dynamic modeling and operation strategy for a microgrid with wind and photovoltaic resources

IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 4, DECEMBER 2012 1867

Dynamic Modeling and Operation Strategy for aMicrogrid With Wind and Photovoltaic Resources

Sungwoo Bae, Student Member, IEEE, and Alexis Kwasinski, Member, IEEE

Abstract—This paper presents a dynamic modeling and controlstrategy for a sustainable microgrid primarily powered by windand solar energy. A current-source-interface multiple-input dc-dcconverter is used to integrate the renewable energy sources to themain dc bus. Potential suitable applications range from a com-munication site or a residential area. A direct-driven permanentmagnet synchronous wind generator is used with a variable speedcontrol method whose strategy is to capture the maximum windenergy below the rated wind speed. This study considers bothwind energy and solar irradiance changes in combination withload power variations. As a case study a 30-kW wind/solar hybridpower system dynamic model is explored. The examined dynamicsshows that the proposed power system is a feasible option for asustainable microgrid application.

Index Terms—Photovoltaic power systems, power conversion,power system modeling, wind power generation.

I. INTRODUCTION

T HIS paper presents a dynamic modeling and controlstrategy for a sustainable microgrid primarily powered

by wind and photovoltaic (PV) energy. These sources areintegrated into the main bus through a current-source-interface(CSI) multiple-input (MI) dc-dc converter. In order to providethe context for the discussion, the intended applications forthis microgrid are a communication site or a residential areapart of a future “smarter grid” [1]. The proposed microgrid isalso equipped with energy storage devices, such as batteries. Autility grid connection is provided in order to replenish energylevels in case of power shortage from the renewable energysources. Due to its diverse sources, power supply availability ofsuch system may exceed that of the grid [2]. Outage possibilityin this power system is close to zero because it is highly un-likely that all energy sources in this microgrid are unavailableat the same time. Moreover, the combination of wind generatorand PV modules with local energy storage devices may reducevulnerability to natural disasters [3], [4] because they do notrequire lifelines.Among the earlier work in the literature, the idea of devel-

oping a sustainable microgrid for telecommunication applica-tions using MI dc-dc converters was introduced in [4] and ex-

Manuscript received July 26, 2011; revised November 30, 2011; acceptedApril 24, 2012. Date of publication May 21, 2012; date of current version De-cember 28, 2012. Paper no. TSG-00266-2011.The authors are with the Department of Electrical and Computer Engi-

neering, The University of Texas at Austin, Austin, TX 78712 USA (e-mail:[email protected]; [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSG.2012.2198498

panded in [5]. A variant of such system with a different MI con-verter (MIC) topology was later on described in [6] suggesteda telecommunication power system in which a diesel generatorand an automatic transfer switch were replaced with fuel cellsand a micro-turbine using an MI dc-dc converter. The powersystems in [4]–[6] had the following advantages: 1) the useof the MIC reduces unnecessary redundancy of additional par-allel converters in each energy source, and 2) the investment inmicro-sources is recuperated because the energy sources in thispower system can be used during normal operation as well asgrid power outages [3]–[6]. Nevertheless, one issue with suchmicrogrid in [6] is that it still requires fuel for the local sourcesin normal operation.In addition, the daily complementary generation profiles of a

wind turbine and a PV module [7] have stimulated research onsimilar power systems with a dc link method rather than an accoupling method [8]–[11]. However, these similar power sys-tems in [8]–[11] combined renewable energy sources with par-allel single-input dc-dc converters which may lead to unnec-essary redundancy in power system components. This problemcan be resolved with an alternative combining method whichuses MI dc-dc converters previously proposed in [2], [4]–[6],[12]–[19]. In addition, an MI dc-dc converter had other advan-tages such as the possibility of decentralized control and mod-ularity. Despite these promising advantages, few studies seemto have explored dynamic modeling techniques for a wind/solarhybrid power system with MI dc-dc converters-in contrast tothose with parallel converters. Although the hybrid power sys-tems in [16] and [17] considered a wind generator as a localsource for an MIC, they did not consider wind energy variationsand ac system characteristics such as ac wind generators, localac load power variations, and interaction with the distributiongrid, which likely affect the controllability and performance ofthe microgrid.This paper presents a dynamic modeling and operation

strategy of a wind/solar hybrid power system with an MI dc-dcconverter in which wind energy changes, ac wind generator,and variations in the local ac load power and dispatch power tothe distribution grid are considered. A direct-driven permanentmagnet synchronous generator (PMSG) is used for the windgenerator model because a direct-driven PMSG has drawnattention for the residential-scale power level due to its gearlesssystem. In addition of wind energy variations, this study alsoconsiders the rapid changing solar irradiance that may happenduring the day and that affects generated power from PVmodules in the proposed power system. Moreover, the hereinproposed microgrid does not require any fuel for the local

