Wind Farm Impact in Power System and Alternatives to Improve the Integration Part 7 pot

Optimal Management of Wind Intermittency in Constrained Electrical Network 139 When the wind production tends to increase, the wind generator can ensure the projected exchange power to

Optimal Management of Wind Intermittency in Constrained Electrical Network 139 When the wind production tends to increase, the wind generator can ensure the projected exchange power to the network Note that the storage need is lower At the end of the day, the storage level may be not equal to the initially expected level (Fig 19, dotted line) In this way, the storage use plan for the next days is challenged It is the responsibility of the wind power manager to decide whether the function plan has to be reviewed The decision may

be made in function of forecast data and the difference between the projected and real storage levels

The injected power plans of the wind generator to the network are given in the Fig 20 in 3 cases of wind production scenarios: with initially expected plan (Fig 20, fulfilled line), with 30% more than expected (dotted line mark ".") and with 30% than expected (dotted line) The difference with the initial plan creates penalties

The objective function’s variation is showed in the as the difference between the energy sale benefit (paid by the network) and the penalties The results are given in percentage compared to the expected power It is interesting to see that with incertitude of about ±30%

on the wind production, the objective function would vary only about 6% That proves the interest of the proposed optimal management method

Avec +30% de production éolienne

With -30% of wind production Initial plan

With +30% of wind production

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

9.2 Reactive management in real-time

We are now J-Day and suppose that some disturbances occur during this day

• At 5 a.m, a lack of power can be translated by an increasing penalty price for each MW that the W+S system does not provided to the network (from 26.51 €/MWh to 76.51

€/MWh)

• At 10 a.m, the wind production increases from 9.26 MW to 11.26 MW

• At 3 p.m, to response to the network need to reduce injected power, the W+S system has to decrease its provided power from 9.84 MW to 7.84 MW

The following graphics show the W+S system’s behaviour under these conditions and the impact of these disturbances on the global result

puissance demande perturbation

puissance réseau perturbation

Fig 22 Final required and exchanged power plan during disturbances

Optimal Management of Wind Intermittency in Constrained Electrical Network 141

Fig 23 Evolution of storage state

In order to face these events, new optimal operate plans are computed each time

disturbances occurred

A suitable response is proposed in order to manage several unpredicted events disturbing

the system and the electrical network The optimization response most suits in function of

unpredictable constraints occurring Concretely, the actual total penalty cost is equal to 1020

€ per day instead of 2363 € per day without disturbances (cf Table 5)

Case I Maximal economic gains (in the case forecast - anticipation)

Case II Minimal power deviations (in the case disturbance – reactive management)

Total net profit [€/day]

Table 5 Simulations of profit and penalty cost considering two kinds of forecast and

disturbances

The benefits of case II is lower than that of case I But the penalty cost of case II represents

only 43.17% of case I But the net profit is higher in case II In conclusion, it is more efficient

to manage the reactive management of minimal power gaps is more efficient than the

management by anticipation of maximal economic gains

10 Conclusions

The development of an optimized management method of W+S systems was the main topic

of this chapter First, a thorough analysis of the W+S system parameters (intermittencies,

dynamic, cost efficiency) has been implemented

Then, bibliography on management methods of W+S systems has been carried on The

differences between the methods are mainly due to the applied conditions concerning the

wind energy implementation With support mechanisms, the objective is to maximize the

benefits of the wind energy selling to the electrical network This strategy allowed a

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

142

significant growth of the wind energy during the last years But, with the increasing of wind energy growth rate a new management method of intermittencies is needed Its objective is

to minimize the impact of intermittencies on the power system

The purpose of the management method dedicated to the optimal operation of a wind farm coupled to a storage system (W+S) which has been proposed in this chapter is its adaptation

to the specific characteristics of the system in the new context of the wind energy implementation within the electrical network The optimal management of the W+S exploitation reduces the impacts of intermittencies impacts and better controls the dynamic Moreover, the economical rentability is preserved The energy flow optimisation technique allows the supply of a power adapted to the electrical network requirements (network system services) This method is efficient with several disturbance sources such as wind speed intermittency, variable network requirements, penalty cost variability This system is characterized by the intermittency of the primary source, and by the unpredictable behaviour of the electrical network The proposed systems of control enable an efficiently operate system management with and without disturbances In other words, the architecture of the management system is based on two driving levels: anticipative management and real time reactive management Anticipation is a main step Operate plan and W+S system involvement are determined by anticipation The mathematic description which has been detailed is based on MLP algorithm which is used for optimisation problem and is seem to be adapted to such problem complexity being highly flexible and fast Concerning the real time reactive management, its main role is to manage variation and intermittency impacts in real operating time The optimisation management requires a robust and efficient algorithm Also, a method of sensitivity analysis has been presented This analysis gave us a methodological framework to evaluate the impacts of disturbances

on the optimal operate system By this way, the wind energy intermittency is treated on several time scales Obtained results are based on a feasibility study case This gives a global view of how operates the system

