ICSET 2008

Abstract—In this paper a microgrid consisting of wind turbine

generators, aqua electrolyzer, fuel cell and diesel generator is examined. The wind generator is controlled using a constant power strategy, to reduce the power fluctuation due to wind speed variations. On the other hand power produced from all the other distributed generation (DG) sources are regulated by simple PI controllers. As the balance between the load and generation changes, the power system frequency deviates. In order to reduce these frequency deviations, the PI controllers, used to regulate the power flow from DGs, are tuned through trial and error method. The simulation results show that the employed PI controllers for the DGs perform satisfactorily in a range of operating conditions to enable automatic generation control.

Index Terms—Distributed generation, power balance, fuel cell, wind turbine generator, diesel generator and frequency deviation.

I. INTRODUCTION

The energy crisis in the power sector has led to difficulties in meeting the increasing power demand. Distributed generation (DG) is increasingly being used to meet these energy needs. DG uses small electric power generation systems located near consumers and load centers. Distributed generation provides reliable electric power. In addition, it also allows businesses to save on electricity costs by using their units during high peak demand periods when power is most expensive. This technology offer comparative advantages to large and centralized plants in terms of efficiency, reliability and security. Growing DGs can improve the quality of atmosphere and reduce the green house effect. Hydrogen fuel cells, photovoltaic cells are some of the environmental friendly generation systems used in DGs. DG covers a broad range of technologies including many renewable sources of energy that provide small scale power at the sites close to the consumers, such as highly efficient combined heat and power plants (CHP plants), back up and peak load systems for increasing capacity. These technologies offer new market opportunities and enhanced industrial competitiveness. On site production minimizes the transmission and distribution losses and costs. Hence, DG helps to bypass congestion in the existing transmission grids.

B. Santosh Kumar, S.Mishra and N.Senroy are with the Department of electrical Engg.IIT Delhi.

Several types of small scale generation systems can be used for DG based micro grids. These include diesel generator, wind energy systems and fuel cells. Fuel cell is an electrochemical device, which converts chemical energy of fuel to electricity by combining gaseous hydrogen with air in the absence of combustion. Wind speed varies continuously with time. This uncertainty results in variation of the output power leading to deviation in frequency of the power system. To overcome this problem, in this paper a doubly fed induction generator (DFIG) employing constant power strategy based wind turbine is proposed so that it can smooth out some power fluctuation arising out of wind speed variations [1].

As the loads on the small scale power system vary; the frequency will change unless the load and generation are again balanced. Hence, there is a need to control the power produced so that the frequency attains the desired value.

In this paper we proposed a proportional plus integral controller to regulate the power output according to the frequency error. The system consists of wind turbine generators, diesel generators, aqua electrolyzer (AE) and fuel cells. Approximately constant power from the wind turbine generators is obtained by using constant power smoothing strategy [1].The generated hydrogen from the aqua electrolyzer is used as fuel for the fuel cell. The sum of the output powers of the wind turbine generators, diesel generators and fuel cells are used to supply the load as well as the AE.

II. SYSTEM MODEL

The proposed microgrid comprises of DFIG based wind turbine generator, fuel cell, diesel generator and AE. The AE is employed to convert the excess generated energy from the wind turbine generator into hydrogen used as fuel by fuel cells. The varying output of wind turbine generator due to fluctuation of wind speed is smoothed using a constant power smoothing strategy [1].The net power available to the load is determined from the sum of the powers from wind turbine generator, diesel generator, and fuel cell minus the input power to the AE.

In this paper simplified models with first order approximation are used as transfer functions for the fuel cell, diesel generator, and AE.The proposed system is shown in the Fig. 1. Mathematical models of wind turbine generator, diesel generator, fuel cell are discussed in the next section.

AGC FOR DISTRIBUTED GENERATION B. Santosh Kumar, S. Mishra, Senior Member, IEEE, N. Senroy, Member, IEEE,

89978-1-4244-1888-6/08/$25.00 c© 2008 IEEE

Fig .1 Configuration of distributed generation system, AE (Aqua electrolyzer), FC (fuel cell), DEG (Diesel Engine Generator), PS (Power System).

