Wind Farm Impact in Power System and Alternatives to Improve the Integration Part 11 pptx

Induction Generator Terminal Voltage – Effect of STATCOM and UPFC 4.5 Effect of wind speed variations The dynamic performance of the FACTS controllers with doubly fed induction generato

Dynamic Simulation of Power Systems with Grid Connected Windfarms 239 Fig 19 and 20 show the response of the induction generator terminal voltage with SVC/TCSC and STATCOM /UPFC

It can be concluded that terminal voltage of the DFIG is above 0.75 p.u after 0.1 seconds Comparing Fig 18, 19 and 20 it can be concluded that the UPFC improves the fault ride through capability of the DFIG very effectively

Fig 19 Induction Generator Terminal Voltage – Effect of SVC and TCSC

Fig 20 Induction Generator Terminal Voltage – Effect of STATCOM and UPFC

4.5 Effect of wind speed variations

The dynamic performance of the FACTS controllers with doubly fed induction generator (DFIG) based wind farm is investigated using the wind speed model shown in Fig 21.[7]

0.85 0.90 0.95 1.00 1.05

Time (sec)

SVC TCSC

Terminal

Voltage

(p.u)

0.92 0.94 0.96 0.98 1.00 1.02

Time (sec)

STATCOM UPFC

Terminal

Voltage

(p.u)

Wind Farm – Impact in Power System and Alternatives to Improve the Integration

240

Fig 21 Wind speed Model considered for long term dynamic simulation

The average wind speed is around 5 Km/h approximately The wind speed data are obtained by measuring the wind speed changes over an hour from the regional meteorological website

It can be observed that during the time period from 0- 1000 sec the wind speed fluctuates around an average wind speed of 5 Km/h But the wind speed reaches 16 Km/h around 1,200 seconds The corresponding rotor speed variation by the induction generator is shown

in Fig 22 It can be observed that the rotor speed changes from its initial value to 1.25 p.u following wind speed increase at 1200 seconds

Time (Sec)

Fig 22 Rotor Speed response of DFIG

The corresponding active power variations are shown in Fig.23

Dynamic Simulation of Power Systems with Grid Connected Windfarms 241

Time (Sec)

Fig 23 Active Power Injected by the wind farm

The active power variations following the wind speed changes are highly fluctuating from the steady state load flow level to the grid The performance coefficient Cp of the wind turbine is kept as 0.48 in the algebraic equation 1C ρ.AV

2

3

p

P = Fig 24 shows the impact of

an SVC/STATCOM controller on the rotor speed response of the DFIG

Fig 24 Rotor Speed Response of induction Generator with SVC/STATCOM

There are no significant rotor speed oscillations in the rotor speed of the induction generator with SVC in the network; however the rotor speed increases to 1.26 p.u with SVC in the network following wind speed increase of 16 Km/h near 1200 seconds The rotor speed response of induction generator with TCSC/UPFC is shown in Fig 25

0 200 400 600 800 1000 1200 1.08

1.10 1.12 1.14 1.16 1.18 1.20 1.22 1.24 1.26

Active

Power

Injected

MW

Wind Farm – Impact in Power System and Alternatives to Improve the Integration

The development of wind turbine and wind farm models is vital because as the level of wind penetration increases it poses dynamic stability problems in the power system For the

1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17

Dynamic Simulation of Power Systems with Grid Connected Windfarms 243 present work we have taken a taken a doubly fed induction generator model and illustrated the presence of sustained oscillations with wind farms Suitable Flexible A.C.Transmission Systems controllers are modeled using the non-linear simulation models and the transient ratings of the FACTS controller are obtained to stabilize the rotor speed/rotor angle oscillations in a DFIG based wind energy conversion scheme The rotor speed stability of the DFIG based system following a generator outage is studied It can be observed that the effect of low voltage ride through (LVRT) is very minimum following the contingency and the presence of a FACTS device like the SVC improves the rotor speed stability

This chapter also presented the results of a long term dynamic simulation of a grid connected wind energy conversion system which simulated wind speed changes From the results it is observed that STATCOM and UPFC are effective candidates for damping the rotor speed oscillations of the induction generator