11 References

[ANA-07] Anagnostopoulos J S., Dimitris E Papantonis, “Pumping station design for a

pumped-storage wind-hydro power plant”, School of Mechanical Engineering, National

Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Athens, Greece Available online 27 August 2007

[BEN-08] Benitez L E., Benitez P C., Cornelis V K G., “The Economics of Wind Power with

Energy Storage”, Energy Economics, Volume 30, Issue 4, July 2008, pp 1973-1989

[BUR-01] Burton T., Sharpe D., Jenkins N., Bossanyi E (2001) “Wind Energy Handbook”, John

Wiley & Sons, Ltd/Inc., Chichester

[CAS-03] Castronuovo E.D., Peças L J A., “Wind and small-hydro generation: An

optimisation approach for daily integrated operation”, Proceedings of the 2003 EWEC (European Wind Energy Conference) June 16–19, 2003, Madrid, Spain [CAS-04a] Castronuovo E.D., Peças L J A., “On the optimization of the daily operation of a

wind-hydro power plant”, IEEE Transactions on Power Systems, Volume 19, Issue

3, Aug 2004, pp 1599 – 1606

Optimal Management of Wind Intermittency in Constrained Electrical Network 143 [CAS-04b] Castronuovo E.D., Peças L J A., “Bounding active power generation of a wind-

hydro power plant”, Proceedings of the PMAPS-2004 (8th International Conference on Probabilistic Methods Applied to Power Systems) September 13-16,

2004, Ames, Iowa, USA

[CAS-04c] Castronuovo E.D., Peças L J A., “Optimal operation and hydro storage sizing of

a wind–hydro power plant”, International Journal of Electrical Power & Energy Systems, Volume 26, Issue 10, December 2004, pp 771-778

[DWIA] Danish Wind Industry Association, http://guidedtour.windpower.org/fr/tour/

[GAR-06] Gary L J (2006), “Wind Energy Systems”, Manhattan, KS

[GEN-05] Genc A., Erisoglu M., Pekgor A., Oturanc G., Hepbasli A., Ulgen K., “Estimation of

Wind Power Potential Using Weibull Distribution”, Energy Sources, Part A: Recovery,

Utilization, and Environmental Effects, Volume 27, Issue 9 July 2005, pages 809 –

822

[GER-02] Gergaud O., “Modélisation énergétique et optimisation économique d'un système

de production éolien et photovoltạque couplé au réseau et associé à un accumulateur”, Thèse de doctorat de l’Ecole Normale Supérieure de Cachan, Décembre 2002

[HAL-01] Halldorsson K., Stenzel J “A scheduling strategy for a renewable power marketer”,

Power Tech Proceedings, 2001 IEEE Porto Volume 1, 10-13 Sept 2001, vol.1, pp 6 pages

[KAL-07] Kaldellis J.K., Zafirakis D.,“Optimum energy storage techniques for the

improvement of renewable energy sources-based electricity generation economic efficiency”, Energy Volume 32, Issue 12, December 2007, Pages 2295-2305

[MAG-03] Magnus K., Holen A T., Hildrum R., “Operation and sizing of energy storage for

wind power plants in a market system”, International Journal of Electrical Power &

Energy Systems, vol 25, Issue 8, October 2003, pp 599-606

[MOM-01] Momoh J A., “Electric Power System Applications of Optimization”, CRC Press; 1

edition (January 15, 2001), 478 pages

[NGU-09] Nguyen Ngoc P.D., Pham T.T.H; Bacha S., Roye D “Optimal operation for a

wind-hydro power plant to participate to ancillary services”, Industrial Technology, 2009 ICIT

2009 IEEE International Conference on Digital Object Identifier: 10.1109/ICIT.2009.4939699, Publication Year: 2009, pp 5 pages

[RTE] Réseau de Transport d’Électricité, http://clients.rte-france.com/

[RTE08] RTE2008: http://www.rtefrance.com/fr/nousconnaitre/espaceesse/dossiers

-de-presse/le-bilan-electrique-francais - 2008

[SAG-07] Saguan M., “L’analyse economique des architectures de marches electrique

