A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system

Renewable Energy 36 (2011) 1693e1703

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system Manoj Datta a, *, Tomonobu Senjyu a, Atsushi Yona a, Toshihisa Funabashi b a b

Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru Nishihara-cho, Nakagami-gun, Okinawa 903-0213, Japan Meidensha Corporation, ThinkPark Tower, 2-1-1, Osaki, Shinagawa-ku, Tokyo 141-6029, Japan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 February 2010 Accepted 3 December 2010 Available online 11 January 2011

A Photovoltaic system’s output power fluctuates as insolation varies with weather condition. Fluctuating PV power causes frequency deviations when large PV power is penetrated in the isolated utility. In this paper, a fuzzy based method for leveling the fluctuations of PV power in a PV-diesel hybrid power system is proposed. By means of the proposed method, output power control of PV system becomes possible considering power utility conditions and the conflicting objective of output power leveling and maximizing energy capture is achieved. Here, fuzzy control is used to generate the output leveling power command. The fuzzy control has three inputs of average insolation, variance of insolation, and absolute average of frequency deviation. First, the proposed method is compared with the method where captured maximum power is given to the utility without leveling. Second, the proposed method is compared with a conventional method where captured maximum power is leveled by using an energy storage system and is given to the isolated utility. Simulation results show that the proposed method is effective in leveling PV power fluctuations and is feasible to reduce the frequency deviations of the isolated power utility. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Leveling PV power fluctuations Fuzzy control Frequency deviation

1. Introduction Isolated islands mostly depend on diesel generators for electric power supply. The generation cost of electric power by using diesel generators in isolated islands is expensive compared to a conventional generation. Besides, as the mitigation of global warming and the reduction of CO2 gas emissions are of great interest worldwide, the consumption of fossil fuels in these isolated islands must be reduced and clean and renewable energy sources must be introduced. One of the most promising application of renewable energy technology is the installation of hybrid energy systems in remote areas, where the cost of grid extension is prohibitive and the price for fuel increases drastically with the remoteness of the location. Renewable energy sources, such as photovoltaic (PV), wind energy, or small scale hydro, provide a realistic alternative to engine-driven generators for electricity generation in remote areas [1]. It has been demonstrated that hybrid energy systems can significantly reduce the total life cycle cost of stand-alone power supplies in many

* Corresponding author. Tel.: þ81 98 895 8686; fax: þ81 98 895 8708. E-mail address: [email protected] (M. Datta). 0960-1481/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2010.12.009

situations, while at the same time providing a more reliable supply of electricity through the combination of energy sources [2]. This paper concentrates on the control and application of PVdiesel hybrid energy systems, which account for the majority of systems installed today. One of the inherent advantages of photovoltaic electricity generation is the absence of any mechanical parts (unless tracking of the sun is included). Professionally installed PV arrays are characterized by a long service lifetime, exceeding 20 years, high reliability: and low maintenance requirements, which are highly desirable for remote area power supplies. In sunny locations, PV generators compare favorably with wind generators, despite the higher investment cost for photovoltaic modules per peak Watt [3]. Wind generators require regular maintenance and are susceptible to damage in strong winds. Global photovoltaic (PV) production has been doubling every two years, increasing by an average of 48% each year since 2002, making it the world’s fastest growing energy technology [4]. Two factors have been boosting this: improved generation efficiency of PV modules and governmental subsidies for the initial cost of residential PV generation systems [5]. However, the power output of PV systems fluctuates depending on weather conditions, season, and geographic location. In the future, when a significant number of PV systems will be connected to the grids of power utilities, power