1949-3053/$31.00 © 2012 IEEE

1868 IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 4, DECEMBER 2012

sources because it is powered by inherently self sustainableenergy sources. Thus, with enough local energy storage, it doesnot rely on lifelines—e.g., roads or pipes for fuel or natural gasdelivery—for operation, which makes it a truly self sustainablepower system ideal to provide power not only in normal condi-tions but also during extreme events when lifeline operation ispoor or not expected. Furthermore, the proposed power systemnot only can produce electricity from the renewable energysources but may also inject surplus power to the utility grid innormal operation.The rest of this paper is organized as follows. The overall

architecture of the proposed sustainable microgrid and themodeling components of this system are discussed in Section IIand III respectively. The control strategies of the proposedmicrogrid are discussed in Section IV, and simulation resultsand discussions about the proposed microgrid are included inSection V in order to illustrate the dynamics of the proposedsustainable power system. A case study of a 30-kW wind/solarhybrid microgrid model is developed and explored in Section V.Section VI concludes with the summary of findings.

II. PROPOSED SUSTAINABLE MICROGRID ARCHITECTURE

Fig. 1 shows the overall architecture of the proposed micro-grid with wind and PV resources. Its main energy sources, windand solar radiation, are transformed in a wind generator and PVmodules. In order to combine these energy sources, a CSI MIC,such as an MI Ćuk converter or an MI SEPIC converter [15]with a dc bus system, is used because a CSI MIC is more ef-fective for maximum power point (MPP) tracking in PV mod-ules and for the input current control method used in this mi-crogrid. MICs were chosen because they provide a cost-effec-tive and flexible method to interface various renewable energysources [4], [5], [15]. In addition, a dc power distribution systemis chosen because dc power systems may achieve higher avail-ability and energy efficiency in a simpler way than equivalentac power systems [2]. A voltage level of 380 V is consideredto be the main dc bus voltage in this power system because it ismore suitable for bidirectional power flow between the intendedpower system and the utility grid [5] and because it is the likelyvoltage to be chosen in a future standard for industrial appli-cations with dc distribution, such as in data centers. However,a three-phase rectifier in the wind generator may be requiredfor this dc distribution system because the output voltage of thewind generator is usually ac. As depicted in Fig. 1, an energystorage system (ESS) is also connected to the main dc bus inorder to overcome the intermittent properties of renewable en-ergy sources and to support local power production in an is-landed mode particularly during blackouts or natural disasters.Depending on applications, the various voltage levels of local

dc loads such as 48 V telecommunication power systems orplug-in electric vehicles can be accommodated through an addi-tional dc-dc converter as described in Fig. 1. The local ac loadswhose line-to-line voltage level is in this microgridcan also be connected with a PWM inverter and an LC filter usedto reduce harmonic voltages produced at the local acbus. As shown in Fig. 1, this local ac distribution system may

Fig. 1. Overall architecture of the proposed sustainable microgrid.

Fig. 2. Wind model used for the simulation study.

also be tied to the three-phase 2.4 kV distribution grid with athree-phase 240 V/2.4 kV transformer that also contributes tofilter harmonic content in the inverter output and to reduce filterneeds in the LC filter.

III. MODELING COMPONENTS OF THE PROPOSED MICROGRID

This section reviews major modeling components which areused in Section V in order to realize its system-wide microgridmodel.

A. Wind Model

This paper uses a wind model presented in [20] in order tosimulate the spatial effect of wind energy variations such asgusting, rapid ramp changes, and background noises. This windmodel is defined by

(1)

where is a constant wind velocity, is a gust windcomponent which can be implemented by a cosine function,

is a rampwind component used for mimicking rapid windchanges, and is background noises of wind. Fig. 2 showsthis wind model used for the simulation study which will be dis-cussed in Section V.

