey

predictor with a look-ahead time of 1h [16]. On the other

hand, using a genetic algo rithm (GA) to optimize a fuzzy

inference system (FIS) model as an improvement, the

result was between 9.5% and 28.4% over persistence

depending on the forecast horizon [17]. And researchers

have started to use decision tree techniques in data min-

ing [18]. The results indicate that the predictive power of

individual variables is dependent on the seasons. And

comparing wind power forecasts at 10 wind farms to the

NWP data at each wind farm by using classical MLP

ANNs, mixture of experts, SVM and nearest neighbor

with PSO [19], the main conclusion is that combining

several models for day-ahead forecasts produces better

results. For example, Mohandes et al. [20] compared

SVM to a multi-layer perceptron ANN model to predict

wind speed. Negnevitsky et al.[21] combine two AI me-

thods, ANN and fuzzy logic in a hybrid approach to de-

velop an adaptive neural fuzzy system model (ANFIS).

3.3. Ramp Prediction Models

Time series analysis is also an effective method applied

to ramp prediction. Because wind power data used in

ramp prediction are also time series data which is ob-

served at regular intervals such as year, month, day and

hour etc.[24]. So time series analysis can forecast the

future value using the past data. Many ramp prediction

algorithms have been proposed such as ARIMA model,

regression analysis method, moving average method,

T. H. OUYANG ET AL. 371

exponential smoothing method, and decomposition

method, etc. Reference [2] forecast power output with

ARIMA and exponential smoothing method that are in-

variant time series models. Reference [22] considers the

seasonal factors, and concludes that up ramp tend to oc-

cur during the mid-day and afternoon periods and down

ramp tend to occurs during the evening and night times.

However, wind power has uncertainty in multiple time

scales, each requires its own time series modeling ap-

proach. In the range of seconds to minutes, autoregres-

sive or persistence techniques can deal well with small

amounts of fast fluctuations, but badly with longer term

trends. ARIMA model can process an integral step, but

do not capture abrupt changes in wind power ramp.

To forecast the ramp events, most of the existing wind

power forecasting methods are not suitable. They are

based on the “point forecasting”, i.e., forecasting the

exact value of wind power at a future time[23-25].Some

other methods have extended the point forecasting me-

thods by estimating the confidence interval of point

forecasts[26]. No matter what model is used, it is about

history data point and future point forecast. The differ-

ence between all of the methods is the selection of time

interval, usually for a few minutes ahead to 24 hours

ahead. As a result, forecasting still suffer from a high

level of inaccuracy because ramp event represent a scene

not a point. So some people categorize ramp events into

'classes', and come up with SVM as classifiers and an

elaborate model, which can use available data to predict

the class of future ramps. Reference [27] uses SVM as

the classification engine to predict ramp event. Further-

more, it used the One-Against-All approach to extend

and apply the binary SVM to multi-class prob lem.

As is said above, ramp forecasting is a kind of scene

prediction which can use Markov chain. Most described

models rely on an observable process and are determined

as a function of past values of the process. Markov

Switching Auto Regressive (MSAR) models, which al-

low the switches to be governed by an unobservable

process, propose an alternative to this observable re-

gime-switching modeling. It is assumed to be a Markov

chain. A good characteristic of such approach is that

permits to reflect the impact of some external factors on

the behaviors of certain time-series [28]. Because it can

manage to capture the influence of some complex mete-

orological features, Markov chain is found to be suitable

for modeling especially for weather variables, such as

daily rainfall occurrences [29] or wind fields [30]. For

the specific case of the fluctuations of wind generation,

some people use this hidden Markov Models (HMM) to

describe meteorological features and to forecast the wind

ramp scene that cannot be determined from past values

of measured power production only. HMM models and

the estimation of their parameters are briefly described in

[31].

To improve the ramp prediction approach, combina-

tions of different methods are necessary. There are many

methods existing to forecast wind ramp, most of which

are inherited from the classical wind power prediction

methods. However, each has its own application field.

Generally speaking, at home and abroad, few of them

can acquire high accuracy in forecasting a wind power

ramp. Comprehensive consideration, on the one hand,

time series models, a kind of statistical prediction meth-

ods, have advantages in dealing with the past historical

data to predict future data. On the other hand, Markov

chain has its unique advantages at the transformation

between different events. So we speculate that combin-

ing time series with HMM models would achieve better

results in the wind ramp forecasting.

4. Conclusions and Tendency

It is necessary to improve forecasting accuracy of the

wind power ramp. With the increasing of wind power

installed capacity and concentrating arrangement of most

wind farms, the wind ramp models are needed to be im-

proved so that system operators can ensure power system

safety and economy. The following gives out some sug-

gestions to improve the results of ramp prediction :

1) Physics-based models mainly rely on NWP data.

Because of NWP data coming from signal model, low

update frequency and resulting in forecasting errors,

these make the accuracy of wind power ramp prediction

low. Combining with multiple NWP models can improve

Physics-based modeling, which can improve the accu-

racy of wind power ramp prediction.

2) For statistical model, the domestic and foreign

scholars have studied that combining the prediction re-

sults of different forecast methods can further improve

the prediction accuracy. Moreover, using some good data

mining methods to process data can also improve the

accuracy.

3) Furthermore, the combination and optimization of

the prediction results from physical models and statistical

models can also improve the accuracy.

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