Aalborg Universitet

Fuzzy droop control loops adjustment for stored energy balance in distributed energystorage system

Aldana, Nelson Leonardo Diaz; Wu, Dan; Dragicevic, Tomislav; Vasquez, Juan Carlos;Guerrero, Josep M.Published in:Proceedings of the 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia)

DOI (link to publication from Publisher):10.1109/ICPE.2015.7167864

Publication date:2015

Document VersionEarly version, also known as pre-print

Link to publication from Aalborg University

Citation for published version (APA):Aldana, N. L. D., Wu, D., Dragicevic, T., Vasquez, J. C., & Guerrero, J. M. (2015). Fuzzy droop control loopsadjustment for stored energy balance in distributed energy storage system. In Proceedings of the 2015 9thInternational Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia) (pp. 728 - 735 ). IEEE Press.https://doi.org/10.1109/ICPE.2015.7167864

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Fuzzy Droop Control Loops Adjustment forStored Energy Balance in Distributed

Energy Storage System

Nelson L. Dıaz∗†, Dan Wu∗, Tomislav Dragicevic∗, Juan C. Vasquez∗, Josep M. Guerrero∗∗ Department of Energy Technology, Aalborg University, Aalborg, Denmark.

†Faculty of Engineering, Universidad Distrital F. J. C., Bogota, Colombia.nda@et.aau.dk, dwu@et.aau.dk, tdr@et.aau.dk, juq@et.aau.dk, joz@et.aau.dk

www.microgrids.et.aau.dk

Abstract—The study of isolated AC microgrid hasbeen under high interest due to the integration ofrenewable energy resources especially for remote areas,or to improve the local energy reliability. The cur-rent trend is oriented to distributed renewable energysources and their corresponding energy storage system,in order to smooth the variations at the prime energygenerator. In this paper, a decentralized strategy basedon fuzzy logic is proposed in order to balance the stateof charge of distributed energy storage systems in low-voltage three phase AC microgrid. The proposed methodweights the action of conventional droop control loopsfor battery based distributed energy storage systems, inorder to equalize their stored energy. The units are self-controlled by using local variables, hence, the microgridcan operate without communication systems. Frequencyand voltage bus signaling are used in order to coordinatethe operation of the microgrid under different stagesfor charging batteries. Simulation results show thefeasibility of the proposed method.

Keywords—Distributed energy storage systems, Droopcontrol, Fuzzy inference system, State of charge.

I. INTRODUCTION

The integration of Renewable Energy Sources(RES) such as photovoltaic (PV) and wind turbinegenerators (WTG), in order to use clean and re-newable energy instead of traditional coal, oil andother non-renewable energy resources, has been un-der rapid development in the world nowadays. Amicrogrid appears as an effective solution for inter-connecting RES, energy storage systems (ESS) andloads as controllable entities, which may operatein grid-connected or islanded mode, either in ACor DC configuration [1], [2]. Particularly, islandedmicrogrids play and important role when economicand environmental issues do not allow an intercon-nection with the main grid [1], [3]. Indeed, isolatedmicrogrids become an additional challenge since thevoltage and frequency are not imposed for the maingrid. Therefore, every distributed energy resourceshas to cooperate in order to ensure the reliability,security and power stability of the local grid [4], [5].

However, the intermittent nature of RES, addedtogether with unpredictable load fluctuations, maycause instantaneous power unbalances that affect

Loads

AC common bus

ESS1 Primary ControlRES1 Primary Control

ESSi Primary

Control

RESi Primary Control

RES2 Primary Control

ESS2 Primary

Control

RES1

RES2

RESi-1 Primary Control

ESSi-1 Primary

Control

RESi-1

...

...