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output fluctuation may cause problems like voltage fluctuation and large frequency deviation in electric power system operation [6e8]. Therefore, for the penetration of large PV system’s output power in the utility without reduction of the reliability of utility power systems, suitable measures must be applied to the PV systems side. Studies have been carried out for reducing the output power fluctuations of PV generator. Power characteristics of PV ensembles are presented in [9] where monitored data from 100 PV systems were used to study effects of combined power generation of these systems, compared to the characteristics of an individual system. It was claimed that a significant amount of power fluctuations disappeared, however, large amount of short-term power fluctuations remained. In addition, when the number of PV power generation systems were decreased, the power fluctuations increased. Smoothing of PV system output by tuning MPPT control is demonstrated in [10]. In this method, when the insolation increases rapidly, the operating MPPT point changes to a new point where the maximum power is not generated with the current insolation. However, the condition of power utilities like frequency deviation is not considered for tuning the MPPT and for limiting the new output voltage. Studies of PV-diesel hybrid systems have been reported in [11e14]. Feasibility analysis of stand-alone renewable energy supply options for a large hotel is given in [11] where commercial softwares are used to check the technical feasibility and economic viability of hybrid power systems. However, issues like control of PV power according to load variations for reducing frequency deviations is not addressed. In [12], Optimal configuration of hybrid power generators in isolated island is reported. Genetic algorithm is used to find the optimal configuration. This study did not focus on reducing the output power fluctuation of PV generator. Multi-objective design of PV wind diesel hydrogen battery systems is described in [13]. Here, the PV power is mainly used for electrolyzer and battery charger. Therefore, the fluctuations did not affect the system frequency. Prospects of autonomous/ stand-alone hybrid power systems in commercial applications in hot regions have been reported in [14]. This study mainly focused on the optimum configuration using fore casted load and insolation data. PV generator supplied the load in conjunction with batter while diesel generator acted as a back up. However, control issues to minimize frequency deviation are not discussed. Usually, maximum power point tracking (MPPT) controls [15e18] are used for PV array. However, the MPPT extracted power varies with the insolation. Therefore, an energy storage system (ESS) is typically used. The PV power is stored in the ESS and later supplied at the night or the PV power fluctuations are smoothed by the charge/discharge action of the ESS. Conventional controls of PVdiesel hybrid systems depicted in [19e21] used typical set-up of PV array, diesel generator and battery. The PV generator and the battery are connected to a DC bus. Diesel generator and AC load are connected to an AC bus. A bi-directional inverter is connected between the AC bus and the DC bus which typically converts the DC power to AC power and also helps to charge the battery by borrowing power from the diesel generator. The battery is mainly used to smooth the PV power fluctuations by following moving point average law. The system frequency regulation and voltage control always depends on the diesel generator. Hence, these type of conventional methods cannot control PV power corresponding to power system condition and insolation variations. In this paper, a fuzzy based method for leveling the PV power fluctuations in a PV-diesel hybrid system is proposed. This method uses fuzzy control to produce output power command. The proposed fuzzy control has three inputs which are absolute average of frequency deviation, average insolation and variance of insolation. Here, the output power command is decreased or is kept constant with smoothing effect in the time when large frequency

deviation continuously occurs. Because the frequency deviation increases when large PV power with fluctuations due to rapid insolation change is given to the isolated utility. On the other hand, output power command increases when frequency deviation is small. Hence, it is possible to control the output power command corresponding to power system condition by the proposed method. It is also important to consider the insolation condition besides the power system condition for realizing the conflicting objectives of both output power fluctuations leveling and maximizing energy capture. If insolation changes rapidly in a short period, the PV system’s output will be smaller than available maximum power when the new operating point of PV array is not an optimal one. First, the proposed method is compared with the system [18] where MPPT extracted PV power is fed to the utility without leveling. Second, the proposed method is compared with system [21] where MPPT extracted PV power is leveled by using an ESS and is fed to the isolated utility. The simulation results using the actual insolation and load show the effectiveness and feasibility of the proposed method compared to both of the conventional methods. The paper is organized as follows: Section 2 provides the system description and methodology. However, specialized topics, such as the optimal energy management and performance of an ESS, modern inverter technology, development of new fuzzy algorithm is not discussed in detail in this section as the main focus of this paper is to provide the smoothing of PV power fluctuation by intelligent algorithm and the reduction of frequency deviation by the control of the PV generator. Section 3 describes the results and discussions. Conclusions are drawn in Section 4. 2. System description and methodology The isolated power utility used in this paper is shown in Fig. 1. This is actually a PV-diesel hybrid power system consisted of a diesel-generator set, a PV generator equipped with a bi-directional inverter and AC load. In addition, it is assumed that the isolated power utility is not connected to any large power utility and it is always operated independently as a stand-alone system. The diesel generator supplies the load demand when no PV power/ few PV power is available. An intelligent hybrid energy management system is developed combining the supervisory control described in [22] and the method proposed in this paper. The isolated power system model [23,24] used for simulation is shown in Fig. 2, where Si is the insolation, Voc is the open-circuit voltage of the PV array, Isc is the short-circuit current of the PV * is the MPPT command power, Pmax is the MPPT output array, Pmax * is the command power of the bi-directional inverter P power, Pinv inv * is the output power of the bi-directional inverter, PESS is the ESS command power, PESS is the ESS output power, Pd is generated

Fig. 1. Concept of a PV-diesel hybrid system.