B. Wind Turbine Model

A wind turbine in the proposed microgrid simulation study ismodeled by an aerodynamic input torque which drives a windgenerator. In order to explain the wind turbine model here, themechanical power captured by the blades of a wind tur-bine is described as follows [21]:

(2)

where is a rotor power coefficient, is a blade pitch angle,is a tip-speed ratio (TSR), is an air density, is the radius

of a wind turbine blade, and is a wind speed. The rotorpower coefficient is defined by the fraction of the available windpower that can be transformed to the mechanical power by arotor [22]. This rotor power coefficient depends on the

BAE AND KWASINSKI: DYNAMIC MODELING AND OPERATION STRATEGY FOR A MICROGRID WITH WIND AND PV RESOURCES 1869

TABLE IPARAMETERS AND SPECIFICATIONS OF THE WIND TURBINE MODEL

blade aerodynamics, which is the function of a blade pitch angleand a TSR [9], [21]. The type of a wind turbine rotor

may also be another factor affecting the rotor power coefficient. However, the of [21] in which a general blade type

was assumed is used in this study for the sake of simplicity [9]

(3)

The TSR can be defined as the function of a wind speed[9], [21] written as

(4)

where is the rotor speed of a wind turbine. Then, from (2),(4), and considering that , the aerodynamic inputtorque bywhich awind generator is driven can be obtainedas follows [9]:

(5)

The wind turbine in the simulation study is modeled by (5) inwhich the input variables are the wind turbine rotor speedand the TSR that can be calculated with (4). The parametersof the investigated wind turbine model in this paper are shownin Table I. According to (3) and (5), the aerodynamic torque ismaximized at a given wind speed when the pitch angle of a blade

is 0 . Therefore, a constant pitch angle is used inthis study as shown in Table I.

C. Direct-Driven PMSG

The wind generator considered here is a gearless di-rect-driven PSMG. This PMSG does not require frequentmechanical maintenance because it does not use gears be-tween wind blades and the generator. Another advantage ofthe direct-driven PMSG is that a permanent magnet elimi-nates the dc excitation circuit that may complicate the controlhardware [23]. Table II shows the specifications of the di-rect-driven PMSG model used in the simulation study. For thesimulation study, the internal model of a PMSG in MATLABSimulink/Simpowersystems is used with the specificationsprovided in Table II.

D. PV System Model

This study uses the PV model that is depicted in Fig. 3 andwas proposed in [24] because it is suitable for simulating prac-tical PV systems which are composed of numerous PV mod-ules and because it only requires a few parameters, such asthe number of PV modules, PV array open-circuit voltage and

TABLE IISPECIFICATIONS OF THE DIRECT-DRIVEN PMSG MODEL

Fig. 3. Circuit-based PV model [24] used in the simulation study.

short-circuit current [24]. Moreover, this model can representsolar irradiance and temperature changes which may happencommonly during the day [24]. The detailed discussions of thisPV model are out of the scope of this paper; however, a readermay refer to [24] for explanation of such model derivations. Therated power of the PV system in this paper is 10 kW, which iscomposed of 50 KC200GT modules manufactured by KyoceraSolar Energy Inc. The simulated PV system configuration is anarray of 5 10 modules, and its voltage and current at the MPPwith the solar irradiance of are 261.3 V and 38.1 A,respectively.

E. ESS Model

This study considers batteries as energy storage devices.However, these batteries may require a dc-dc power converterin order to step up their voltage to the main dc busvoltage because their nominal voltage whose level is 240V in this microgrid is typically lower than the main dc busvoltage. One reason for using a lower battery voltage is to im-prove their reliability and life-time by avoiding issues found inhigher voltage configurations, such as cell voltage equalization.For this purpose, a bidirectional boost/buck converter shownin Fig. 4 is considered in the proposed microgrid. If the powergeneration from the renewable micro-energy sources is insuffi-cient for the demand power at the load side, this bidirectionalconverter operates in a boost mode in order to discharge energyfrom batteries to the main dc bus as depicted in Fig. 4. But,when the renewable power production exceeds the load-sidedemand power, this power converter works in a buck mode inwhich power flows from the main dc bus to charge the batterieswith the extra local power production.

F. MI CSI Converter

Among MIC topologies in [2], [4]–[6], and [12]–[19], MICSI converters such as an MI Ćuk converter [12], [15] and anMI SEPIC converter [15], [19] can be used in this microgrid.These MI CSI converters provide nearly continuous input cur-rent waveforms due to their CSI input legs. Hence, these con-verters provide more operational flexibility than an MI buck-


Fig. 4. ESS model.