Application au market design du "temps reel”, Thèse de Doctorat de l’ Université Paris-Sud 11, Avril 2007

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

144

[SOM-03] Somaraki M., “A Feasibility Study of a Combined Wind - Hydro Power Station in

Greece”, a thesis submitted for the degree of Master in Science in “Energy Systems

and the Environment”, University of Strathclyde, Department of Mechanical Engineering, October 2003 - Glasgow

7

Intelligent Control of Wind Energy

Conversion Systems

Abdel Aitouche1 and Elkhatib Kamal2

controllers (Lescher et al., 2006) and by other methods (Chen & Hu, 2003; Kamal et al., 2008;

Muljadi & Edward, 2002) Nonlinear controllers (Boukhezzar & Siguerdidjane, 2009; Chedid

et al., 2000; Hee-Sang et al., 2008) have also been proposed for the control of WECS

represented by fuzzy models

In addition to stability, robustness is also an important requirement to be considered in the study of uncertain nonlinear WECS control systems Robustness in fuzzy-model-based control has been extensively studied, such as stability robustness versus modelling errors

and other various control techniques for Takagi–Sugeno (TS) fuzzy models (Kamal et al., 2010; Uhlen et al., 1994)

In order to overcome nonlinearity and uncertainties, various schemes have been developed

in the past two decades (Battista & Mantz, 2004; Boukhezzar & Siguerdidjane, 2010; Prats et

al., 2000; Sloth et al., 2009) (Battista & Mantz, 2004) addressing problems of output power

regulation in fixed-pitch variable-speed wind energy conversion systems with parameter uncertainties The design of LMI-based robust controllers to control variable-speed, variable-pitch wind turbines, while taking into account parametric uncertainties in the

aerodynamic model has been presented (Sloth et al., 2009) (Boukhezzar & Siguerdidjane,

2010) comparing several linear and nonlinear control strategies, with the aim of improving

wind energy conversion systems (Prats et al., 2000) have also investigated fuzzy logic

controls to reduce uncertainties faced by classical control methods

Furthermore, although the problem of control in the maximization of power generation in variable-speed wind energy conversion systems (VS-WECS) has been greatly studied, such

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

146

control still remains an active research area (Abo-Khalil & Dong-Choon, 2008; Aggarwal et

al., 2010; Barakati et al., 2009; Camblong et al., 2006; Datta & Ranganathan, 2003; Galdi et al.,

2009; Hussien et al., 2009; Iyasere et al., 2008; Koutroulis & Kalaitzakis, 2006; Mohamed et al., 2001; Prats et al., 2002; Whei-Min & Chih-Ming, 2010) (Abo-Khalil & Dong-Choon, 2008; Aggarwal et al., 2010; Camblong et al., 2006; Datta & Ranganathan, 2003; Whei-Min &

Chih-Ming, 2010) maximum power point tracking (MPPT) algorithms for wind turbine

systems have been presented (Galdi et al., 2009) as well as design methodology for TS fuzzy

models This design methodology is based on fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms (GA), and recursive least-squares (LS) optimization methods for model parameter adaptation A maximum power tracking algorithm for wind turbine systems, including a matrix converter (MC) has been presented

(Barakati et al., 2009) A wind-generator (WG) maximum-power-point tracking (MPPT)

system has also been presented (Koutroulis & Kalaitzakis, 2006), consisting of a high efficiency buck-type dc/dc converter and a microcontroller-based control unit running the

MPPT function An advanced maximum power-tracking controller of WECS (Mohamed et

al., 2001), achieved though the implementation of fuzzy logic control techniques, also

appears promising The input to the controller consists in the difference between the maximum output power from the WES and the output power from the asynchronous link and, the derivative of this difference The output of the controller is thus the firing angle of the line-commutated inverter, which transfers the maximum tracked power to the utility grid Fuzzy controllers also permit the increase of captured wind energy under low and

high wind speeds (Prats et al., 2002; Hussien et al., 2009) The fuzzy controller is employed to

regulate, indirectly, the power flow in the grid connected WECS by regulating the DC current flows in the interconnected DC link Sufficiently stable conditions are expressed in

terms of Linear Matrix inequalities (LMI) (Iyasere et al., 2008) to maximize the energy

captured by the wind turbine under low to medium wind speeds by tracking the desired pitch angle and rotor speed, when the wind turbine system nonlinearities structurally uncertain