6 Appendix

a Parameters

Base values for the per unit system conversion

Base Power: 100 MVA, Base Voltage: 0.69 KV for low voltage bus bar, 150 KV for high voltage busbar

b Doubly- Fed Induction Generator

Rated apparent power MVA: 2 MVA, Rotor inertia: 3.527 MW s/MVA

Rs (p.u.) = 0.0693,Xs (p.u.) = 0.080823,Rr (p.u.) =0.00906,Xr (p.u)= 0.09935,

Xm (p.u) = 3.29, Minimum Rotor Speed: 0.56 p.u., Maximum Rotor Speed: 1.122 p.u

c Transformers

Three winding transformer (150 KV: 0 69 KV), Primary rated apparent power=25 MVA, Secondary rated apparent power = 25MVA,Tertiary rated apparent power = 6 MVA

7 Acknowledgement

The author sincerely thanks Dr.M.Abdullah Khan, Professor of Eminence/EEE, B.S.Abdur

Rahman University (Formerly B.S.A.Crescent Engineering College) for his invaluable guidance for completing the research work The author sincerely thanks his father

Mr.S.K.Natarajan & wife Mrs Bhuvana for the moral support extended to him, at times of

pressure during the research work

The author also wishes to place on record his sincere gratitude to Mr.R.M.Kishore Vice Chairman, RMK Engineering College and Prof.Geetha Ramadas, Head of the Department,

Electrical and Electronics Engineering, RMK Engineering College, Tiruvallur District, Tamilnadu, India

8 References

Abdel – Magid Y.L and El-Amin I.M., (1987)“Dynamic Stability of wind –turbine generators

under widely varying load conditions”, Electrical Power and Energy Systems, Vol.9,

No.3, pp.180-188,1987

Chai Chompoo-inwai, Wei-Jen Lee, Pradit Fuangfoo, Mitch Williams and James R.Liao,

(2005), “System Impact study for the interconnection of wind generation and utility

system”, IEEE Transactions on Industry Applications, Vol.41, No.1, pp.163- 168

Wind Farm – Impact in Power System and Alternatives to Improve the Integration

244

Claudio A Canizares, Massimo Pozzi, Sandro Corsi, Edvina Uzunovic,(2003), “STATCOM

modelling for voltage and angle stability studies”, Electrical Power and Energy

Systems, Vol.25,pp.431-441

Clemens Jauch, Poul Sorensen, Ian Norheim, Carsten Rasmussen, (2007) “Simulation of the

Impact of Wind power on the transient fault behavior of the Nordic Power

System”, Electric Power System Research, Vol 77, pp.135-144

Haizea Gaztanaga, Ion Etxeberria-Ottadui, Dan Ocnasu,(2007) “Real time analysis of the

transient response improvement of wind farms by using a reduced scale

STATCOM prototype”, IEEE Transactions on power systems, Vol.22, No.2,

pp.658-666

http://www.kea.metsite.com-online website for wind speed data

Istvan Erlich, Jorg Kretschmann, Jens Fortmann, Stephan Mueller-Engelhardt and Holger

Wrede, (2007),“Modeling of Wind Turbines Based on Doubly-Fed Induction

Generators for Power System Stability Studies”, IEEE Transactions on Power Systems,

Vol.22, No.3, 2007, pp.909-919, 2007

Kundur.P,(1994), “Power System Stability and Control”, McGraw hill

Lie Xiu, Yi Wang,(2007), “Dynamic Modeling and Control of DFIG based Wind Turbines

under Unbalanced Network Conditions, IEEE Transactions on Power Systems, Vol.22,

No.1, pp 314-323

Mohamed S.Elmoursi, Adel M.Sharaf,(2006), “Novel STATCOM controllers for voltage

stabilization of standalone Hybrid schemes”, International Journal of Emerging

Electric Power Systems, Vol.7, Issue 3, Art 5,pp 1-27

Mohan Mathur R, Rajiv K Varma, (2002),Thyristor – Based FACTS controllers for electrical

transmission systems, IEEE press, Wiley and Sons Publications

Nadarajah Mithulananthan, Claudio A Canizares, Graham J.Rogers ,(2003), “Comparison of