Fig. 5. MI Ćuk dc-dc converter [12].

Fig. 6. Switching diagram of the MI Ćuk dc-dc converter.

Fig. 7. Operational modes of the MI Ćuk dc-dc converter. (a) Mode I (onlyconducts current). (b) Mode II (only conducts current) (c) Mode III (onlydiode conducts current).

boost converter [13] because they allow the integration of inputsources that require a relatively constant current [12], such asthe input current control that is used in this power plant andis explained in Section IV-A. An MI Ćuk converter [12], [15]is similar to an MI SEPIC converter [15], [19] except for theoutput voltage inversion. However, since there are more pastworks focusing exclusively on the MI SEPIC [19], the analysishere focuses on the MI Ćuk converter shown in Fig. 5.

Fig. 6 illustrates the switching diagram of an MI Ćuk con-verter. If it is assumed to be operated in a continuous conduc-tion mode, circuit operation in a steady state can be describedbased on the following three operational modes.1) Mode 1 (see Fig. 7(a); ): It is assumed thatthe voltage level of the first input source is higherthan that of the second input source . Although activeswitches and are turned on in this mode as depictedin Fig. 6, only conducts current since the diode isreverse-biased due to the assumption that is greaterthan . The diode at the common output stage is alsoreverse-biased.

2) Mode 2 (see Fig. 7(b); ): As illus-trated in Fig. 6, only an active switch is turned on andconducts current in this mode since the diode is alsoturned on. The diode at the common output stage is stillreverse-biased.

3) Mode 3 (see Fig. 7(c); ): All switchesexcept the diode are turned off in this mode. Therefore,the diode only conducts current.

Based on the described operational modes, the switched dy-namic model of this MI Ćuk converter is governed by

(6)

(7)

where and are the switching functions of the MI Ćukconverter, and and are the effective switchingfunctions of each input cell that equal andrespectively. In an average sense, the derivatives of an inductorcurrent and a capacitor voltage are zero. In addition, switchingfunctions, , , and , can be considered as dutycycles , , and respectively in the average model.Then, the average model of this MI Ćuk converter is

(8)

(9)

where , , and are the average voltages on capacitors, , and , respectively, and , , and are the av-

erage currents of inductors , , and respectively, andis the output power of the MI Ćuk converter. Therefore, thesteady-state output voltage of the MI Ćuk converter is

(10)


Fig. 8. Mechanical power of a wind turbine at various wind speeds [25].

IV. CONTROL STRATEGIES

A. Wind Turbine: Variable Speed Control

This paper uses a variable speed control method whosestrategy is to capture the maximum wind energy below therated wind speed. Fig. 8 shows mechanical power captured bywind turbine blades at each rotor speed of the wind turbine

and various wind speeds . As Fig. 8 illustrates,mechanical power from the wind turbine depends on the windturbine rotor speed . In addition, the optimal power linecan be obtained by connecting MPPs at each wind speedbecause a single MPP exists at each wind speed as shown inFig. 8. Hence, the operation of the wind turbine at the optimalrotor speed on the optimal power curve ensures thatthe wind turbine captures the maximum wind energy below therated wind speed.One feasible method to operate the wind turbine on the op-

timal power line below the rated wind speed is to control thethree-phase rectified output current with the wind turbinerotor speed [9], [25]. In order to describe such controlmethod, the optimal mechanical power of the wind tur-bine is considered to be [25]

with (11)

where is the maximum rotor power coefficient, isthe optimal TSR, is an optimal power constant, is airdensity, and is the radius of a wind turbine blade. If powerefficiencies of the wind generator and the three-phase rectifierin Fig. 1 are assumed to be constant at and respectively,the optimal real power at the three-phase rectifier outputis

(12)

where and are the rectified output voltage and currentrespectively. If a PMSG is assumed to be an ideal generator, theline-to-line voltage is

(13)

and where is the voltage constant of the generator, isthe electrical angular frequency of the generator, and is the

Fig. 9. Current mode controller.

Fig. 10. Flow chart of an incremental conductance method [27].