Concerning other studies, due to the strong requirements of the Wind Energy Field, fault tolerant control of variable speed wind turbine systems has received significant attention in

recent years (Bennouna et al., 2009; Gaillard et al., 2007; Odgaard et al., 2009; Ribrant, 2006; Wang et al., 2010; Wei et al., 2010) To maintain the function of closed-loop control during

faults and system changes, it is necessary to generate information about changes in a supervision scheme Therefore, the objective of Fault Tolerant Control (FTC) is to maintain current performances close to desirable performances and preserve stability conditions in the presence of component and/or instrument faults FTC systems must have the ability to adjust off-nominal behaviour, which might occur during sensor, actuator, or other

component faults A residual based scheme has been presented (Wei et al., 2010) to detect and accommodate faults in wind turbines An observer based scheme (Odgaard et al., 2009)

has been proposed to detect and isolate sensor faults in wind turbine drive trains A study of

fault tolerant power converter topology (Gaillard et al., 2007) and fault identification and

compensation for a WECS with doubly fed induction generator (DFIG), has also been done

In addition, a survey on failures of wind turbine systems in Sweden, Finland and Germany (Ribrant, 2006), has been carried out, where the data are from real maintenance records over the last two decades Robust fault tolerant controllers based on the two-frequency loop have

also been designed (Wang et al., 2010) The low-frequency-loop adopts a PI steady-state

optimization control strategy, and the high-frequency-loop adopts a robust fault tolerant

Intelligent Control of Wind Energy Conversion Systems 147 control approach, thus ensuring the actuator part of the system during failure in normal operation Fault signature analysis to detect errors in the DFIG of a wind turbine has again

been presented (Bennouna et al., 2009)

It is well known that observer based design is a very important problem in control systems Since in many practical nonlinear control systems, state variables are often unavailable, output feedback or observer-based control is necessary and these aspects have received

much interest (Khedher et al.,2009, 2010; Odgaard et al., 2009; Tong & Han-Hiong, 2002; Tong et al., 2009 ; Wang et al., 2008; Yong-Qi, 2009; Zhang et al., 2009) fuzzy observer designs

for TS fuzzy control systems have been studied, and prove that a state feedback controller and observer always result in a stabilizing output feedback controller, provided that the stabilizing property of the control and asymptotic convergence of the observer are guaranteed through the Lyapunov method However, in the above output feedback fuzzy controllers, the parametric uncertainties for TS fuzzy control systems have not been considered As such robustness of the closed-loop system may not be guaranteed

In this chapter, a Robust Fuzzy Fault Tolerant control (RFFTC) algorithm is proposed for hybrid wind-diesel storage systems (HWDSS) with time-varying parameter uncertainties, sensor faults and state variable unavailability, and measurements based on the Takagi-Sugeno (TS) fuzzy model Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability and are formulated in the form of Linear Matrix Inequalities (LMIs) The proposed algorithm combines the advantages of:

• The capability of dealing with non-linear systems with parametric uncertainties and sensor faults;

• The powerful Linear Matrix Inequalities (LMIs) approach to obtain fuzzy fault tolerant controller gains and observer gains;

• The maximization of the power coefficient for variable pitch variable-speed wind energy conversion systems;

• In addition, reduction of voltage ripple and stabilization of the system over a wide range of sensor faults and parameter uncertainties is achieved

Also in this chapter, a Fuzzy Proportional Integral Observer (FPIO) design is proposed to achieve fault estimation in TS fuzzy models with sensor faults and parameter uncertainties Furthermore, based on the information of online fault estimation, an observer-based robust fuzzy fault tolerant controller is designed to compensate for the effects of faults and parameter uncertainties, by stabilizing the closed-loop system Based on the aforementioned studies, the contributions of this chapter are manifold:

• A new algorithm for the estimation of time-varying process faults and parameter uncertainties in a class of WECS;

• And a composite fault tolerant controller to compensate for the effects of the faults, by stabilizing the closed-loop system in the presence of bounded time-varying sensor faults and parameter uncertainties

This chapter is organized as follows In section 2, the dynamic modelling of WECS and system descriptions is introduced Section 3 describes the fuzzy plant model, the fuzzy observer and the reference model In section 4, robust fuzzy fault tolerant algorithms are proposed, to close the feedback loop and the stability and robustness conditions for WECS are derived and formulated into nonlinear matrix inequality (general case) and linear matrix inequality (special case) problems Section 5 presents the TS Fuzzy Description and Control structure for HWDSS Section 6 summarizes the procedures for finding the robust fuzzy fault tolerant controller and fuzzy observer In section 7 simulation results illustrate the