RESi ESS1 ESSi

ESSi-1 ESS2

(PV+ESS)

(PV+ESS)

(WTG+ESS)

(WTG+ESS)

ACDC

ACDC

AC DC

ACAC

DCAC

DCAC

ACAC

DCAC

Figure 1: Islanded AC microgrid configuration basedon distributed (PV+ESS) and (WTG+ESS) genera-tors.

the operation of the microgrid. Hence, ESS are re-quired for ensuring the operating conditions of thepower grid by smoothing the variations of RES [6]–[8]. In fact, there are two ways of integrating ESS(aggregated or distributed) [9], [10]. At this sensedifferent studies have been done in order to comparethe performance of these two strategies. In general,some results show advantages in the distributedstrategy [9]. Anyhow, the current trend is orientedto distributed ESS where an ESS is associated to itscorresponding RES into an entity denoted as activegenerators (PV+ESS or WTG+ESS), as is shown inFig. 1. This configuration, aims to ensure constantpower production when PV and WTG are used [6],[8], [11], [12], . Commonly, in islanded microgridsthe ESS are composed of banks of valve regulatedlead-acid (VRLA) batteries [1], [13]–[15].

When a microgrid is composed by distributedESS, a coordination to ensure stored energy balancebetween the units is required. This coordinationaims to avoid deep-discharge in one of the ESSand over-charge in the others. Consequently, duringthe charging process, it is desirable to prioritize thecharge of the unit with the smallest state of charge(SoC), and similarly, during the discharging process,

This document downloaded from www.microgrids.et.aau.dk is the preprint of the final paper:N.L. Diaz, D. Wu, T. Dragicevic, J.C. Vasquez, J.M. Guerrero, "Fuzzy droop control loops adjustment for stored energy balance in distributed energy storage system," in Proc. IEEE ECCE-ASIA, 2015.

the unit with the highest SoC should provide morepower to the microgrid than the others in orderto ensure stored energy balance [16]–[18]. Com-monly, droop control strategies are used in orderto achieve power sharing between units. Therefore,conventional control loops for power sharing at eachESS are complemented with stored energy balancecontrol actions which adjust the droop coefficientsin accordance to the SOC.

Different approach based on centralized and dis-tributed strategies have been proposed for energystorage balance in distributed ESS for DC micro-grids [13], [15], [19]–[22]. The main drawback ofthose strategies is that they are based on centralizedcontrol units, and they require the use of com-munication systems, which are not always suitablefor decentralized configurations of a microgrid. Adecentralized method for a DC microgrid has beenproposed in [23] which does not use communicationbetween units. However, in [23] the authors onlyconsider the equalization process when the batter-ies are under discharge. In [24] authors consider adouble quadrant method for DC microgrids whichensure SOC equalization for both charging and dis-charging process. Even so, those methods requirethe knowledge of the initial SoC at each unit inorder to obtain adequate values for their parameters,and for ensuring the stability of the system. In thesame way, in [25] a decentralized adaptive droopcontrol have been proposed for equalizing the SoCof distributed ESS in DC microgrid. Nevertheless, in[25] it is required a previous knowledge about thecharacteristics of the others ESS in order to calculatethe charge and discharge functions. On top of that,a strategy based on a fuzzy inference systems (FIS)was proposed previously for islanded DC micro-grids [16]. This strategy assures good stored energybalance, and additionally it is absolutely modular,expandable. Indeed, centralized control and commu-nications between units are not required. Anotherimportant issue to take into account, is that the pro-posed strategy takes into account the different stagesrequired for charging an ESS based on batteries.Apart from that, it is important to consider howthe battery charge process is coordinated with theoperation of the others distributed energy resourcesinto an islanded microgrid.

In this paper, the FIS proposed in [16] is adapted,applied and evaluated for an islanded AC micro-grid. In this case, the proposed FIS weights the P−ωdroop coefficients of the droop controllers in accor-dance with the SoC at each ESS. In particular, theimplementation of this strategy implies additionalchallenges in the primary control loops since the ACmicrogrid has to deal with issues associated withsynchronization, reactive power flows, and DC/ACconversion losses, which are not a concern in DCmicrogrids [20].