Fig. 11. - inverter current controller. (a) Voltage controller. (b) Active powercontroller.

number of poles in the generator. Then, the three-phase rectifiedoutput voltage is [26]

(14)

where is the peak line-to-line voltage, and is the statorphase inductance of the PMSG. By solving the quadratic equa-tion that can be obtained from (12) and (14) with respect to ,the reference rectified current results to be equal to

(15)

Hence, the wind turbine can be operated along the optimalpower curve if is controlled to its reference valueby adjusting the duty ratio of the MIC at each according to(15). This paper uses a PI controller, shown in Fig. 9, in orderto achieve this target current .

B. PV Module: MPP Tracking

The PV system is also controlled so that it operates at itsMPP. An incremental conductance method [27] is selected forthis purpose. It uses the PV modules output current and voltageinformation based on polarity changes in the derivative of powerwith respect to their voltage, which is zero at the MPP, positiveat the left of theMPP, and negative at the right of theMPP. Thesevoltage polarity changes characteristics lead to the following


Fig. 12. Configuration of the simulated 30-kW sustainable microgrid.

criterion that identifies whether PV panels reach their MPP ornot [27]:

(16)

Once the MPP is calculated with this method, the MIC con-troller regulates PV modules’ output voltage towards the ob-tained reference voltage by adjusting the MIC’s duty ratios. Thedetailed flow chart of this control method is provided in Fig. 10.As indicated in Fig. 10, a tolerance which equals zero is usedfor this criterion in the simulation study because this toleranceallows PV modules to remain at their MPP once they reach theirMPP. Otherwise, PV modules may oscillate around their MPPwhen they reach their MPP, thus producing steady-state errorat the operating points of the PV system. Practical ways of ad-dressing this issue in real situations [28] have extensively beenstudied in the past and are out of the scope of this paper.

C. ESS Control

The ESS in this microgrid is controlled to regulate the maindc bus voltage both when there is not sufficient powerproduction from the wind generator and PV modules and whenthere is excess local power production to charge the batteries. Abidirectional boost/buck converter shown in Fig. 4 is used withthe hysteresis control in [9]. If is higher than a upper voltagelimit , ESS will be charged in a buck mode sothat is regulated toward . If is lower than a lowervoltage limit , ESS will be discharged in aboost mode in order to regulate toward . Otherwise,ESS will be in a float mode.

D. PWM Inverter Control

The primary goal of a PWM inverter controller is to regulatethree-phase local ac bus voltage and frequency in this microgridand to dispatch target active power to the distributiongrid, which may be set by users or grid operators. For these pur-poses, based current control is used in the PWM inverter.As described in Fig. 11(a), a local ac line-to-line voltageis regulated by the component of the reference inverter cur-rent in the frame. Dispatch active power to thegrid can also be controlled by the component of thereference inverter current as depicted in Fig. 11(b).

V. RESULTS AND DISCUSSIONS

Fig. 12 shows the overall configuration of the simulated30-kW wind/solar power system. In order to focus on local

Fig. 13. Block diagram of the wind turbine in Fig. 12.

Fig. 14. Block diagram of the wind turbine controller in Fig. 12.

ac load and grid injected power variations, this study did notconsider local dc loads in the simulation because dc loads canbe trivially connected to the main dc bus if its dc voltage isregulated. The wind turbine is modeled by (3), (4), and (5) asindicated in Fig. 13. Detailed specifications of the wind turbineand the PMSG are shown in Tables I and II, respectively.Fig. 14 depicts the wind turbine controller developed based on(15) and the current mode controller shown in Fig. 9. Fig. 15illustrates the digital PV module controller that is realizedbased on the incremental conductance control in order totrack the MPPs of solar energy as discussed in Section VI-B.The MI Ćuk converter is modeled with built-in circuit-basedcomponents in MATLAB Simulink/Simpowersystems, andthe circuit schematic and component values are illustrated inFig. 16. The internal models of a PMSG, a three-phase rectifier,a PWM inverter, and a three-phase 240 V/2.4 kV transformerin MATLAB Simulink/Simpowersystems are used for thisstudy. The circuit-based PV model shown in Fig. 3 is usedfor this study with the parameters presented in the previousSection III-D.

A. Control Performance of the Wind Turbine

A wind model presented in Section III.A is considered tosimulate the spatial effect of varying wind components. Asindicated in Fig. 17(a), (b), and (f), when wind speed increases,the wind turbine rotor speed accelerates so that the outputpower from the wind turbine increases. On the otherhand, when wind speed decreases, the wind turbine rotor speed

slows down so the output power from the wind turbinedecreases. The wind turbine is also operated at the

optimal rotor speed and harvests the maximum power


Fig. 15. Block diagram of the PV panel controller in Fig. 12. ADC: Analog-to-digital converter. PWM: Pulse width modulator.

