A Novel Operation and Control Strategy for a Standalone Hybrid Renewable Power System

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402 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 4, NO. 2, APRIL 2013

A Novel Operation and Control Strategy for aStandalone Hybrid Renewable Power System

A. M. Osman Haruni, Student Member, IEEE, Michael Negnevitsky, Senior Member, IEEE,Md. Enamul Haque, Senior Member, IEEE, and Ameen Gargoom, Member, IEEE

AbstractThis paper proposes a novel operation and cont rol

strategy for a renewable hybrid power system for a standalone op-eration. The proposed hybrid system consists of a wind turbine, afuel cell, an electrolyzer, a battery storage unit, and a set of loads.The overall control strategy is based on a two-level structure. Thetop level is the energy management and power regulation system.

Depending on wind and load conditions, this s ystem generates ref-

erence dynamic operating points to low level individual subsys-tems. The energy management and power regulation system alsocontrols the load scheduling operation during unfavorable windconditions under inadequate energy storage in order to avoid a

system blackout. Based on the reference dynamic operating pointsof the individual subsystems, the local controllers control the wind

turbine, fuel cell, electrolyzer, and battery storage units. The pro-posed control system is implemented in MATLAB Simpower soft-ware and tested for various wind and load conditions. Results arepresented and discussed.

IndexTermsBattery storage, electrolyzer, energy management

and power regulation system, fuel cell, load management, stand-alone hybrid power system, wind energy conversion system.

I. INTRODUCTION

I N remote and isolated areas, diesel generators are com-monly used to provide electricity because grid connectionsare often neither available nor economically viable. Diesel gen-

erators are popular in remote area power system applications for

their reliability, low installation cost, ease of starting, compact

power density, and portability [1], [2]. However, diesel gener-

ators are becoming expensive to run; they also need frequent

maintenance. Most importantly, they pollute the environment.

Hybrid power generation systems that combine different

renewable energy sources and energy storage systems offer an

environmentally friendly alternative for standalone operations.

However, there are several challenges for the hybrid power

system. Appropriate control and coordination strategies among

various elements of the hybrid system are required so it can de-

liver required power. Renewable-energy-based hybrid systems

must also be also reliable and cost-effective.

Manuscript received September 23, 2011; revised August 05, 2012; acceptedOctober 06, 2012. Date of publication December 21, 2012; date of current ver-sion March 18, 2013.

The authors are with the Centre for Renewable Energy and PowerSystems (CREPS), University of Tasmania, Hobart, Tasmania 7001, Aus-tralia (e-mail:[email protected]; [email protected];[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSTE.2012.2225455

Renewable energy sources such as solar, wind, bio-mass,

geothermal, and tidal energy can play an important role as a

primary power source for the hybrid power generation system.

However, favorable conditions for renewable energy are nec-

essary as the cost of electricity is heavily dependent on local

weather patterns. As an example, a case study conducted in

Tunisia [3] demonstrated that a hybrid system based on the solar

power and battery storage can offer a cost-effective solution

compared with the diesel-generator-based standalone power

system. In favorable wind conditions, wind-turbine-basedhybrid power systems can offer a cheaper option to a solar-en-

ergy-based hybrid renewable power supply [4].

Due to the intermittent nature of the wind, the instantaneous

power extracted from the wind turbines often does not match the

instantaneous load demand. As a result, energy storage systems

are essential for continuous and reliable operation [5][11],

[13], [14]. Energy storage devices provide transient stability

during sudden wind and load variations [5][7]. Moreover, they

are very useful for load-leveling applications [8], [9]. Different

types of energy storage systems such as pumped hydro, com-

pressed air energy storage, flywheel energy storage, thermal,

hydrogen, batteries, superconducting magnetic energy storage,and super-capacitors are used in different applications for

different purposes. Pumped hydro and compressed air energy

storages are low cost options [8]. However, they have lower

efficiencies. Moreover, pumped hydro is mostly dependent on

the geographical location. Flywheel energy storage, batteries,

superconducting magnetic energy storage, and super-capacitors

have a higher energy density and a very fast time response [8],

[9]. As a result, they can support a sudden change in power

demand and provide better transient stability. On the other

hand, fuel cells and electrolyzers have higher power density

with slower time responses [9][11], [13], [14]. Therefore,

they are moresuitable for long-term load leveling applications.