A. Wind speed modeling The wind speed is uncertain and is related to its past values.

Thus, the wind speed can be modeled using an autoregressive and moving average time series model [2].

1 1 2 2 . . . .t t t n t n ty y y yφ φ φ σ− − −= + + + +

mtntt −−− −−−− σθσθσθ ....2211 (1)

Where iφ (i = 1,2,n ), jθ ( j= 1,2…m) and tσ are the auto

regressive parameters, moving average parameters and a normal white noise process with zero mean respectively. The simulated wind speed tsω can be calculated using the following equation s t = t + tyt (2) Where tμ and tα are the average wind speed and standard deviation respectively.

B. Characteristics of wind turbine power The wind turbine converts kinetic energy of the wind into

mechanical energy by decelerating the air mass and feeds to the generator as a mechanical power, where fraction of mechanical energy is converted into electrical energy. The power coefficient is a function of both tip speed ratio and blade pitch angle .The tip speed ratio, which is defined as the ratio of speed at the blade tip to the wind speed, can be given as [3].

VRωλ = (3)

Where R radius of blade

rotational speed of blades V velocity of wind

The power developed by wind turbines are given by [3]

312 pP AV Cρ= (4)

Where ρ is the density of air, A is the swept area of the blade, Cp is the power coefficient and P is the output power of the turbine.

C. Constant power strategy The variation in wind speed is shown in Fig 2(a). Constant

power strategy is used to smooth the output power fluctuations before feeding to the generation system. The output of wind generator under constant power strategy is shown in Fig 2(b). The constant power strategy is obtained by creating a look up table in the control block of DFIG. By loading generator power refP versus speed ( mω ) curves into the look up table,

approximately constant power can be obtained. In the constant power strategy, refP is a function of mω [1] i.e.

omref pP =)(ω (5)

Where oP is average wind power, mω is shaft speed referred to the generator side of gear box.

0 50 100 1504

6

8

10

12

14

Time (sec)

Win

d sp

eed

in m

/s

(a)

0 50 100 150

0.58

0.6

0.62

0.64

Time (sec)

Win

d po

wer

in p

u

(b)

Fig.2.Simulation results of: (a).Variation of wind speed in m/s (b). Output wind power using constant power strategy.

D. Aqua electrolyzer Part of the generated power from the wind turbine generator

(WTG) is sent to the AE to produce hydrogen for fuel cell [4]. The decomposition of water into hydrogen and oxygen can be achieved by passing the electric current between the two electrodes separated by aqueous electrolyte [5]. The transfer function of first order AE is given by

( )AE

AEAE ST

KSG

+=

1 (6)

90

Where AEK and AET are the time constant and the gain of aqua electrolyzer respectively. As the AE consists of several power converters, we assumed that the time constant is very small [5]. The values of time constant and gain are 0.2sec and 1 respectively.

E. Fuel cell Fuel cells are the promising technologies to meet the energy

crisis in near future. The efficiency of fuel cells is nearly twice the efficiency of internal combustion engines. Fuel cells directly convert fuel and oxidant into electricity through electrochemical process. Fuel cell generators are of high order models and have non linearity. However when the time frame of analysis is large it can be approximated with a first order model. Hence in this paper it has been represented by the following transfer function.

( )FC

FCFC ST

KSG

+=

1 (7)

Where, FCT is the time constant of fuel cell. Time constant of the fuel cell is determined from the simulation results in [5].The values of time constant and gain are 4 sec and 1 respectively.

F. Diesel generator Diesel generators can follow the load demand variations by

means of their speed and power control mechanisms [6]. When power demand fluctuates, the diesel generator varies its output via fuel regulation through its governor. On the other hand since this is a synchronous generator, its output voltage can be regulated by controlling the excitation. In this paper the diesel generator is represented with a first order transfer function.

( )DE

DEDE ST

KSG

+=

1 (8)

Where, DET s the time constant of the diesel generator. The gain and time constants of diesel generator (DEG) are consider as 1 and 2 sec respectively.

G. Power and frequency deviation In a power system, if the balance between the generation

and load changes, the output frequency changes (either increasing or decreasing) depending on the domination of generation or load. The power deviation is the difference between the power generation GP and the power demand LP .

