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THE NINTH INTERNATIONAL MIDDLE-EAST POWER SYSTEMS CONFERENCE, MEPCON’2003 Minoufiya University, Shebin El-Kom, Egypt, December 16-18,2003

DESIGN AND CONTROL STRATEGY OF UTILITY INTERFACED PV/WTG HYBRID SYSTEM H. H. El-Tamaly

Ali M. El-Tamaly

A. A. El-Baset Mohammed

Faculty of Engineering, Elminia University, Elminia, Egypt. almost triple by 2050. So, oil can only supply the world for up to 150 years [1]. Using renewable energy is one way to meet the future need. So, we can say that the renewable energy is the fuel of the future. In [2] a CAD/CAA tool for design of integrated hybrid power system and in [3] a design of a hybrid wind/photovoltaic based on political and social conditions and use trade-off /risk method. These researches [2,3] didn't take into account the most important technical parameters as maximum power point, optimum output voltage from PV and best coefficient of performance for WTG, which have been deeply investigated in this paper. This paper presents a complete design of a hybrid PVWES interconnected with UG. The design is based on energy balance, minimum price of generated kWh from the system under study and maximum power point tracking for PV and WTG by using expert neural network. The design methodology has been applied to Al'Zafrana site in Egypt as a case study.

Abstract This paper introduces a complete simulation program for optimal design of a hybrid photovoltaic wind energy system, PVWES, to be interconnected with utility grid, UG. A computer program has been designed to determine optimum number of PV modules and optimum number of wind turbine generator, WTG based on maximum power point by using neural network for the system under study. Many WTG and PV types have been introduced to computer program to chose the best type and the penetration ratio for WTG and PV modules. The computer program can completely design the hybrid system and determine the hourly system parameters as power flow, frequency of output power form WTG, and DC output voltage from PV modules. The decision from the computer program is based on minimum price of the generated kWh from the system. The system show the superiority of using hybrid PVWES. 1- Introduction

2- System Configuration

As energy demands around the world increase, the need for a renewable energy source that will not harm the environment has been increased. Some projections indicate that the global energy demand will

2- 1 Modeling Photovoltaic Array The electrical power generated and terminal voltage of PV module depend on solar radiation and ambient temperature. The

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equivalent electrical circuit describing the solar cells array used in the analysis is shown in Fig.1. The mathematical equation describing the I-V characteristics of a PV solar cells array is given by: [4]  

(

)

q V + I * R   v + I*R s s  −1 −   AKT R    sh

I =I ph −Ioexp

Rs

+ Iph

(

Fig.2 Power-voltage characteristics of a PV solar cells array Radiation

)

Temperature

where, Tr is the reference temperature, oK, Ego is the band-gap energy of the semiconductor used in solar cell array, KI is the short circuit current temperature coefficient, and, Ior is the saturation current at Tr, Amp. ph

= (I + K (T − 298)) * rad / 100 sc I

Calculate Max. Power

Time Bias Bias

Fig. 3. Configuration of Neural Network used to determine the MPP

(3)

2-2 Modeling of Wind Turbine

The output of the solar cell can be calculated by the following equation:

Pout = V* I

V

Fig.1 Equvilant circuit of PV solar cells array.

I = Ior T/T 3 expq*E / K 1/T −1/T (2) o r go I r  

I

Rsh

(1)

where, I is the output current, Amp, V is the output voltage, Volt, A is the ideality factor for p-n junction, T is the temperature, Kelvin, K is the Boltzman's constant in Joules per Kelvin, and, q is the charge of the electron in Coulombs. Io is the reverse saturation current and the generated current Iph of solar cell array vary with temperature according to the following equations: [4]

( )

I

The model used to calculate the output power generated by WTG is shown in Fig.4. Where Pw is the power in the wind, Pm is the turbine output power, Pt is the generated input power and Pe is the generator power output. Cp is the coefficient of performance of the turbine, ηm is the transmission efficiency and ηg is the generator efficiency.ωm is the turbine angular velocity.

(4)

by applying a computer program the powervoltage characteristics of a PV solar cells array to the measured data was calculated as shown in Fig.2 . From this figure it is found that the maximum power point, MPP vary with the change of radiation. Therefore in order to track the MPP form PV solar cell array at any solar radiation a three-layer neural network as shown in Fig.3 is applied [5].

Pw

Pm Wind Turbine Cp ωm

Transmission ηm

Pt ωt

generator ηg

Fig.4 Block diagram of wind electrical system.

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Pe ωg

From the block diagram shown in Fig.4, the electrical power output can be written as, P = C * η * η * 0 .5 * ρ * A * v 3 (5) e p m g The characteristic of power output from WTG can be described by the following formula [7]. 0  C *η *η *0.5*ρ*A*v3 p m g Pe(v) = 3 C *η *η *0.5*ρ*A*v p m g  0 

: vV  f 

Fig.6 shaft power output versus rotor speed 2-3 Load Characteristics It is assumed here that the load demand varies monthly. This means that each month has daily load curve different from other months. Therefore, there are twelve daily load curves through the year. Fig. 7 shows the load demand for January, April, July and October.

where: ν is the wind speed; A : effective swept area in m2.The coefficient of performance is the ratio of the mechanical power at the turbine shaft to wind power. This factor is not constant, but varies with tip speed ratio, λ as shown in Fig. 5 , where λ can be found as in Eqn. (7) [8,9]. λ=

rm *ω m v

(7)

where: rm is the radius of swept area in meters. in order to extract peak power, the rotor must be held at its optimal tip speed ratio as shown in Fig. 5, which means that the rotor angular velocity must vary proportional to wind speed. The relation between the electrical power output from WTG and rational speed, rpm for different wind speed is shown in Fig. 6.