This paper is organized as follows: Section II

describes the operation of the islanded microgridunder different control operation modes. Section IIIshows the design and operations of the proposedfuzzy strategy for stored energy balance. SectionIV explain how the droop control loop for eachRES is adjusted in order to ensure power shar-ing proportional to their maximum power. Finally,section V presents simulation results under differ-ent operational conditions. The proposed methodis tested in a low voltage AC microgrid underislanded operation. Simulation results based on aMATLAB/Simulink model of the microgrid are pre-sented to show the applicability and advantages ofthe proposed strategy. Finally, Section VI presentsconclusions and perspectives for future works.

II. ISLANDED MICROGRID OPERATION

Normally, in an islanded microgrid all the powerconverters operate in voltage control mode (VCM)by following conventional droop control strategy,aimed to regulate the bus voltage and frequencyand achieve good power sharing between units [26].Droop control, enhances system reliability, expand-ability, and ensures the robustness without the useof external communication system [5], [27]. This ap-proach works well with dispatchable power sourcessuch as diesel generators but it is not effective forintermittent sources such as RES which are morelikely to operate under an algorithm of maximumpower tracking (MPPT). At that case, RES unitsbehave as current sources and operate on currentcontrol mode (CCM) [1], [5]. Meanwhile, the ESSoperate in VCM being responsible of regulating thebus voltage. Then, under VCM the batteries will becharged or discharged in order to compensate theunbalance between the energy generated by RESand load consumption [16], [28].

However, the most effective way of charging abattery is by means of a two stages procedure whichinvolve two different control loops [14]. First, theESS are charged based on the energy unbalancebetween RES generation and load consumption, atthis moment, the power is limited by droop controlloops, then the ESS operate under VCM. Subse-quently, when the voltage per cell reach a thresh-old value (Vr), known as the regulation voltage(typically 2.45± 0.05 volts/cell), the battery voltageshould be kept constant and the current at thebattery will approach to zero asymptotically, andonce it falls below a certain value, the battery may beconsidered as fully charged [14], [29]. At this point,each ESS draws as much power as needed to keepits battery voltage at Vr [15]. Because of this, eachESS operates on CCM and the RES must assumethe responsibility of bus voltage regulation. As aconsequence, the RES change their operation modeto VCM. To be more precise, the primary control ofeach distributes energy resource is composed by twocontrol loops (VCM and CCM) and they should beable switch between them as is shown in Fig. 2. In

Loads

AC common

bus

RES)Primary)Control

AC AC

DC AC

AC DC

MPPT

P-ω,Q-E

Droop)Control

RES)Primary)Control

FIS

ESS)Primary)Control

Constant)Voltage

Charger

(PV+ESS)(WTG+ESS)

P)-)ω,)Q-EDroop)Control)

P-ω,Q-E

Droop)Control

MPPT

VCM

VCM VCM

CCM

CCMCCM

Islanded)AC)Microgrid

ESS)Primary)Control

AC DC

FIS

P-ω,)Q-EDroop)Control)

Constant)Voltage

Charger

VCM

CCM

Figure 2: Islanded AC microgrid with (PV+ESS) and(WTG+ESS) generators.

light of the above, the operation of each RES andESS in the microgrid should be accompanied by adecision-maker strategy in order to switch betweencontrollers. Bus-signaling method, by using differentbus voltage/frequency thresholds, is used to triggerthe changes at the operation mode for RES and ESSin a coordinated way [5], [16].

At this paper in order to simplify the analysisan islanded microgrid composed by two RES (PVand WTG), loads, and two banks of valve regulatedlead-acid (VRLA) batteries, as the one shown in Fig.2, will be consider.

A. Reactive power sharing

Since voltage bus signaling will be used fordetermining the transitions between control modesat each distributed energy resource, reactive powerQ − E control loops play an important role. Con-ventional Q − E droop controllers defined by thefollowing equation:

E = E∗ − n ·Q (1)

are used in order to share equally the reactive powerflow between units in a microgrid. In (1) E is thevoltage amplitude in the common bus, Q is thereactive power at the respective unit, E∗ is thevoltage reference and n is the droop coefficient [27].