Considering the application of energy storage systems in the

wind-turbine-based hybrid power system, a combination of the

fuel cell, electrolyzer, and battery can represent the most suit-

able option. First, excess power from wind can either be stored

in the battery storage system or used to generate hydrogen by

the electrolyzer. Second, batteries respond very quickly, which

ensures better stability of the hybrid system during transient

periods caused by sudden changes of wind and load. Third,

this combination can improve the efficiency of the system by

sharing power so as to allow the operation of a fuel cell in a

high efficiency region.

Recent studies on hybrid power systems focus on various

issues such as the size and cost optimization [15][17], power

1949-3029/$31.00 2012 IEEE



HARUNIet al.: NOVEL OPERATION AND CONTROL STRATEGY 403

Fig. 1. Structure of the proposed hybrid power generation system.

management [14], [19][21], power quality [21], and reliability

[22]. However, little attention has been paid to the load man-

agement under inadequate energy reserves during low wind

conditions, which may lead to a system blackout. In fact, load

management strategies are essential for providing the required

power supply during these conditions.

In this paper, a standalone hybrid power supply scheme

suitable for remote and isolated communities is presented.

The system consists of an interior permanent magnet (IPM)

synchronous generator-based wind turbine, battery storage,

fuel cell, and electrolyzer unit as shown in Fig. 1.

This paper proposes a novel energy management and power

regulation system (EMPRS). This ensures coordinated control

and flexible energy management of the hybrid power system.

The EMPRS generates the dynamic operating point for each

subsystem of the hybrid power system which ensures: 1) op-

timum utilization of wind resources, 2) proper management of

energy storage resources, and 3) continuous operation of the hy-

brid power system under unfavorable wind and insufficient en-ergy storage conditions.

Based on the dynamic operating points of each subsystem

generated by EMPRS, the wind energy conversion system is

controlled by a rectifier in order to: 1) extract optimum power by

regulating the rotor speed, and 2) ensure efficient operation of

the IPM synchronous generator by regulating the - and -axes

components of the stator current. The fuel cell is controlled

by the hydrogen flow regulator and boost converter. The elec-

trolyzer is controlled by a buck converter. The battery storage

is controlled by a bidirectional dcdc converter.

The performance of the proposed control system is tested

under different wind and load conditions, and results validatethe effectiveness of the system.

II. SYSTEMCOMPONENTS ANDCONTROLLERSMODELING

A. Wind Energy Conversion System (WECS) Modeling

1) Wind Turbine Model for Optimum Energy Extraction: The

aerodynamic rotor power from wind can be expressed as

[23]

(1)

where is the air density, is the rotor swept area, is the

wind speed, and are the cut-in and cut-off wind speed,

respectively, and is the power coefficient which is a function

of the speed ratio and the pitch angle .

The speed ratio of the wind turbine can be defined as

(2)

where is the rotor speed, and is the radius of rotor.

From (1) and (2), for a particular wind speed, the output

power is proportional to the rotor speed and can be expressed

as

(3)

where .

From (3), optimum aerodynamic rotor power from the wind

turbine can be extracted by controlling the rotor speed . For

a particular wind speed, the optimum power is given as [23]

(4)

where .Fig. 2 demonstrates the power generated by a turbine as a

function of the rotor speed for different wind speeds. The op-

timum power extraction from the wind refers to extracting the

necessary power under varying wind speed conditions. As an

example, for a particular wind speed , the optimum power

is generated by keeping the rotor speed equal to either

or . However, as is higher than the base rotor speed,

the control system has to choose the rotor speed . If the wind

speed drops from to , the control system setsthe rotor speed

to to extract the required power.

2) IPM Synchronous Generator Model: From Fig. 3, the

voltage equations of the IPM synchronous generator in the -

and -axes are expressed as follows [24]:

(5)

(6)

where and are the - and -axes components of the stator

voltage, respectively; is the stator resistance; and are

the - and -axes components of the stator current, respectively;

is the frequency; and is theflux linkage.

The torque equation of the IPM synchronous generator can

be expressed as follows [24]:

(7)




Fig. 2. Wind turbine characteristic curve.

Fig. 3. (a) The - and (b) -axes circuits of the IPM synchronous generator.

where is the number of pole pairs, and is the generated

torque of the IPM synchronous generator.

From (7), the -axis stator current component for constant

torque can be expressed as a function of the -axis stator current

component

(8)

The maximum efficiency of the IPM synchronous generator

can be achieved by minimizingthe copper and core losses. From

Fig. 3, the copper and the core losses for the IPM

synchronous generator can be determined as follows [25]:

(9)

(10)

where is the core loss component.