M. Datta et al. / Renewable Energy 36 (2011) 1693e1703

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Δfd = Δfe 1 R

Governor

1 Tsm s + 1 insolation

Ki s Si

Load PL

Diesel Engine

5 Td s + 1

Pd

Psys

1 Ms +D

Δfe

Ppv

PV inveter

PV array

* Pinv

Fuzzy based output power command generationsystem

Δfe

Fig. 2. Isolated power system model.

power by diesel-generator set, R is the droop and Ki is the integral control gain of speed governor, Tsm is the time constant of the valve actuator servomechanism, Td is the time constant of diesel engine, M is the inertia constant and D is the damping constant of the diesel-generator set, Dfe is the frequency deviation, PL is the AC load, and Psys is the PV-diesel hybrid system’s output power. 2.1. PV system characteristic and model As the design of the power converter and control system is significantly influenced by the PV module characteristics, these will be reviewed here briefly. The PV module is a nonlinear device and can be represented as a current source model, as shown in Fig. 3. The traditional IeV characteristics of a PV module, neglecting the internal series resistance, is given by the following equation [25]:


   qVo  1  Irsh Io ¼ Np Ig  Np Isat exp AKTmod

(1)

where Io and Vo are the output current and output voltage of the PV module, respectively, Ig is the generated current under a given insolation, Isat is the reverse saturation current, q is the charge of an electron, K is the Boltzmann’s constant, A is the ideality factor, Tmod is the temperature (K) of the PV module, Np is the number of cells in parallel, and Irsh is the current due to intrinsic shunt resistance of the PV module. The saturation current (Isat) of the PV module varies with temperature according to the following equation [25]:

 Isat ¼ Ior

Tmod Tr

3

 exp

  qEg 1 1  KTmod Tr Tmod

(2)

Io

Rs Ig

D

Id

R sh

Vo

I rsh

Fig. 3. Equivalent circuit of a PV module.

Ro

Ig ¼ Isc

Si þ It ðTmod  Tr Þ 1000

(3)

where Ior is the saturation current at Tr, Tr is the reference temperature (K), Eg is the band gap energy, It is the short-circuit current temperature coefficient, and Isc is the short-circuit current of PV module. The current due to the shunt resistance is given by the following equation [25]:

Irsh ¼

Vo Ns Rsh

(4)

where Ns is the number of cells in series and Rsh is the internal shunt resistance of the PV module. For the PV module, equations (1)e(4) are used in the development of MATLAB/SIMULINK based computer simulations. Fig. 4(a) and (b) shows the simulated ampereevolt and powerevolt curves for the PV module. Here, the discrete data points shown are taken from the manufacturer’s data sheet [26] for validating the model. From these curves, it is observed that the output characteristics of the PV array is nonlinear and is vitally affected by the variation of insolation. 2.2. Diesel generator model In Fig. 5, the standard model of the diesel generator and speed governor is illustrated in block diagram form. This model is widely used and describes well the dynamic behavior of small diesel generator sets, as it has been shown in [27]. The diesel engine and the valve actuator servomechanism are represented by first order lags, with time constants Td and Tsm, respectively. Parameters of the speed governor are the droop R and the integral control gain Ki. The objective of the integral control is to eliminate the steady state frequency error and in many cases (particularly in small and older units) may be absent. The actuator position limiter is ignored in the frequency domain analysis, where linearized models are used. Input to the model is the load demand PL, i.e. the output power of the electrical generator. Output is the generator speed, ud, which is equal (in per unit) to the electrical frequency of the system, ue. The derivation of the model equations is a straightforward and rather trivial procedure and for this reason it is omitted. The diesel engine must be able to follow the variation of loads and PV power. The size of frequency variation indicates how well the diesel and its governor maintain the balance of active power in the system, and the size of voltage variation indicates how well the gen-set and its voltage regulator maintain the balance of reactive power through the generator excitation. Under transient

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a

9 8

Module Current [A]

7

1000

W/m2

800

W/m2

600

W/m2

6 5 4 3

T=25 C

2 1 0

0

5

10

15

20

25

30

35

40

Module Volatge [v]

b

250 1000

W/m2

Module Power [W]

200 800

W/m2

150 W/m2

600 100 T=25 C

50

0

0

5

10

15

20

25

30

35

40

Module Volatge [v] Fig. 4. PV module characteristic curves. (a) Currentevoltage curves. (b) Powerevoltage curves. The discrete data points shown are taken from the manufacturer’s curves [26], and show excellent correspondence with the model.

conditions, the frequency and the voltage will not be absolutely constant because PV power and load variations change constantly.

losses of the inverter can be presented by internal AC resistance, which considers the switching losses and interconnection losses within the power conversion device.