At this application only the units working underVCM are responsible of reactive power flow, forthat reason they will establish the amplitude of theoutput voltage E in accordance to (1). To be moreprecise, ESS and RES units, interchange the respon-sibility of the reactive power flow. Since bus voltage-signaling is used for triggering the changes at thedecision-makers, a positive droop coefficient (n) isdefined for ESS and a negative droop coefficient(n) is defined for RES. In this way it is possible to

-6000 -5000 -4000 -3000 -2000 -1000 0322

323

324

325

326

327

328

329

Q (VAr)

E (

V)

E*

E=E*+nQRES

E=E*-nQBat

Figure 3: Droop characteristics Q− E

identify which one (ESS or RES) are operating underVCM. Equation (1) is redefined as:

E =

{E∗ − n ·QBati, When ESS are in VCM;E∗ + n ·QResi, When RES are in VCM.

(2)

where (QBati) and (QResi) represent the reactivepower flow at each ESS and RES respectively. Thedroop coefficient n is calculated for a voltage de-viation smaller than 5% at the common bus. Fig.3 represents graphically the operation of the Q− Edroop control loop.

B. Decision-maker operation

Decentralized finite state machines with twostates are used at each RES and ESS unit in orderto coordinate the changes at the control operationmode. The transition between operation modes aretriggered by bus voltage/frequency-signaling, thebattery array voltage (VBati), and the power com-parison when the MPPT value is smaller than thegenerated power for ESS and RES respectively. Fig.4a shows the Moore finite state machine proposedfor ESS, where the input variables are defined bythe following equations:

X1Batt =

{1, When VBati ≥ Vr;0, Otherwise .

(3)

X2Batt =

{1, When f reqACbus ≤ f rect;0, Otherwise .

(4)

X3Batt =

{1, When E ≥ E∗;0, Otherwise .

(5)

where f reqACbus is the frequency at the common bus,and f reqt is the threshold frequency.

Likewise, Fig. 4b shows the Moore finite statemachine established for RES, and the input variablesare defined by the following equations:

X1RES =

{1, When f reqACbus ≤ f rect;0, Otherwise .

(6)

X2RES =

{1, When E ≥ E∗;0, Otherwise .

(7)

VCM

11

BattX

1State

2State

CCM

2 31 & 0

Batt BattX X

(a)

VCM

1 21 & 1

RES RESX X

1State

2State

CCM

31

RESX

(b)Figure 4: Finite state machines for: (a) ESS Decision-maker (b) RES Decision-maker.

X3RES =

{1, When PMPPT < PRESi;0, Otherwise .

(8)

where PMPPT is the power reference given by theMPPT algorithm, and PRESi is the power generatedfor each RES unit.

III. FUZZY ADJUSTMENT FOR SOC BALANCE

When batteries are in the process of charge ordischarge, the power balance is managed by P− ωdroop control loops [27]. Therefore, the frequencyat the common AC bus given by the followingequation:

ω = ω∗ −m · PBati (9)

where m is the droop coefficient, ω is the angularfrequency at the common bus, ω∗ is the referenceof the angular frequency, and PBati is the powerdriven at each ESS. If we consider differences inthe droop coefficients (m) at each ESS, the batterywith the lowest m will inject/extract more powerto/from the grid in order to keep the power balancein the microgrid [2]. For that reason, the ESS withthe lowest m will be charged or discharged fasterthan the other.

For energy storage equalization, it is desirablethat the battery with the lowest SoC will be chargedfaster than all the others and in this way ensuringstored energy balance. Then, a smaller m should beassigned to that battery. Likewise, when batteriesare supplying power to the microgrid, it is desiredthat a larger m is assigned to the battery with thelowest SoC, in order to prevent a deep discharge andbalance the stored energy. Accordingly, the SoC canbe equalized when correct values of m are assignedto each ESS. In addition, the value of m has to beadjusted when the different SoC’s approach betweenthem.