Fig. 16. Detailed schematic of the MI Ćuk converter in Fig. 12.

from wind energy at each wind speed since a rotor powercoefficient keeps constant at 0.44, which is its maximumpossible value as shown in Fig. 17(c). Fig. 17 also shows theoutput terminal electrical characteristics of the three-phaserectifier with wind energy variations. As shown in Fig. 17(a)and (d), the reference input current elevates when windspeed increases. Thus, the rectified output current is con-trolled toward the reference current , and the terminalrectified output voltage increases as indicated in Fig. 17(e).Therefore, the output power from the wind turbineincreases when the wind speed also increases. Similarly, whenwind speed decreases, the reference input current de-clines, thus decreasing the rectified output current and theterminal rectified output voltage. Hence, the output power fromthe wind turbine declines when wind speed decreases.Therefore, it can be concluded that the wind generator operatesin the optimal power point despite different environmentalconditions such as sudden increases or decreases of the windspeed, which likely happen during the day. Moreover, the windgenerator controller expeditiously reacts to such rapid changingenvironmental conditions.

B. Control Performance of the PV Modules

This study also investigates the system performance withsolar irradiance variations. The PV panel surface temperatureis assumed to be fixed at during the entire simulationperiod. Fig. 18 shows the control performance of PV moduleswith solar irradiance variations whose data sets [29] werecollected at Golden, CO, by NREL from 12:41 pm to 1 pmMST on July 31, 2008. The PV modules operating powerpoints are well-followed toward the MPPs becausedescribed in (16) is almost zero even when the solar irradiancechanges as attested in Fig. 18(a) and (e). Thus, this PV systemcontroller tracks the MPPs of solar energy regardless of therapidly changing wind energy. Specifically, this PV systemcontroller immediately locates the MPP since this PV systemis independently controlled. Considering that the performancesof these wind and PV controllers illustrated in Figs. 17 and 18,

Fig. 17. Wind turbine control performance. (a) Wind speed . (b) Tur-bine rotor speed . (c) Wind turbine rotor power coefficient . (d) Ref-erence current and three-phase rectified output current . (e) Three-phase rectified output voltage . (f) Wind turbine power .

it is verified that the discussed control strategy is an adequateone for a wind/solar microgrid with a CSI MIC.

C. Control Performance of the MI Ćuk Converter

Fig. 19 shows the control performance of the MI Ćuk con-verter when wind speed and solar irradiance change in the samemanner than in Figs. 17 and 18. Fig. 20 shows the input currentwaveforms of the MI Ćuk converter presented in [12] from ahardware experimental prototype. Figs. 19(a) and 20 show theinput current waveforms of the MI Ćuk converter that are con-tinuous so that it can provide operational flexibilities discussedin Section III-E. Fig. 19(b) shows the input and outputpower of the MI Ćuk converter, the wind turbine power,and the PV modules’ power. There seem to be differences be-tween and due to the switching and conduction losses inactive circuit components, as shown in Fig. 16.

D. Inverter Results With Variable Source and Load Power

Fig. 21 illustrates microgrid performance with variablesource and load power conditions on the grid-side of the in-verter. When the input source power is changed as shown inFig. 21(a), the demand at the local ac bus is also changed asdepicted in Fig. 21(b). In addition, the target dispatch activepower to the distribution grid is modified from 10 to5 kW at the 10 minutes mark. As described in Fig. 21(a) and (b),


Fig. 18. PV modules control performance (a) Solar irradiance. (b) PV modulescurrent. (c) PV modules voltage. (d) PV modules power. (e) .

Fig. 19. MI Ćuk converter (MIC) control performance. (a) MIC input currents:PV modules current and wind generator rectified current. (b) MIC inputand output power , wind turbine power, PV modules power.

Fig. 20. Current waveforms of the MI Ćuk dc-dc converter [12].

the grid-side PWM inverter generates enough power requiredfor the variable local ac load and dispatch power to the grid.

Fig. 21. Results with variable source and load power. (a) Input source power:wind generator power , PV modules power , and ESS power

. (b) Demand power at the local ac bus: inverter output power , in-jected power to the grid , target dispatch power to the grid ,and local ac load power . (c) Dc link voltage . (d) MIC outputcurrent , ESS output current , and inverter input current ).(e) phase voltage magnitude . (f) Local three-phase ac voltageTHD. (g) Local ac frequency.