2.3. Bi-directional inverter model The inverter is bi-directional, that is, it not only can supply AC power to the load but also can charge the battery (if connected with the proposed system) by rectifying the surplus power when the total supply power exceeds the load power. The bi-directional inverter is modeled by calculating the power conversion losses in dependence of the inverter Ac power, where the inverter DC power is expressed as

PINV;DC ¼ PINV;AC þ PINV;LOSS

(5)

For operation of the bi-directional inverter in rectifier-mode, both the inverter DC and AC power are negative, indicating a change of the directional power flow. The inverter loss consists of dynamic power conversion loss PLOSS,dyn and fixed losses. The dynamic I2R Δωd =Δωe 1 R

Ki s

Governor

1 Tsm s + 1

2.4. Fuzzy logic controller design Fuzzy logic has been used for decades in the fields of renewable energy [28e32]. The main purpose of this research is to develop a leveling strategy for PV output power fluctuations. Therefore, no new fuzzy logic development is presented here rather a simple active power control according to frequency deviation and isolation variations is presented by using fuzzy logic. In order to control the output power of PV system considering the power utility and insolation conditions, output power * is generated by output power command generation command Pinv system shown in Fig. 6. This command system consists mainly of two fuzzy controls. Fuzzy control is described by a set of “if-then” based fuzzy rules. Fuzzy control is effective when mathematical

Si

Diesel Engine

5 Td s + 1

Load PL Pd

Δf

Eq. (7) Eq. (6)

Si

Fuzzy I

Δf s

γ

1.0 ΖΟΗ

2 Eq. (11) σ

1 Ms +D

Fig. 5. Standard model of diesel generator and speed governor.

Fuzzy II

γΙΙ

u(k)

γ(k)

0.3 1 z

Fig. 6. Output power command generation system.

Eq. (16)

P*

M. Datta et al. / Renewable Energy 36 (2011) 1693e1703 Table 1 Fuzzy rules of fuzzy control I.

Table 2 Fuzzy rules of fuzzy control II.

Dfs Si

NB NB NS ZO PS PM PB

1697

Dfs

NB

NM

NS

ZO

PS

PM

PB

ZO PS PM PB PB PB PB

NS ZO PS PM PB PB PB

NM NS ZO PS PM PB PB

NB NM NS ZO PS PM PB

NB NB NM NS ZO PS PM

NB NB NB NM NS ZO PS

NB NB NB NB NM NS ZO

s2

NB NB NS ZO PS PM PB

NB

NM

NS

ZO

PS

PM

PB

PB PB PB PB PM PS ZO

PB PB PB PM PS ZO NS

PB PB PM PS ZO NS NM

PB PM PS ZO NS NM NB

PM PS ZO NS NM NB NB

PS ZO NS NM NB NB NB

ZO NS NM NB NB NB NB

NB ¼ Negative Big; NM ¼ Negative Medium; NS ¼ Negative Small; PB ¼ Positive Big; PM ¼ Positive Medium; PS ¼ Positive Small; ZO ¼ Zero.

NB ¼ Negative Big; NM ¼ Negative Medium; NS ¼ Negative Small; PB ¼ Positive Big; PM ¼ Positive Medium; PS ¼ Positive Small; ZO ¼ Zero.

expressions are difficult by inherent complexity, nonlinearity, or unclarity. Therefore, no deterministic model is required. First, fuzzy control I is explained. There are two inputs of fuzzy control I. One is absolute average of frequency deviation Dfs, and the other is average insolation Si . The former, which is an index to estimate power system condition, is expressed by

output power command. When frequency deviation Dfe is more than 0.2 Hz in certain times, fuzzy rules and membership functions that yield an output to decrease the frequency deviation are defined by trial-and-error. The i th of fuzzy rules is expressed as

Dfs ¼

1 T

Zt

jDfe jd t

x ¼ 1; 2; /; 7;

(6)

where t is the present time and T is the integral interval. Since absolute value of frequency deviation Dfe is used, absolute average of frequency deviation Dfs increases or decreases with the increase or decrease in frequency deviation Dfe. Therefore, equation (6) indicates frequency deviation quantitatively at any given time. Average insolation Si is defined by

1 T

y ¼ 1; 2; /; 7;

gI ¼

49 X

wi Zl

49 .X

i¼1

l ¼ 1; 2; /; 7

wi

(9)

i¼1

where wi denotes the grade for the antecedent and is obtained by

wi ¼ wDfs i wS i

Zt

(10)

i

Si d t

where wDfs i and wS i are the grades of antecedents for each rule. i Second, fuzzy control II is explained. Absolute average of frequency deviation Dfs and variance of insolation s2 are used as inputs of fuzzy control II, where variance of insolation s2 is expressed as

(7)

tT

where Si is instantaneous insolation. Fuzzy rules and membership functions of fuzzy control I are shown in Table 1 and in Fig. 7, respectively. Here, the output power control of PV system according to the power utility condition is accomplished by using the absolute average of frequency deviation Dfs as input of fuzzy control. It is undesirable to increase output power command of PV array considerably by insolation condition because probability of insolation decrease at short times is high. The important thing considered in this paper is the prevention of large frequency deviations. The frequency deviation Dfe of magnitude more than 0.2 Hz is prevented rather than increase of generated power of PV system. Thus, membership functions are decided so that the decreasing tendency is stronger than the increasing tendency of the