PSS, SVC and STATCOM controllers for Damping Power System Oscillations”,

IEEE Transactions on Power Systems, Vol.8, No.2, pp.786-792

Olof Samuelsson and Sture Lindahl, (2005),“On Speed Stability”, IEEE Transactions on Power

Systems, Vol.20, No.2,pp 1179-1180

Senthil Kumar.N , Abdullah Khan.M., (2008) ,“Impact of FACTS controllers on the dynamic

stability of power systems connected with Wind Farms”, Wind Engineering, Vol.32,

No.2, pp.115-132

Varma R.K and Tejbir S.Sidhu, (2006)“Bibliographic Review of FACTS and HVDC

applications in Wind Power Systems”, International Journal of Emerging Electric Power Systems, Vol.7, No 3, pp 1-16

Vladislav Akhmatov, (2003),“Analysis of dynamic behavior of electric power systems with

large amount of wind power”, Ph.D thesis, Technical University of Denmark Dr.N.SENTHIL KUMAR is presently working as Professor in the department of Electrical

and Electronics Engineering, RMK Engineering College, Chennai His area of research includes modeling of FACTS devices for power system studies, modeling

of wind energy conversion systems for power system stability analysis Email: nsksai@rediffmail.com

Part 3

Modelling and Simulation

of Wind Power System

11

Modeling Wind Speed for Power System Applications

Noha Abdel-Karim, Marija Ilic and Mitch J Small

Carnegie Mellon University

USA

1 Introduction

The intermittent nature of wind power presents special challenges for utility system operators when performing system economic dispatch, unit commitment, and deciding on system energy reserve capacity Also, participation of wind power in future electricity markets requires more systematic modeling of wind power It is expected that the installed energy capacities from wind sources in the United States will increase by up to 20% by the year 2020 New York Independent System Operator (NYISO), General Electric (GE), and Automatic Weather Stations Inc., (AWS) conducted a project for the future of wind energy integration in the United States They stated that NY State has 101 potential wind energy sites and it should be able to integrate wind generation up to at least 10% of system peak load without further expansion (GE report 2005) In order to integrate wind power systematically, it is necessary to solve the technical challenges as well as policy regulation designs Some of these polices have been updated to allow increased intermittent renewable energy by settling imbalances in generation rulemakings and portfolio standards, where the most commonly used one at this time is the production tax credit portfolios

Due to intermittent nature of wind power, forecasting methods become a powerful tool and

of great importance to many power system applications that include uncertainties in generation outputs The recent work has discussed several methods to develop wind power forecasting algorithms to anticipate the degrees of uncertainty and variability of wind generation (C Lindsay & Judith, 2008) use an auto-regressive moving average model to estimate the next ten-minute ahead production level for a hypothetical wind farm and investigate the possibility of pairing wind output with responsive demand to reduce the variability in the net wind output In (Kittipong M et al., 2007), the authors develop an Artificial Neural Network (ANN) model to forecast wind generation power with 10-min resolution Current and previous wind speed and wind power generation are used as input parameters to the network where the output from the ANN is the wind generation power (M S Miranda & R W Dunn, 2006) predicted one-hour-ahead of wind speed using both an auto-regressive model and Bayesian approach (D Hawkins & M Rothleder, 2006), discuss operational concerns with increased amount of wind energy in the Day-ahead- and Hour-ahead-Market for CAISO in California They emphasize the importance of forecasting accuracy for unit commitment and ancillary services and the implications of load following

or supplemental energy dispatch to rebalance the system every five minutes In (Alberto F

at el., 2005), the authors propose a probabilistic method to estimate the forecasting error for

Wind Farm – Impact in Power System and Alternatives to Improve the Integration

248

a Spanish Electricity System They propose cost assessment with wind energy prediction error The assessment is developed to estimate the cost associated with any energy deviation they cause (Dale L Osborn, 2006) discusses the impact of wind on the LMP market for Midwest MISO at different wind penetrations level His LMP calculations decrease with the increase of wind energy penetration for the Midwest area The authors of (Cameron W Potter at el., 2005) describe very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia They introduce an Adaptive Neural Fuzzy Inference System (ANFIS) for short-term forecasting of a wind time series in vector form that contains both wind speed and wind direction