Fig. 21(c) and (d) depict that the ESS is also well-controlledbecause it is discharged and regulates the main dc link voltage

toward (i.e., 370 V) when the renewable powerproduction is insufficient compared to demand power, and viceversa. Moreover, Fig. 21(e)–(g) shows that this microgrid in-verter well regulates the local ac voltage level of andfrequency in 60 Hz despite source and load power variations.

VI. CONCLUSION

This paper presented the dynamic modeling and operationalstrategy of a sustainable microgrid primarily powered by windand solar energy. These renewable sources are integrated into


the main dc bus through an MI CSI dc-dc converter. Wind en-ergy variations and rapidly changing solar irradiance were con-sidered in order to explore the effect of such environmental vari-ations to the intended microgrid. In addition, the proposed mi-crogrid is equipped with an ESS and is connected with the distri-bution grid. These diverse micro-energy resources can improvethe microgrid performance and reduce power generation vari-ability and vulnerability to natural disasters. Its power convertercan also be designed in a smaller size with low production costsbecause MICs can remove unnecessary redundant components.A 30-kW wind/solar hybrid microgrid dynamic model was

developed withMATLAB Simulink/Simpowersystems. For thispurpose, this paper focused on the MPP tracking of the renew-able micro-energy source power variations under the local acdemand changes and the variable dispatch power to the distri-bution grid. For the wind generator, this paper used a variablespeed control method whose strategy is to capture the maximumwind energy below the rated wind speed. Specifically, an inputcurrent control method was used for this variable speed con-trol. In addition, a circuit-based PV system model with an incre-mental conductance control method was used for the simulationstudy.In contrast to previous works, this paper explored the system-

wide performance of the sustainable microgrid with anMI dc-dcconverter when the micro-energy source power, the local acload, and the dispatch power to the distribution grid change. Inaddition, this study also considered an ac wind generator and agrid-side inverter in the proposed model. The system-wide sim-ulated dynamics in Section V attested that the control strategyproposed in this paper is feasible when deploying a sustainablemicrogrid with a CSI MI dc-dc converter which can reduce itsproduction costs.

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Sungwoo Bae (S’08) received the B.S. degree inelectrical engineering from Hanyang University,Seoul, Korea, in 2006, and the M.S.E. and Ph.D.degrees in electrical engineering from the Universityof Texas at Austin, in 2009 and 2011, respectively.In 2005, Dr. Bae was awarded the Grand Prize at

the National Electrical Engineering Design Contestby the Minister of Commerce, Industry and Energyof the Republic of Korea.


Alexis Kwasinski (S’03–M’07) received the B.S.degree in electrical engineering from the BuenosAires Institute of Technology (ITBA), Buenos Aires,Argentina, the Graduate Specialization degree intelecommunications from the University of BuenosAires, Buenos Aires, in 1997, and the M.S. andPh.D. degrees in electrical engineering from theUniversity of Illinois at Urbana-Champaign, in 2005and 2007, respectively.From 1993 to 1997, he was with the Telefonica of

Argentina designing and planning telephony outsideplant networks. Then, he was with Lucent Technologies Power Systems (laterTyco Electronics Power Systems) for five years as a Technical Support Engineerand a Sales Technical Consultant in Latin America. He was also a part-time in-structor in charge of ITBA’s Telecommunications Laboratory for three years. He

is currently an Assistant Professor in the Department of Electrical and ComputerEngineering, The University of Texas at Austin. His current research interestsinclude power electronics, distributed generation, renewable and alternative en-ergy, smart grids, and analysis of the impact of natural disasters on critical powerinfrastructure, which included site damage assessments after several natural dis-asters in Japan, New Zealand, U.S., and Chile.Dr. Kwasinski was a member of the Executive Committee of the Argentine

Electrotechnical Association during 1994 and 1995. He is an Associate Ed-itor for the IEEE TRANSACTIONS ON ENERGY CONVERSION. In 2005, he wasawarded the Joseph J. Suozzi INTELEC Fellowship, and in 2007, he receivedthe Best Technical Paper Award at INTELEC. In 2009, he received the NationalScience Foundation (NSF) CAREER Award and in 2011 he received an IBMFaculty Innovation Award.

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