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

148

effectiveness of the proposed control methods for wind systems In section 8, a conclusion is

drawn

2 WECS model and systems descriptions

2.1 The wind turbine characteristics

Variable Speed wind turbine has three main regions of operation as shown in Fig.1 (Galdi et

al., 2009) The use of modern control strategies are not usually critical in region I, where the

monitoring of the wind speed is performed to determine whether it lies within the

specifications for turbine operation and if so, the routines necessary to start up the turbine

are performed Region II is the operational mode in which the goal is to capture as much

power as possible from the wind Region III is called rated wind speed The control

objectives on the full load area are based on the idea that the control system has to maintain

the output power value to the nominal value of the generator The torque at the turbine

shaft neglecting losses in the drive train is given by (Iyasere et al., 2008):

2 3

),(5

0 πC λ β R ν

T

t

where T G is the turbine mechanical torque, Where ρ is the air density (kg /m3), R is the

turbine radius (m),ν is the wind velocity (m/s), and C t (λ,β) is the turbine torque coefficient

The power extracted from the wind can be expressed as (Galdi et al., 2009) :

νπρβλ

ωT 0.5C ( , ) R2 3

P

p G

where C p (λ,β) is the rotor power coefficient defined by the following relation,

),(),(λ β λ λ β

t

βλβ

λπββ

λ, ) (0.44 0.0167 )sin[ ( 3)/15 0.3 ] 0.00184( 3)

β is the pitch angle of rotor blades (rad) (β is constant for fixed pitch wind turbines), λ is the

tip speed ratio (TSR) and is given by:

νω

where ωt is the rotor speed (rad/sec) It is seen that if the rotor speed is kept constant, then

any change in the wind speed will change the tip-speed ratio, leading to the change of

power coefficient C p as well as the generated power out of the wind turbine If, however, the

rotor speed is adjusted according to the wind speed variation, then the tip-speed ratio can

be maintained at an optimal point, which could yield maximum power output from the

system Referring to (3) optimal TSR λopt can be obtained as follow:

3])167.044.0(

)3.015(00184.0[cos)3.015

ββ

πβ

Intelligent Control of Wind Energy Conversion Systems 149

From (5) it is clear that λopt depends on β The relationship of C p versus λ, for different values

of the pitch angle β, are shown in Fig 2 The maximum value of C p (C p(max)= 0.48) is achieved

for β = 0o and for λ= 8 This particular value of λ is defined as the optimal value of TSR

(λopt).Thus the maximum power captured from the wind is given by:

νπβ

(max) (max) 0.5C ( , ) R

p

Normally, a variable speed wind turbine follows the C p(max) to capture the maximum power

up to the rated speed by varying the rotor speed at ωopt to keep the TSR at λopt

Fig 1 Power-wind speed characteristics

2.2 WECS system description

A wind-battery hybrid system consists of a wind turbine coupled with a synchronous

generator (SG), ), a diesel-induction generator (IG) and a battery connected with a

three-phase thyristor-bridge controlled current source converter In the given system, the wind

turbine drives the synchronous generator that operates in parallel with the storage battery

system When the wind-generator alone provides sufficient power for the load, the diesel

engine is disconnected from the induction generator The Power Electronic Interface (PEI)

connecting the load to the main bus is used to fit the frequency of the power supplying the

load as well as the voltage Fig 3 shows the overall structure of wind-battery system: E fd is

the excitation field voltage, f is the frequency, V b is the bus voltage, C a is the capacitor bank,

V c is the AC side voltage of the converter, and I ref is the direct-current set point of the

converter

Fig 2 Power coefficient C p versus TSR λ

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

150

The dynamics of the system can be characterized by the following equations (Kamal et al., 2010):

)()(t Bu t Ax

where

T s

b ][V

ref

fd ][E

V J B

V J s

ωω

where ω s is the bus frequency (or angular speed of SG) J s , D s are the inertia and frictional

damping of SG, i sd , i sq are the direct and quadrature current component of SG, L d , L f are the

stator d-axis and rotor inductance of SG, L md is the d-axis field mutual inductance, τ`is the transient open circuit time constant, r a is the rotor resistance of SG, P ind is the power of the

induction generator, P load is the power of the load Equation (7) indicates that the matrices A

and B are not fixed, but change as functions of state variables, thus making the model

nonlinear Also, this model is only used as a tool for controller design purposes

Fig 3 Structural diagram of hybrid wind-diesel storage system

The used system parameters are shown in Table 1 (Chedid et al., 2000; Kamal et al., 2010)