Fig. 7 The hourly load demand for January, April, July and October. 3- PV/Wind System Sizing Fig. 8 shows the proposed integrated PVWES interconnected to UG. The power generated by PV solar cells array and WES at any time t can be expressed as follow: * P pv (t) * η P ( t ) = α* N pv pv g (8) + (1 − α) * N * Pw (t) * η w w where; α is the penetration ratio, Npv is number of photovoltaic module and Nw is number of WTG. In the case of power generated from PVWES is greater than the load demand

Cpm

λop

Fig. 5 Coefficient of performance Cp versus tip speed ratio.

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then the surplus power will be transmitted to UG. And in this case switch S1 and S3 shown in Fig. 8 will be ON and S2 will be OFF. In the case of the power generated from PVWES is lower than the load demand then the deficit power will be supplied from UG. So in this case S1 and S2 shown in Fig. 8 will be ON and S3 will be OFF.

Start Read Radiation, Wind speed, PV modules parameters, WTG parameters and laod demand Modification of radiation on surfaces tilted by monthly best tilt angle and wind speed at WTG hub height. Set Npv, Nw

For pen=0 : 1 : 0.1

Wind Speed Gear box

Generator

Calculate maximum power for one module based on MMP

AC/AC

Energy balance for PV to determine number of PV module

Radiation DC/DC

DC/AC S3

Calculate maximum power for one turbine based on MMP

S1 UG

S2

Energy balance for WTG to determine optimum number of WTG

Load

Fig.8 PVWES connected with utility grid case of Mode 1(Pg>PL).

Energy balance for Hybrid system to determine optimum number PV

The program for calculating optimum number of PV modules and optimum number of wind turbines was written in Matlab software. A flowchart of this program is shown in Fig. 9. The input data of this program consists of : 1- Hourly radiation. 2- Hourly wind speed. 3- Characteristic of PV module 4- Characteristic of wind turbine. 5- Hourly load demand. The output of this program are : 1- Optimum number of PV modules. 2- Optimum number of wind turbines 3- Cost of kWh generated from the hybrid system. 4- Monthly surplus power. Figs. 10a-10d show the daily load curve and output power from PV solar cells array and WTG for months January, April, July and October respectively.

Take a decision to select optimum number of PV and WTG for minimum cost End

Fig 9. Flowchart of the proposed computer program

(10-a)

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Table 1 surplus power and deficit power. Month January Feb. March April May June July August Sept. Oct. Nov. Dec.

(10-b)

Power MW -14.00 130.06 138.25 61.86 130.20 -46.62 -79.97 -141.88 59.06 -1.70 -210.51 -24.74

The operation of the three switch shown in Fig. 8 can be summarized as shown in Table 2. Table 2. Operational Modes of PVWTG hybrid system. Generated power vs. Mode S1 S2 S3 Load demand

(10-c )

1

ON

OFF

ON

P g > PL

2 3

ON OFF

ON OFF

OFF OFF

P g < PL Pg =0

4- Conclusion

This paper presents a technique to design and control a utility interfaced PV/WTG hybrid system. This technique use energy balance to reduce the cost of electricity while meeting the load demand. A controller that monitors the operation of interfaced system is designed. This controller indicates the available energy from hybrid system and utility grid in order to meet the load demand.

(10-d) Fig. 10 The hourly generated power from PV/WTG, surplus power and load demand (a)January. (b) April (c) July (d) Oct.

From the results obtained above, the following are the salient conclusions that can be drawn from this paper:

Table 1 shows the power purchased and sold from utility grid. Negative power purchased and positive power sold to UG.

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[4] Chihchiang Hua, Jongrong Lin and Chihming Shen, " Implementation of a DSP-controlled photovoltaic system with peak power tracking", IEEE Trans. Industrial electronics, Vol. 45, No. 1, pp.99-107, Feb. 1998. [5] R. Kyoungsoo, Saifur Rahman,"Twoloop controller for maximum performance of a grid-connected photovoltaic-fuel cell hybrid power plant", IEEE Trans. Energy conversion, Vol.13, pp.276-281, Sep. 1998. [6] T. Hiyama and K. Kitabayashi, " Neural Network Based Estimation of Maximum Power Generation From PV Module Using Environmental Information", IEEE Trans. Energy conversion, vol.12, pp.241-247, Sep. 1997. [7] Suresh H. Jangamshetti, and V. Guruprasada Rau, " Site matching of wind turbine generator : A case study", IEEE Trans. Energy conversion, Vol. 14, No. 4, pp.1537-1543, Dec. 1999. [8] Gary L. Johnson, "Wind energy systems", Book, Prentice-Hall.,1985 [9] A. M De Bore, S. Drouilhet, and V. Gevorgian, "A peak power tracker for small wind turbines in battery charging applications", IEEE Trans. Energy conversion, Vol. 14, No. 4, pp.16301635, Dec. 1999.

1- A simulation program for design and control of operation for hybrid power system based on minimum cost per kWh. 2- Purchased and sold power from UG have been calculated 3- Combination of wind turbine and photovoltaic perform better than either wind or solar alone. 4- Maximum power point for photovoltaic and wind are taken into account for calculation of sizing hybrid system. 5- control strategy of hybrid system have been studied. REFERENCES [1] International Energy agency report " Key Issues in Developing Renewables", 1997. [2] R. Chedid, and Saifur Rahman, "Unit sizing and control of hybrid wind-solar power systems", IEEE Trans. Energy conversion, Vol. 12, No. 1, pp.79-85, March 1997. [3] R. Chedid, and Saifur Rahman, " A decision support technique for the design of hybrid solar-wind power systems", IEEE Trans. Energy conversion, Vol. 13, No. 1, pp.76-83, March 1998.

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