1

0

L

L

L

L

L

L

NB

NM

NS

ZO

PS

PM

s2 ¼

1

0

Zt

ðSi  Si Þ2 d t

(11)

Output power command that depends on power system condition rather than insolation condition is decided by using absolute average of frequency deviation Dfs as input of both fuzzy controls. In addition, when insolation fluctuations are large, the variance of insolation s2 is used as one of the inputs since the objective is to decrease frequency deviations. Fuzzy rules and membership M NB

1

Δ f [Hz]

0

M NM

100

S

Z NB

1 T

tT

L PB

0.015 0.0225 0.03 0.0375 0.045 0.0525 0.06

(8)

where Lx, My denote the antecedents and Zl are consequent part. Fuzzy control gI is calculated by

tT

Si ¼

Dfs is Lx and Si is My then gI is Zl

Rule i : if

250

Z NM

Z NS

Z ZO

Z PS

-0.03 -0.02

-0.01

00

0.01 0.015

Z PM

M NS

M ZO

M PS

400

550

750

Z PB

0.02 γ

Fig. 7. Membership functions of fuzzy control I.

Ι

M PM

850

M PB

1000 S (W/m2 )

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H NB

1

0

H NM

H NS

H PS

H ZO

H PM

0.015 0.0225 0.03 0.0375 0.045 0.0525

1

0

Q NB

H PB

0.06 Q NM

I NB

1

Δ f [Hz]

0

450

S

Q NS

-0.008 -0.006 -0.004

Q ZO

00

Q PS

I NM

2375 Q PM

0.006 0.008

I NS

I ZO

4300

6225

I PS

I PM

I PB

8150 10075 12000

2

2

σ (W/m )

Q PB

0.01 γ

ΙΙ

Fig. 8. Membership functions of fuzzy control II.

functions of fuzzy control II are shown in Table 2 and in Fig. 8, respectively. Set-up of fuzzy rules and parameters of membership functions are determined to prevent the boosting of frequency deviation. The o th of fuzzy rules is expressed as

Rule o : if

Dfs is Hc and s2 is Iz then gII is Qh

c ¼ 1; 2; /; 7;

z ¼ 1; 2; /; 7;

(12)

h ¼ 1; 2; /; 7

where Hc, Iz denote the antecedents and Ql are consequent part. Fuzzy control gII is defined by

gII ¼

49 X

wo Qh

49 .X

o¼1

wo

(13)

o¼1

where wo denotes the grade for the antecedent and is obtained by

wo ¼ wDfs o ws2 o

(14)

where wDfs o and ws2 o are the grades of antecedents for each rule. The fuzzy rules and membership functions presented in fuzzy control I and II are defined by trail-and-error. However, it is

possible to tune the parameters of controllers and membership functions of fuzzy control to achieve leveling and maximum acquisition of available PV power. A variety of methods have been proposed recently for tuning the fuzzy controller such as selftuning algorithm based on an experimental planning method [33], in which the scaling factors of optimal parameters can be determined efficiently according to the desired performance indexes, Taguchi tuning method [34], and tuning the membership functions [35,36]. Most of these methods need performance index. Two performance indexes can be made based on frequency deviation (tends to zero) and maximum PV power (tends to maximum). It will be easy to define performance indexes if 1 h ahead insolation and temperature prediction are possible. However, in the present literature, selection of scaling factors is still based on a trail-and-error method. As can be seen from Fig. 6, the discrete value u(k þ 1) is obtained by the sum of the outputs of fuzzy control I, gI, and fuzzy control II, gII through zero-order-hold, and the rate of current rated output power of PV array. Then, the new rate of rated output power of PV array g(k þ 1) becomes output power command by the following equation:

Fig. 9. A 30 min sample of insolation, load, and change of insolation.

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Linear output power command is obtained in each sampling time using the following equation:


 gðk þ 1Þ  gðkÞ * Pinv ¼ Prated gðkÞ þ f ðtÞ Ts * Pinv ¼ Prated gl :

(16) (17)

* to the Finally, equation (16) becomes output power command Pinv PV system, where Prated is the rated output power, Ts is the sampling time, f(t) is the periodic function such that f(t) ¼ t for 0 < t < Ts and gl is the linear output power command.