The output power from the generator can be given as

(11)

The optimum value of can be determined from the output

power versus the -axis stator current curve based

on (5)(11), as shown in Fig. 4. From Fig. 4, the optimum value

of the - axis current component is chosen such that the output

power of the IPM synchronous generator is maximized. The

corresponding value of can be obtained from (8).

3) Machine Side Converter Controller Design: The machine

side converter shown in Fig. 5 consists of three controllers

working on the principle based on (5)(11). In the first stage,

a controller is used to regulate the speed by controlling the

Fig. 4. The -axis current versus output electric power.

Fig. 5. Machine side converter controller.

torque. In the second stage, two controllers are used to reg-

ulate the - and -axes currents under specific torque or power

conditions in order to minimize losses.

B. Fuel Cell Model and Control

The model used in the paper is based on the dynamic proton

exchange membrane fuel cell model (PEMFC) discussed in [10]

and [12].

This model is based on a relationship between the Nernst

voltage and the average magnitude of the fuel cell stack voltage

[10]

(12)

where is the fuel cell voltage, is the number of fuelcells connected in series, is the Nernst voltage, and is

the irreversible voltage losses.

The Nernst voltage developed in the fuel cell is defined as

follows [10]:

(13)

where is the voltage associated with the reaction free energy,

is the universal gas constant, is the Faradays constant,

is the fuel cell absolute temperature, the partial pressure of

hydrogen in the anode, and are the partial pressure

of oxygen and water available in the cathode, respectively. ,




Fig. 6. (a) and (b)powercurrentcharacteristics of a fuel cell.

, and can be expressed using the following differential

equations [10]:

(14)

where and are the molar follow of hydrogen

and oxygen, respectively; , , and are constants;

is a constant, which can be defined by the relationship be-

tween the rate of reactant hydrogen and is the fuel

cell current [10]

(15)

The output voltage of a fuel cell at normal operating con-

ditions is determined by the irreversible voltage loss ,

which can be classified into three types: the activation voltage

loss , ohmic voltage loss , and concentration

voltage loss [12]. The and powercurrentchar-

acteristics of a fuel cell can be seen in Fig. 6.

The output power of a fuel cell is determined as follows:

(16)

In order to design a control strategy for the fuel cell, the hy-

drogen flow has to be regulated to achieve the output power

based on (12)(16). Moreover, as the fuel cell voltage varies

according to the dynamic operating point, as shown in Fig. 6, a

controlled boost converter is used to interface the fuel cell with

the dc link of the system. The fuel cell controller is shown inFig. 7.

C. Electrolyzer Model and Control

The electrolyzer consumes the electric power to produce hy-

drogen. The alkaline electrolyzer model is used in the applica-

tion. The voltage drop across each electrolyzer cell is given by

[8], [33]

(17)

where is the voltage drop across the electrolyzer, is the

thermodynamic cell voltage, is the electrolyzer tempera-

ture, and are parameters for the electrolyzer over voltage,

Fig. 7. Fuel cell controller.

Fig. 8. characteristics of an electrolyzer.

are parameters of ohmic resistance, is the electrolyzer

current, and is the area of electrode.

The total voltage drop across the electrolyzer is de-

fined as [8]

(18)

where is the number of cells.

The total power consumption of the electrolyzer is given as

(19)

The electrical characteristics of the electrolyzer depend on the

voltage, current, and temperature. The nonlinear relationship of

the electrolyzer cell voltage and current at a given temperature

is shown in Fig. 8.

The hydrogen production rate can be expressed as

a function of applied current as follows [8], [14]:

(20)

where is the Faradays constant, is the current density, and

is a function of the current density and temperature.

In normal operating conditions, the hydrogen outlet rateshould be equal to the hydrogen production rate so that the

pressure and stored hydrogen quantity in the cathode can be

maintained as constant. Based on the ideal gas law, the resultant

pressure of hydrogen can be written as [8], [14]

(21)

where is the cathode volume, is the partial pres-

sure of hydrogen in the cathode, isthe molar hydrogen

outflow rate to hydrogen tank, and is the ideal gas constant.

In order to controlthe powerflow in the electrolyzer, the input

current has to be controlled. A buck converter is used to regulate

the powerflow in the electrolyzer by regulating the electrolyzer

current based on (17)(21) as shown in Fig. 9.