LGe PPP −=Δ (9) Due to time delay between the system frequency deviation and power deviation, the transfer function for system frequency variation to per unit power deviation are given by

DMS

Kf+

=Δ (10)

Where, K is the system frequency character constant. M and D are the inertia constant and damping constant respectively of power system. In this study D and M is chosen as 0.2 and 0.012 respectively [7].

.

III. SIMULATION RESULTS

This section presents the time domain performance of the proposed distributed generation system. The employed parameters for the studied case and the associated block diagram are shown in Fig.1 and listed in Table. I. We have chosen a simulation interval of 150 s for load perturbation

In all the cases, constant wind power of approximately 0.6 pu obtained from the DFIG, through constant power strategy is fed to the system along with the other sources. Load demand is constant at a value of 1 pu till 70 sec, after which a sudden change in a load is initiated. The deviation in frequency due to load change is controlled using the PI controller. The controller parameters are chosen based on trial and error method. The deviation in load is automatically adjusted by fuel cell and diesel generator through these controllers since the wind generator is under constant power strategy.

Case1: Load increased from 1 pu to 1.2 pu Sudden increase of load demand from 1 pu to 1.2 pu is

under taken at 70 sec. This change in load demand is met by fuel cell and diesel generator. The PI controllers are tuned in such a way to reduce the frequency deviation. Minor deviation of frequency from standard value is due to the fluctuation in output wind power, as the other renewable energy sources such as fuel cell, DEG cannot respond instanteously to small changes of wind power around 0.6 pu .

As the sudden change in load (increase) causes deviation in power balance, the controllers are adjusted in such a way that the load is met by fuel cell and diesel generator. Fig .3(d) and 3(e) show that when the load changes suddenly, the power outputs of the fuel cell and diesel generators are also increased to around 0.33 pu and 0.41 pu from 0.23 pu and 0.27 pu respectively. Frequency deviation of system, error in supply demand, output of aqua electrolyzer and the total power generation are shown in Fig.3(a), Fig.3(b), Fig.3(c) and Fig.3(f) respectively. Finally the frequency settles around a steady state value.

0 50 100 150-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

Time (sec)

Fre

quen

cy d

evia

tion

of P

ower

Sys

tem

in H

z

(a)

91

0 50 100 150-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

Time (sec)

Erro

r in

suup

ly d

eman

d in

pu

(b)

0 50 100 1500.08

0.1

0.12

0.14

0.16

Time (sec)

Out

put o

f aqu

a el

ectro

lyze

r in

pu

(c)

0 50 100 1500.2

0.25

0.3

0.35

0.4

Time (sec)

Out

put o

f fue

l cel

l in

pu

(d)

0 50 100 1500.25

0.3

0.35

0.4

0.45

Time (sec)

Out

put o

f DEG

in p

u

(e)

0 50 100 1500.95

1

1.05

1.1

1.15

1.2

1.25

Time (sec)

Tota

l out

put p

ower

in p

u

(f)

Fig.3. Simulation results of the proposed system for an increase in load: (a).frequency deviation of power system. (b) Error in supply demand. (c) Output from AE (d) power generated by fuel cell. (e) Power generated by diesel generator. (f) Total power generated (all are in pu).

TABLE I

Integral Gain Proportional Gain Aqua

Electrolyzer 0.01 0.01

Fuel cell 0.05 0.02

Diesel Generator

0.03 0.01

Case 2: Load decreased from 1 pu to 0.8 pu:

When the load reduces from 1pu to 0.8 Pu, frequency deviation of system, error in supply demand, output of aqua electrolyzer and the total power generation are shown in Fig.4 (a), Fig.4 (b), Fig.4(c) and Fig.4 (f) respectively.

During load change, frequency deviation is controlled by changing the generations of fuel cell and diesel generator. Generations produced by fuel cell and diesel generator decreases from 0.23 pu and 0.26 pu to 0.12 pu and 0.14 pu (approximately) respectively as shown in Fig.4 (d) and Fig.4 (e).