3. Simulation results In this paper, effectiveness of the proposed method to provide PV output power fluctuations smoothing is examined by simulation with the system model and parameters mentioned in [37e41]. In order to use the parameters of practical large PV system given in [40,41], the rated output power of the PV array is 225 kW. Simulation parameters of the power utility, the PV array, the power conversion system, and the diesel generator are shown in Table 1. Here, the integral time, T is 100 s and the sampling time, Ts to obtain discrete value of output power command is 10 s. The total simulation time is 30 min. Actual insolation and load data of a summer day of Miyako island [12,40,41], Okinawa, Japan are used for simulation. Insolation data are collected from The Japan Weather Association (JWA) [42] and load data are collected from The Okinawa Electric Power Company, Incorporated (OEPC) [43]. A 30 min sample data of the daily insolation and load are shown in Fig. 11. These insolation and load data are used for performance comparison of MPPT control [18], conventional control [21] and proposed control of PV-diesel hybrid system (Fig. 9). Fig. 10. Comparative simulation results with the MPPT control [18] and the proposed control.

3.1. Comparative simulation results with the MPPT control [18] and the proposed control

gðk þ 1Þ ¼ gðkÞ þ uðk þ 1Þ:

The comparative simulation results using the MPPT control [18] and the proposed control are shown in Fig. 10. Here, results obtained by the MPPT control and by the proposed control are shown by dotted line and solid line respectively. Output power of

(15)

Moreover, since the rate obtained by equation (15) changes step, it is necessary to convert it into a smooth output power command. Table 3 Simulation parameters. Parameters of PV module Cell Rated output power Open circuit voltage, Voc Short circuit current, Isc Shunt resistance, Rsh Series resistance, Rs

Polly-crystalline silicon 216 W (þ10%/5%) 36.50 V 8.10 A 50 U 5.0 mU

Parameters of small power system Inertia constant, M Damping constant, D Governor time constant, Tsm Diesel time constant, Td Speed regulation, R

1.450 3.047e-7 A  1.73e-3 A/ K 25  C 60 164 cm  99.40 cm  4.6 cm

Parameters of PV array 0.150 puMW s/Hz 0.008 puMW/Hz 0.10 s 5.0 s 2.5 Hz/puMW

Parameters of PV inverter Inverter power rating Nominal ac output voltage Nominal ac output frequency Maximum ac line current Maximum dc input voltage Maximum dc input current Efficiency

Diode Ideality factor, A Inverse diode saturation current, Ior S.C. current temperature coefficient, It Reference temperature, Tr Number of cells in series, Ns Dimensions

Rated output power Open circuit voltage, Voc Short circuit current Isc Number of module in series Number of module in parallel Total no. of cells

225 kW 584 V 526.50 16 65 62,400

Parameters of ESS converter 225 kW 480 V (3-phase) 50 Hz 271 A rms 600 V 781 A 94.5%

Charging input voltage Discharging input voltage Charging output voltage Discharging output voltage Charging current Discharging current Maximum charging current Maximum discharging current

Up to 750 V dc Up to 750 V dc Up to 750 V dc Up to 750 V dc 120 A dc 120 A dc 210 A dc 210 A dc

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Fig. 11. Inputs and outputs of fuzzy controls.

PV system Ppv is shown in Fig. 10(a). From Fig. 10(a), it is seen that significant amount of output power fluctuations are removed and PV power fluctuations leveling is achieved. Output power of diesel generator Pd is shown in Fig. 10(b). From Fig. 10(b), for the MPPT control, it is found that the diesel generator output power Pd

fluctuates highly in order to cancel out fluctuation of PV power Ppv, and load PL. On the other hand, for the proposed control, diesel generator output power Pd fluctuates less. This is because the PV output power is leveled and is controlled considering the frequency deviations of the power utility and insolation. Fig. 10(c) shows

M. Datta et al. / Renewable Energy 36 (2011) 1693e1703

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Fig. 12. Concept of a parallel PV-diesel hybrid system.

frequency deviation Dfe where frequency deviation produced with the MPPT control deviates more than 0.3 Hz frequently and the maximum frequency deviation Dfe is 0.61 Hz. Therefore, if the MPPT extracted PV power is fed to the isolated utility without leveling, large frequency deviations occurred. However, for the proposed method, frequency deviation does not deviate more than 0.2 Hz frequently. So, the proposed method is effective in reducing frequency deviation Table 3. For the proposed method, from t ¼ 100 s to t ¼ 550 s, the frequency deviation is low, and the output power increases to supply possible maximum power. From t ¼ 550 s to t ¼ 1050 s, the frequency deviation and the variation of load PL are high, output power first decreases, then remains steady to reduce the frequency deviation. From t ¼ 1050 s to t ¼ 1800 s, the frequency deviation and the variation of load PL is low, the output power command is increased first and then is controlled near or at maximum power point with leveling effect. So from the discussed simulation results, it can be said that output power control of PV system considering the power system condition and insolation variation is achieved by the proposed method. The variance of insolation s2 is shown in Fig. 11(a). The variance is very high as the insolation changes so rapidly. Fig. 11(b) shows absolute average of frequency deviation Dfs. Outputs of fuzzy control I, II and the rate of linear output power of PV array, gl, are shown in Fig. 11(c)e(e), respectively. It can be found that outputs of fuzzy control I, II change in response to each of the inputs which are absolute average of frequency deviation Dfs, average insolation Si , and variance of insolation s2. However, the sum of fuzzy control I, II is discretized, and this sum is smoothed by using equation (16).