We next describe our modeling approach to derive a family wind models ranging from short through and long term models Using the same data, we illustrate achievable accuracy

of this model This chapter presents three major parts in sections 2, 3 and 4 First, section 2 presents a short term wind speed linear prediction model in state space representation using linear predictive coding (LPC), FIR and IRR filters 10-minute, one-hour, 12-hour, and 24-hour wind speed predictions are evaluated in least square error sense and the prediction coefficients are then used in the state space stochastic formula representing past and future predicted values One year wind speed data in 10 minute resolution are first fitted by two Weibull distribution parameters and then transformation to normal distribution is done for prediction calculation purposes

Second, section 3 of the chapter models wind speed patterns by decomposing it in different time scales / frequency bands using the Fourier Transform The decomposition ranges from hourly (high frequency) up to yearly (low frequency), and are important in many power grid applications Short, medium and long-term wind speed trends require data analysis that deals with changing frequencies of each pattern By applying Fourier analysis to wind speed signal, we aim to decompose it into three components of different frequencies, 1) Low Frequency range: for economic development such as long term policies adaptation and generation investment (time horizon: many years), 2) Medium Frequency range: for seasonal weather variations and annual generation maintenance (time horizon: weeks but not beyond a year), 3) High frequency range: for Intra-day and Intra-week variations for regular generation dispatches and generation forced outage (time horizon: hours but within

a week) Each decomposed signal is presented in a lognormal distribution model and a Discrete Markov process and the aggregated complete wind speed signal is also applied Third, section 4 presents the prediction results using past histories of wind data, which support validity of Markov model These independencies have been modeled as linear state space discrete Markov process A uniform quantization process is carried to discretize the wind speed data using an optimum quantization step between different state levels for both wind speed distributions used Also state and transition probability matrices are evaluated from the actual representation of wind speed data Transition probabilities show smooth transitions between consecutive states manifested by the clustering of transition probabilities around the matrix diagonal

2 Wind speed prediction model

2.1 Wind data distribution models

This prediction model uses more than 50 thousands samples of one-year wind speed data in 10-minute resolution The data are used to determine the best fitted parameters of the

Modeling Wind Speed for Power System Applications 249

Weibull distribution model Wind speed data are obtained from National weather station in

NYISO zonal areas by approximate longitudes and latitudes station’s allocation (National

weather station, Available online) The empirical cumulative distribution function (CDF) for

the wind speed random variable (RV) X has been evaluated using n samples based on the

statistical Weibull formula as (Noha Abdel-Karim at el., 2009):

square 0.0356 1.77 1.4 10 3 8 10 4 99.4%

R-Table I Linear regression defines Weibull distribution parameters

Where a random variable X (R.V) represents wind speed, and “n” is the total sample size

Knowing ahead that the wind speed RV is best characterized by the Weibull distribution

model:

 

1 1

X

x a

x e

x X

x a

Where in equation (2) or (3), we mention two alternate, yet equivalent forms of Weibull PDF

and CDF related by a1 Linear regression is performed between Xln( )x , where x

is the data plotted on the horizontal axis, versus the following CDF metric on the vertical

The regression results are shown in table I and both empirical and Weibull cumulative

distributions are plotted in figure 1

Figure 1 presents a best Weibull distribution fit with the empirical CDF to wind speed data

The next step is the transformation to normal distribution with mean zero and variance one

Wind Farm – Impact in Power System and Alternatives to Improve the Integration

250

This transformation is used in both the fitting and prediction processes The histograms of wind speed signals in both Weibull and Normal distributions are shown in Figures 2 and 3, respectively By looking to Figure 3, the shape of the actual signal is shifted down with the exact pattern due to the normalization process, (Noha Abdel-Karim at el., 2009)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Empirical Lower bound Upper bound Weibull

Fig 1 Empirical and Weibull Cumulative Distribution Functions

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Fig 2 Actual & normalized frequency occurrence of wind speed data