Fig. 9. Electrolyzer controller.

D. Compressor and Tank Model

The relationship between the molar flow rate

from the electrolyzer and the compressor power is

given as follows according to the polytrophic model [8], [14]:

where

(22)

where is the compression efficiency, and is the poly-

tropic work, is the polytrophic coefficient, and is the

pressure of storage tank.

Hydrogen storage is the difference between the

hydrogen produced by the electrolyzer and the hy-

drogen used by the fuel cell as

(23)

The pressure of stored hydrogen in the hydrogen tank

can be derived as

(24)

where is the hydrogen storage tank volume, is the ideal

gas constant, and is the temperature of the tank.

E. Battery Storage System Modeling and Control

Among different battery technologies, Liion batteries repre-

sent a suitable option for fuel-cell-based hybrid energy storage

systems due to their high energy density and efficiency, light

weight, and good life cycle [26].

The generic Liion battery model is used [27]. The battery

state of charge (SOC) is an indication of the energy reserve and

is expressed as follows [27]:

(25)

where is the battery current, and is the battery capacity.

The battery controller is a bidirectional dcdc converter that

stabilizes the dc link voltage during sudden wind and load

changes. The controller is shown in Fig. 10.

F. Output Voltage and Frequency Controller

In a standalone power system, various loads (linear, non-

linear, balance, and unbalanced three phase loads) can be con-

Fig. 10. Battery charger/discharger controller.

Fig. 11. Load side inverter controller.

nected. As a result, in the proposed system, we use a three

phase four wire inverter with split capacitor as shown in Fig. 11.

The controller of the inverter compares the -, -, and -axes

components of the output voltage with their reference values

. Based on the error signal, a set of

PI controllers generates appropriate signals to the PWM signal

generator.

III. ENERGYMANAGEMENT AND POWERREGULATIONSYSTEM(EMPRS)

The EMPRS ensures a continuous operation of the hybrid

system via the coordination of the wind turbine, energy storage

system, and loads. The EMPRS works in three stages. In the

first stage, the EMPRS predicts the wind and load profile for a

specified period of time. In the second stage, based on the wind

and load profile and the status of energy reserve, it schedules the

maximum load that can be supplied by the system. In the third

stage, it determines the operating condition of each subsystem.

A. Wind and Load Prediction

An accurate wind and load prediction is a key factor toensuring a robust performance of the EMPRS. In several

studies conducted earlier, it was demonstrated that an accurate

forecasting system can be developed for the short-term (up to

15 min) forecasting of wind and load conditions [28][32]. An

integration of wind and load forecasting in the EMPRS will

allow the implementation of the load curtailment in advance,

thus avoiding system blackouts, as will be demonstrated in the

following.

B. Load Scheduling

Based on the wind and load prediction, the power balance

equation of the hybrid system can be expressed as follows:

(26)




where and are the load demand and battery power, re-

spectively.

From (26), during high wind conditions, the excess power

is consumed by the electrolyzer and battery storage as

follows:

(27)

During low wind conditions, the power deficit from

the wind can be supplied by the fuel cell and battery storage as

follows:

(28)

The energy balance equation can be obtained by integrating

(26)

(29)

where is the total energy produced by the wind energy con-

version system, is the total energy consumed by the load,

is the total energy supplied by the battery, is the total

energy consumed by the electrolyzer, and is the total en-

ergy supplied by the fuel cell.

However, in real hybrid system operation conditions, (29) is

only valid for the following conditions:

1) If , the excess energy is stored in the hybrid

system.

2) If , the energy deficit from wind is balanced

by the fuel cell and battery storage. In this condition, the

fuel cell and battery storage can produce required power

provided the hydrogen storage and SOC of the battery areavailable. The system may experience blackout conditions

if the energy reserves are not sufficient to meet the load

demand.

The robustness of EMPRS depends on its prediction accu-

racy. Although it has been demonstrated that the short-term pre-

diction error can be as low as 1% for normal conditions, this can

increase under sudden wind guests or sudden changes of large

industrial loads. As a result, sufficient reserves must be allocated

to offset the prediction error of up to 5%.