3.2. Comparative simulation results with the conventional control [21] and the proposed control From Fig. 10(a), it is found that precious PV power loss is experienced by using the proposed method as the MPPT extracted Fig. 14. Comparative simulation results with the conventional control [21] and the proposed control.

Δfd = Δfe 1 R

1 Tsm s + 1

Ki s

5 Td s + 1

Pd

Psys

1 Ms +D

Ppv

insolation Si

PA PV array

Voc Isc

Boost chopper

Two-stage MPPT Battery Bank

Load PL

Diesel Engine

Governor

Bi-directional converter

* Pmax

PESS

Pmax

Bi-directional PV inveter PESS * Pinv

Fuzzy based output power command generation and ESS charging/discharging * PESS system

Fig. 13. Modified isolated power system model.

Δfe Si

Δfe

PV power was not supplied to the isolated utility. Moreover, the diesel power needed with the proposed method is higher than the diesel power needed with MPPT control. It will boost CO2 emission. To rectify these problems, an ESS will be used with the proposed method. Figs. 1 and 2 are modified and shown in Figs. 12 and 13, respectively. This is actually a parallel PV-diesel hybrid power system consisted of a diesel-generator set, a PV generator equipped with a maximum power point tracking (MPPT) control [44], an ESS, a bi-directional inverter and AC load. In Fig. 13, Voc is the opencircuit voltage of the PV array, Isc is the short-circuit current of the * is the MPPT command power, Pmax is the MPPT PV array, Pmax * is the ESS command power, PESS is the ESS output power, PESS

M. Datta et al. / Renewable Energy 36 (2011) 1693e1703

Δfe [Hz]

1702

0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4

Diesel

MPPT

0

200

400

600

800

1000

1200

1400

1600

1800

Time, t [s] Fig. 15. Frequency deviations comparison of diesel only system and diesel-PV system.

output power. In this modified configuration, the MPPT extracted PV power is given to the DC bus, the bi-directional inverter is controlled according to the proposed method described earlier, the battery is charged when MPPT extracted PV power (Pmax) is greater * ), than the command PV power of the bi-directional inverter (Pinv the battery is in neutral mode when MPPT extracted PV power is equal to the command PV power of the bi-directional inverter, and the battery is discharged in night or when no insolation is available. The comparative simulation results using the conventional control [21] and the proposed control are shown in Fig. 14. Here, results obtained by the conventional control and by the proposed control are shown by dotted line and solid line respectively. Output power of PV system Ppv is shown in Fig. 14(a). From Fig. 14(a), it is seen that significant amount of PV power fluctuations leveling is achieved by the proposed method compared to the conventional method. Output power of diesel generator Pd is shown in Fig. 14(b). From Fig. 14(b), it is found that the diesel-generated power required with the conventional control fluctuates more than the dieselgenerated power required with proposed control. Fig. 10(c) shows frequency deviation Dfe where frequency deviation produced with the conventional control do not deviate more than 0.2 Hz

Maximum energy function Emax , [MJ]

a

350 300 250

MPPT control Conventional control Proposed control

frequently. However, the frequency deviation is high compared to the proposed method. Fig. 14(d) shows the ESS charging discharging power. At the daytime, for the proposed method, ESS only experiences charging action as the PV power produced by the proposed control is less than MPPT extracted PV power. This charging power can be supplied at night. 3.3. Feasibility analysis During the nighttime, PV power will not be available; therefore, the diesel generator must regulate the power quality of the isolated power utility system. However, during the daytime, when the diesel generator in conjunction with the PV generator supplies the load, diesel generator alone cannot regulate the frequency deviations. Because, PV output power has fluctuating nature as the insolation varies quickly with time. Moreover, PV power is not controlled according to the load variation. The frequency deviations produced when only the diesel generator supplies the load and the frequency deviation produced when the diesel generator and the MPPT controlled PV generator supply the load are compared in Fig. 15. It has been seen that when only the diesel generator supplies the load, the frequency deviations are within the permissible limit of 0.2 Hz. On the other hand, when the diesel generator and the MPPT controlled PV generator supply the load, the frequency deviations are not within the permissible limit of 0.2 Hz. Therefore, the proposed method is practical considering this problem of frequency deviations.