0 50 100 150-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Time (sec)

Freq

uenc

y de

viat

ion

of P

ower

Sys

tem

in H

z

(a)

92

0 50 100 150-0.05

0

0.05

0.1

0.15

0.2

Time (sec)

Erro

r in

supp

ly d

eman

d in

pu

(b)

0 50 100 1500.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Time (sec)

Out

put o

f aqu

a el

ectro

lyze

r in

pu

(c)

0 50 100 1500.05

0.1

0.15

0.2

0.25

Time (sec)

Out

put o

f fue

l cel

l in

pu

(d)

0 50 100 1500.1

0.15

0.2

0.25

0.3

0.35

Time (sec)

Out

put o

f DEG

in p

u

(e)

0 50 100 1500.7

0.8

0.9

1

1.1

Time (sec)

Tota

l out

put p

ower

in p

u

(f) Fig.4. Simulation results of the proposed system for a decrease in load :( a) Frequency deviation of power systems. (b) Error in supply demand. (c) Output from AE (d) power generated by fuel cell. (e) Power generated by diesel generator. (f) Total power generated (all are in pu)

IV. CONCLUSION

The microgrid system consisting of wind turbine generator, aqua electrolyzer, fuel cell and diesel generator was simulated. All the DG sources were represented as first order transfer functions. A system that controls the frequency through AGC was proposed. PI controllers used for AGC are installed before each generator, so that they can control deviation in load and generation and maintains the frequency at desired value.

V. REFERENCES

[1] C. Luo and B. T. Ooi, “strategies to smooth power fluctuation of wind Turbine generators IEEE Trans. Energy Convers., vol. 22, no. 2, Jun.2007

[2] Peng Wang and Roy Billinton ,”Reliabilty Benefit analysis of adding WTG to a distribution system”, IEEE Trans. Energy Convers., vol. 16, no. 2 Jun. 2001.

[3] Muljadi, E., McKenna H.E., "Power Quality Issues in a Hybrid Power System," Proceedings of the IEEE Industry Applications Society Conference, Chicago (CD ROM version), September 30 - October 4, 2001

[4] D.Lee and Li Wang, ”Small Signal Stability analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy Storage system time domain simulations”,IEEE Trans. Energy Convers.,vol.23,no.1, March.2008

[5] D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, ‘‘Development of a Stack simulation model for control study on direct reforming molten Carbonate fuel cell power plant,’’ IEEE Trans. Energy Convers., vol. 14, no. 4, pp.1651---1657, Dec. 1999.

[6] S. Dokopoulos, A. C. Saramourtsis, and A. G. Bakirtzis, ‘‘Prediction And evaluation of the performance of wind-diesel systems ,IEEE Trans. Energy Convers., vol. 11, no. 2, pp. 385---393, Jun. 1996.

[7] T. Senjyo, T. Nakaji, K. Uezato, and T. Funabashi, ‘‘A hybrid power system using alternative energy facilities in isolated island,’’ IEEE Trans. Energy Convers., vol. 20, no. 2, pp. 406---414, Jun. 2005.

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VI. BIOGRAPHIES

B.Santosh Kumar obtained his B.Tech from JNTU University, Hyderabad, India. He is currently pursuing his M.Tech in power systems in the department of electrical engineering, Indian Institute of Technology, New Delhi.

S. Mishra received BE degree from University College of Engineering, Burla, Orissa, India and M.E. and PhD degree from Regional Engineering College, Rourkela, Orissa, India, in 1990 and 1992 and 2000 respectively. In 1992, he joined the department of Electrical Engineering, University College of Engineering, Burla, as a lecturer and subsequently became a reader in 2001. Presently he is a faculty at the department of Electrical Engineering, Indian Institute of Technology, Delhi, India. He has been honored with many prestigious awards such as INSA Young Scientist Medal-2002, INAE Young Engineer's

award-2002, recognition as DST Young Scientist 2001-2002, etc. His interests are in fuzzy logic and ANN applications to power system control and power quality.

Nilanjan Senroy is an assistant professor in the department of electrical engineering at Indian Institute of Technology, New Delhi, India.His research interests are in the field of power system stability and control.

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