Moreover, the unlikely event of no wind conditions may

occur and continue for a relatively long period. During this

period, the hybrid system will totally rely solely on storedenergy. Thus, energy reserves that can serve high priority loads

for a sufficient period have to be preserved. As a result, in this

study a portion of energy from the battery storage system is

dedicated as a reserve. For normal operation conditions, the

SOC range of the battery is assumed to be between 75% and

95%. For emergency operating conditions, the SOC is allowed

to be as low as 40%. However, it has to be noted that these

ranges are case specific. A number of constrains such as his-

toric wind and load conditions, economic sizing of the storage

system, security, and reliability issues of the system can affect

the optimum range of SOC for both normal and emergency

operating conditions. In this study, the selected ranges are used

for demonstration only. A wider allowable SOC range can be

considered for specific study.

Fig. 12. Load management algorithm.

Considering practical operational aspects during low windconditions, management of energy reserves of the hybrid system

is vital. In order to ensure the system operation, the load curtail-

ment is adopted. The load management algorithm is shown in

Fig. 12. It is described as follows:

1) Calculate the total energy difference between the

wind energy and the load demand

(30)

2) If , check SOC of the battery and the status of

the hydrogen storage. If , no load curtailment

is required. If and extra energy from wind is

not sufficient to bring the SOCto 75%, theload curtailmentis executed. In this condition, the EMPRS allows the SOC

of the battery to be 75%.

3) If , check SOC of the battery. If

and the fuel cell and the battery have enough

reserves to supply the energy deficit, no load curtailment

is needed. For other conditions, load curtailment is imple-

mented. In this condition, the EMPRS allows the SOC of

the battery to be 75%.

To implement the load curtailment, loads are divided ac-

cording to their priority. The loads such as hospitals, police

stations, etc. can be considered as high priority or emergency

loads. The hybrid system has to fulfil the power demand ofthese loads at any condition. On the other hand, some lighting

loads, washing machine loads, etc. can be considered as low

priority loads and can be switched off when required.

C. Operation Point of Each Subsystem

The EMPRS generates the operation point based on the

current wind and load conditions and actual limitations of each

subsystem. Limitations include the maximum and minimum

power of the fuel cell for operation in

the ohmic region, the maximum charging or discharging power

, and the maximum and minimum state of charge

of the battery storage system. In this paper,

the allowable range of SOC is assumed to be from 40% to 95%.

For normal operating conditions, the allowable SOC of the




TABLE IDYNAMIC OPERATINGPOINT

battery storage system is assumed at 75% to 95%. However, for

emergency operation, the SOC is allowed to be as low as 40%.

The limitations also include the maximum and minimum power

consumption of the electrolyzer , and

the maximum pressure of the hydrogen cylinder .

The summery dynamic operating points are outlined in Table I.

Details of the dynamic operating points are described as fol-

lows:

Mode 1: High wind condition ,

and .In this mode, the wind turbine extracts optimum power that

is equal to the load demand. The electrolyzer and the battery

cannot consume any power because the hydrogen tank pressure

and the SOC of the battery have reached their maximum limits.

Mode 2: High wind condition and

.

In this mode, the wind turbine extracts optimum power that

is equal to the load demand plus the capacity of the battery and

electrolyzer.

Mode 3: High wind condition and

.

In this condition, the wind turbine extracts the maximum

power. The excessive power from the wind is consumed by the

electrolyzer and battery.

Mode 4: Low wind condition ,

, and .


available power. The deficit power is provided by the battery

and fuel cell during transient conditions. Once the system

reaches a steady-state condition, deficit power is supplied by

the fuel cell only.


, and .


power. The fuel cell provides its maximum power and the

battery storage provides the remainder.


, and .


power and the battery storage provides the necessary power as

the power deficit is lower than the minimum power limit of the

fuel cell.

Mode 7: Low wind condition , low {\rm SOC}

, and .In this condition, the wind turbine extracts the maximum

power. The battery cannot provide power as the SOC is close

to 75%, which is the minimum limit for normal operation con-

ditions. The fuel cell operates in its ohmic zone. Since power

produced by the fuel cell is higher than , the extra power is

used to charge the battery. When the SOC of the battery storage

system reaches about , the operation of the system

moves toMode 6.

Mode 8: No wind condition.

In this condition, the wind turbine is unable to produce any

power. The fuel cell and the battery provide the required power.

Depending on the severity of this condition, the EMPRS allowsthe SOC of the battery storage to drop as low as 40%.

IV. SIMULATION, RESULTS, AND DISCUSSION

Simulation studies are conducted to evaluate the performance

of the proposed system under varying wind and load conditions.