200

3.4. Performance function of PV output power

150 100 50 0

0

200

400

600

800

1000 1200 1400 1600 1800

Performance of maximum PV output power and fluctuations leveling is represented as maximum energy function Emax and leveling function Plevel, which are expressed as

Time t, s

Leveling function P level , [MJ]

b

Zt Emax ¼

3

(18)

 Zt  d PA ðtÞ    d t d t

(19)

0

MPPT control Conventional control Proposed control

2.5 2

Plevel ¼

0

1.5 1 0.5 0

PA ðtÞd t

0

200

400

600

800

1000 1200 1400 1600 1800

Time t, s Fig. 16. Performance function of PV output power. (a) Maximum energy function. (b) Leveling function.

If Emax in equation (18) is large, the efficiency of the system for maximizing energy capture has a good performance. If Plevel in equation (19) is small, the PV output power fluctuation is small such that leveling of output power has a good performance. Fig. 16(a) and (b) shows the validity of the proposed method (without battery) in terms of performance function. The maximum energy function Emax in Fig. 16(a) of the proposed method is reduced by approximately 16.67% and 10.14% compared to the maximum energy function of the MPPT control and the conventional control respectively. IF the battery is considered, then no

M. Datta et al. / Renewable Energy 36 (2011) 1693e1703

power reduction will be experienced. Since the purpose of this study is the leveling of PV power fluctuations by intelligent, a drop in the PV power cannot be avoided when no battery is considered. The MPPT tracked PV power always fluctuates with insolation variation. So it can be said that the proposed method is a good trade off between maximizing energy capture and PV output power fluctuations leveling. From Fig. 16(b), it is found that leveling function Plevel of the proposed method drops to about 80% and 40% compared to the MPPT control and the conventional control respectively. Hence, a good leveling performance is achieved by the proposed method. 4. Conclusion This paper presents output power leveling of large PV generator in a PV-diesel hybrid power system considering power system condition and insolation condition. Output power command is defined by fuzzy control which has three inputs of absolute average of frequency deviation, average insolation, and variance of insolation. Set-up of fuzzy rules and parameters of membership functions are determined to prevent the increase of frequency deviation. The PV system is controlled to level the PV power fluctuations so as not to harmfully influence the isolated power system at times of large frequency deviation. On the other hand, at times of small frequency deviation, the proposed system can operate to increase generated power near available maximum power. According to the performance function of the PV output power, the proposed method is a fine trade off between maximizing energy capture and smoothing PV output power fluctuations. Acknowledgement This research work is partially supported by the Marubun Research Promotion Foundation, Japan and the Power Academy, Japan. References [1] Bakirtzis AG, Gavanidou ES. Optimum operation of a small autonomous system with unconventional energy sources. Electr Power Syst Res 1992;23:93e102. [2] MacGill IF, Watt ME. Field experience with RAPS and grid supply for residential power in remote areas. In: Proc. Solar 94 ANZSES, Sydney, 1994. [3] Wichert B. PV-Diesel hybrid energy systems for remote area power generation-a review of current practice and future developments. Renew Sustain Energ Rev 1997;1(3):209e28. [4] Olken M. The rise of the sun. IEEE Power Energy 2009;7(3):4e104. [5] Hara R, Kita H, Tanabe T, Sugihara H, Kuwayama A, Miwa S. Testing the technologies. IEEE Power Energy 2009;7(3):77e85. [6] Yanagawa S, Kato T, Wu K, Tabata A, Suzuoki Y. Evaluation of LFC capacity for output fluctuation of photovoltaic generation systems based on multi-point observation of insolation. In: Proc. IEEE PES SM, Vancouver, Canada, July 2001, p. 1652e1657. [7] Woyte A, Thong VV, Belmans R, Nijs J. Voltage fluctuations on distribution level introduced by photovoltaic systems. IEEE Trans Energy Convers 2006;21 (1):202e9. [8] Asano H, Yajima K, Kaya A. Influence of photovoltaic power generation of required capacity for load frequency control. IEEE Trans Energy Convers 1996;11(1):188e93. [9] Wiemken E, Beyer HG, Heydenreich W, Kiefer K. Power characteristics of PV ensembles: experiences from the combined power production of 100 grid connected PV systems distributed over the area of Germany. Solar Energy 2001;70(6):513e8. [10] Ina N, Yanagawa S, Kato T, Suzuoki Y. Smoothing of PV system output by tuning MPPT control. Electr Eng Jpn 2005;152(2):10e7. [11] Dalton GJ, Lockington DA, Baldock TE. Feasibility analysis of stand-alone renewable energy supply options for a large hotel. Renewable Energy 2008;33:1475e90. [12] Senjyu T, Hayashi D, Yona A, Urasaki N, Funabashi T. Optimal configuration of power generating systems in isolated island with renewable energy. Renewable Energy 2007;32:1917e33.

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