A. Performance of the Local Controllers Under Different

Wind and Loading Conditions

In this section, the performance of the local controllers is

evaluated under varying wind and load conditions. The parame-

ters of the wind turbine, IPM synchronous generator, and energy

storage system are shown in Table II.1) Dynamic Operating Points of Subsystems Under Different

Wind and Loading Conditions: Figs. 13(a) and (b) show hy-

pothetical wind and scheduled load profiles, respectively. The

EMPRS determines the operating mode according to current

wind and load conditions, and available energy reserves as dis-

cussed in Section III. Fig. 14(a) shows the power generation

from the wind energy conversion system. Fig. 14(b) shows the

electrolyzer, fuel cell, and battery power. Figs. 14(c) and (d)

show the status of the hydrogen storage and the SOC of the bat-

tery, respectively. Fig. 14(e) shows the operation mode of the

hybrid power system.

From Figs. 13(b) and 14(a), the initial load demand is 0.5kVA

(0.48 kW, 0.05 kVAR), and the maximum available power after

considering conversion losses is about 0.9 kW. In this condi-




TABLE IISIMULATIONPARAMETERS

Fig. 13. Wind and load profiles. (a) Wind speed; (b) real power and reactivepower.

tion, the EMPRS operates inMode 3as the excess power from

the wind can be stored in the battery and can be consumed by

the electrolyzer to produce hydrogen. At the time of 20 s, the

SOC of the battery reaches its maximum limit (about 95%). As

a result, the battery cannot consume any more power. In this

Fig. 14. Power balance and operation mode sequence of hybrid system.(a) Power from wind energy conversion system. (b) Dynamic interaction ofenergy storage system. (c) Hydrogen pressure (pu). (d) SOC (%). (e) Modesof operation.

condition, the EMPRS goes to Mode 2, which allows the wind

energy conversion system to extract optimum power (0.85 kW)

by controlling the rotor speed. In this condition, the excess wind

power is consumed by the electrolyzer.

From Fig. 13(b), at the time of 40 s, the load increases from

0.5 to 0.65 kVA (0.6 kW). At this time, the pressure of the hy-drogen storage system reaches its maximum value. As a result,

the electrolyzer cannot consume any extra power from the wind

energy conversion system. In this condition, the EMPRS goes to

Mode 1, which allows the wind energy conversion system to ex-

tract optimum power (0.65 kW) by controlling the rotor speed.

From Fig. 13(b), at the time of 50 s, the load increases from

0.65 to 1.5 kVA (1.4 kW). In this situation, as the maximum

available energy from the wind turbine is less than the load de-

mand and the power deficit is within the fuel cell power gener-

ation limit, the EMPRS operates the system in Mode 4.

However, at the time of 60 s, the wind speed drops from 12

to 8 m/s. As the deficit power is about 1.2 kW, which is within

the combined limit of the fuel cell and the battery, the EMPRS

operates the system in Mode 5.




At the time of 70 s, the load decreases from 1.5 kVA (1.4 kW)

to 1.0 kVA (0.97 kW). In this condition, the EMPRS operates

the system inMode 4as the deficit power is within the limit of

the fuel cell.

At the time of 80 s, the wind speed increases from 8 to

11.7 m/s. In this condition, as the deficit power (0.170 KW) is

less than the minimum power limit of the fuel cell, the EMPRS

operates inMode 6.

At the time of 100 s, a 0.1-kW single-phase load is connected

to the system. At this time, as the SOC of the battery approaches

its minimum limit, the EMPRS runs the system in Mode 7. In

this condition, the fuel cell can produce sufficient power, which

is used to balance the deficit power and to increase the SOC of

the battery.

At the time of 110 s, the wind speed drops and the wind tur-

bine cannot produce any power. The load power increased from

1.1 to 1.3 kVAr (1.25 kW). In this condition, the fuel cell and

battery storage system supply the power. As this condition is

considered as an emergency condition, the EMPRS allows the

SOC of battery storage system as low as 40%.2) Performance of the Wind Energy Control System:

Fig. 15(a) shows power extracted from the wind turbine and

power losses. The efficiency of the wind energy conversion

system is shown in Fig. 15(b). From Fig. 15(c), it can be seen

that the wind energy control system extracts the optimum

power by regulating the rotor speed of the IPM synchronous

generator. From Figs. 15(d) and (e), we can see that the wind

energy control system maintains an efficient operation of the

IPM synchronous generator by controlling the - and -axes

components of the stator current.

3) Performance of the Electrolyzer Controller: The buck

converter is used to control the electrolyzer power flow. Theelectrolyzer voltage and current are shown in Figs. 16(a) and (b),

respectively.

4) Performance of the Fuel Cell Controller: The boost con-

verter is used to control the dc-link voltage and powerflow of

the fuel cell. The fuel cell voltage and current are shown in

Figs. 17(a) and (b), respectively.

5) Performance of the Bidirectional Battery Controller: As

shown in Fig. 14(b), the bidirectional dcdc converter controls

the battery charging/discharging power in order to ensure the

power balance and transient stability of the system.

6) Dynamic Interaction of the Energy Storage System: The

dynamic interaction of each energy storage device is shown inFig. 14(b). It can be seen that under any change of wind speed or

load demand, the battery storage system provides or consumes

the transient power owing the faster dynamics of its fuel cell

counterpart.

7) Performance of the Inverter Controller: Fig. 18 shows

the output voltage and frequency is regulated by the load side

inverter controller under varying wind and load conditions.

Fig. 19 shows the dynamic performance of the inverter at

70 s, when the load decreases from 1.5 to 1.0 kVA. Fig. 20

demonstrates the dynamic performance of the inverter at 100 s,

when a load of 0.1 kW is connected to phase B. From Fig. 20, it

is revealed that the current in phase B increases while the phase

voltages remain constant. Fig. 21 demonstrates the active and

reactive power demand response of the inverter.

Fig. 15. Performance of the wind energy conversion system controller.( a) Powe r fr om wind turbine and power conver sion loss .

(b) Efficiency (%). (c) Rotor speed . (d) -axis current . (e) -axiscurrent .

Fig. 16. Electrolyzer voltage and current. (a) Electrolyzer voltage. (b) Elec-trolyzer current.

B. Load Management of the System Under Low Wind

Conditions

The EMPRS performance is evaluated under realistic wind

and load scenarios. Let us assume the proposed hybrid shown

in Fig. 1 operates in an isolated area. The average load demand

is assumed to be about 0.6 kW and peak load demand is 1 kW.

A case study is performed under low wind conditions when the


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Fig. 17. Fuelcell voltage andcurrent.(a) Fuel cell voltage. (b)Fuel cell current.

Fig. 18. System voltage and frequency. (a) System voltage. (b) System fre-quency.

Fig. 19. Inverter response during load change at 70 s when the load decreased

from 1.5 to 1.0 kVA: (a) voltage and (b) current responses.

load peak occurs. Let us assume the energy reserve is low and

cannot support the system without load curtailment.

The wind speed profile is shown in Fig. 22(a). A hypothetical

wind prediction is assumed with an error of 5%. The corre-

sponding power extracted from the wind turbine is shown in

Fig. 22(b).

The load profile is shown in Fig. 23(a). All loads are divided

into four categories. Type is the load with the highest pri-

ority that constitutes about 25%30% of the total load. Type

is a high priority load that constitute about 25%30% of

the total load. Type is the load with a medium priority

that constitutes about 25%30% of the total load. Type

Fig. 20. Inverter response at 100 s when a load is connected to a single phase:(a) voltage and (b) current responses.

Fig. 21. Real power and reactive power responses.

Fig. 22. Wind speed profile and generated wind power. (a) Wind speed.(b) Power from wind turbine.

has the lowest priority. The load curtailment operation is shown

in Fig. 23(b).

Figs. 24(a) and (b) show the operation of the elec-trolyzer/fuel cell and associated hydrogen storage, respectively.

Figs. 25(a) and (b) show the operation of the battery storage

and associated SOC, respectively. From Figs. 2225, it can be

seen that the EMPRS can operate the system without any load

curtailment up to 2:00 hours. However, from 2:00 to 3:24 hours,

the EMPRS curtails the load with the lowest priority

as the stored energy is not sufficient to provide the required

deficit. At 3:24 hours, the hydrogen storage runs out. Because

the energy stored in the battery is not sufficient to run the

system in the normal operating condition, the EMPRS goes

to the emergency mode (Mode 8). As a result, from 3:24 to

5:00 hours, the EMPRS curtails loads in

order to prevent the system blackout. During this period, the

EMPRS uses the emergency reserves of the battery to supply




Fig. 23. Load condition. (a) Load profile. (b) Load management.

Fig. 24. Fuel cell, electrolyzer power, and hydrogen status. (a) Power fromelectrolyzer/fuel cell. (b) Hydrogen storage.

Fig. 25. Battery power and SOC. (a) Battery power. (b) SOC.

power only to the loads with the highest priority. The wind

returns at 3:48 hour. However, the EMPRS still considers the

situation as an emergency because the SOC of the battery is too

low for the normal operation. During this period, if the wind

power exceeds the emergency load, the excess power is used

to charge the battery.

V. CONCLUSION

A novel operation and control strategy for a hybrid power

system with energy storage for a standalone operation is pro-

posed. The performance of the proposed control strategy is eval-

uated under different wind and load conditions. From the sim-

ulation studies, it is revealed that the machine side converter is

able to extract the optimum power. It is also able to operate theIPM synchronous generator with maximum efficiency. The bat-

tery storage system is successfully controlled by a bidirectional

converter. The fuel cell and electrolyzer are controlled using

boost and buck converters, respectively. The overall coordina-

tion of the wind turbine, fuel cell, battery storage system, elec-

trolyzer, and loads is done by the developed EMPRS. The ob-

vious advantage of the EMPRS is that it can prevent the system

from blackouts in the event of low wind conditions or inade-

quate energy reserves.

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A. M. Osman Haruni (S10) received the B.Sc. degree in electrical and elec-tronic engineering from Bangladesh University of Engineering and Technologyin 2003, and the M.Eng.Sc. degree from University of Tasmania in 2008. He iscurrently working toward the Ph.D. degree at the University of Tasmania, Aus-tralia.

He worked as a engineer with Siemens Bangladesh Limited in the PowerTransmission and Distribution (PTD-MV) Department from 2004 to 2006. Hisresearch interests include power electronics control for renewable energy tech-nologies, load modeling and its application in power system operation and con-trol, and use of artificial intelligence in power systems.

Michael Negnevitsky(M95SM07) received the B.S.E.E. (Hons.) and Ph.D.degrees from the Byelorussian University of Technology, Minsk, Belarus, in1978 and 1983, respectively.

Currently, he is Chair Professor in Power Engineering and Computational In-telligence and Director of the Centre for Renewable Energy and Power Systemsat the University of Tasmania, Hobart, Australia. From 1984 to 1991, he was aSenior Research Fellow and Senior Lecturer in the Department of Electrical En-gineering, Byelorussian University of Technology. After arriving in Australia,he was with Monash University, Melbourne, Australia. His interests are powersystem analysis, power quality, and intelligent systems applications in powersystems.

Dr. Negnevitsky is a Chartered Professional Engineer, Fellow of the Insti-tution of Engineers Australia, Member of CIGRE AP C4 (System TechnicalPerformance), Member of CIGRE AP C6 (Distribution Systems and DispersedGeneration), Australian Technical Committee, and Member of CIGRE WorkingGroup JWG C1/C2/C6.18 (Coping with Limits for Very High Penetrations ofRenewable Energy), International Technical Committee.

Md. Enamul Haque (M97SM10) graduated in electrical and electronicengineering from Rajshahi University of Engineering Technology [formerly,Bangladesh Institute of Technology (BIT)], Rajshahi, Bangladesh, in 1995.He received the M.Engg. degree in electrical engineering from UniversityTechnology Malaysia in 1998, and the Ph.D. degree in electrical engineeringfrom University of New South Wales, Sydney, Australia, in 2002.

He has worked as an Assistant Professor for King Saud University, SaudiArabia, and United Arab Emirates University for four years. He is currentlyworking as a Lecturer in renewable energy and power system with the School ofEngineering, University of Tasmania, Australia. His research interests includesmart energy systems, control and grid integration of renewable energy sourcesand energy storage system, micro grid system with hybrid wind/solar/fuel cellsystems, power electronics applications in smart-grid, micro-grid and powersystem applications.

Ameen Gargoom(M08) received the B.Sc., M.Sc. (with honours), and Ph.D.degrees in 1994, 2001, 2007, respectively, all in electrical power engineering.

He worked as a consultant engineer with Al-Emara Co. for Engineering Con-sultants, Libya, for six years before joining the University of Garyounis in 2001as an Associate Lecturer. In 2008, he joined Tasmania University as a ResearchFellow. Currently he is working as a Lecturer with the School of Engineering,University of Tasmania, Australia. His research interests include power elec-

tronics control for renewable energy technologies and smart grid systems, newtechniques for power quality monitoring and classification, and the applicationof signals processing techniques in power systems.

A Novel Operation and Control Strategy for a Standalone Hybrid Renewable Power System

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