In particular, a FIS can easily summarize allthe qualitative knowledge, expressed above, aboutthe expected behavior of the system in order toweight the droop coefficient at each ESS based on itsown SoC [16]. Therefore, a fuzzy weighting factorW (SoCBati) is proposed for weighting the droopcoefficient as is shown in Fig. 5, where it is assumed

-1500 -1000 -500 0 500 1000 1500314.15

314.152

314.154

314.156

314.158

314.16

314.162

314.164

314.166

314.168

P (W)

(

rad

/s)

*

PBat1

PBat2

PBat1

PBat2

DISCHARGE

=*-m(W(SoCBat1

)PBat1

CHARGE

=*-m(W(SoCBat2

)PBat2

=*-m(W(SoCBat1

)PBat1

=*-m(W(SoCBat2

)PBat2

Figure 5: Droop characteristics P−ω

0

1

0.5 0.6 0.7 0.8 0.9 1.0SoC

µ (SoC)Small Medium High

1

0 0.5 1.0−0.5−1.0sgn(ω)

µ(sgn(ω))PN

0

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

µ(W(SoC))

W(SoC)

S M H

Consecuente

Para el consecuente u se definieron las siguientes funciones de pertenencias con centros en−9,8 para NF, 9,8 para PF y 0 para Z

1

2 4 6 8 10−2−4−6−8−10

µ(x)

NF Z PF

Como agregacion el metodo utilizado es el maximo y como implicacion el mınimo. Paradefusificar se utilizo el centroide.

4. Superficie final de control.

La superficie de control que se obtiene se presenta en la siguiente figura.

3

(a)

0

1

0.5 0.6 0.7 0.8 0.9 1.0SoC

µ (SoC)Small Medium High

1

0 0.5 1.0−0.5−1.0sgn(∆ω)

µ(sgn(∆ω))PN

0

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

µ(W(SoC))

W(SoC)

S M H

Consecuente

Para el consecuente u se definieron las siguientes funciones de pertenencias con centros en−9,8 para NF, 9,8 para PF y 0 para Z

1

2 4 6 8 10−2−4−6−8−10

µ(x)

NF Z PF

Como agregacion el metodo utilizado es el maximo y como implicacion el mınimo. Paradefusificar se utilizo el centroide.

4. Superficie final de control.

La superficie de control que se obtiene se presenta en la siguiente figura.

3

(b)

0

1

0.5 0.6 0.7 0.8 0.9 1.0SoC

µ (SoC)Small Medium High

1

0 0.5 1.0−0.5−1.0sgn(ω)

µ(sgn(ω))PN

0

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

µ(W(SoC))

W(SoC)

S M H

Consecuente

Para el consecuente u se definieron las siguientes funciones de pertenencias con centros en−9,8 para NF, 9,8 para PF y 0 para Z

1

2 4 6 8 10−2−4−6−8−10

µ(x)

NF Z PF

Como agregacion el metodo utilizado es el maximo y como implicacion el mınimo. Paradefusificar se utilizo el centroide.

4. Superficie final de control.

La superficie de control que se obtiene se presenta en la siguiente figura.

3

(c)Figure 6: Membership functions: (a) Input SoC, (b)Input sgn(∆ω), (c) Output W(SoC).

SoCBat2 > SoCBat1. As a result, equation (9) isrewritten as:

ω = ω∗ −m ·W (SoCBati) · PBati (10)

Given the above points, a Mamdani FIS has beenproposed in order to establish the weighting factorW(SoC) at each ESS based on its correspondingSoC. This kind of FIS are usually used in feedbackcontrol mode, because they are computationally sim-ple, present low sensibility to noise in the input,what is important in power system, and they caneasily represent the knowledge about the expectedcontrol action [30]. The knowledge is represented bymeans of rules in the form if-then and synthesizedin form of an input-output mapping between theantecedent and the consequent [30]. The consequentof the proposed FIS is the weighting factor W(SoC),and the antecedents are the SoC and the sign ofthe frequency deviation (sgn(∆ω)) defined by thefollowing equation:

sgn(∆ω) =

{1, If ω−ω∗ > 0;−1, If ω−ω∗ < 0.

(11)

For the SoC three membership functions have beendefined with the linguistic labels ”Small”, ”Medium”and ”High” as is shown in Fig. 6a. Likewise, for thesgn(∆ω) two singleton membership functions havebeen defined with the linguistic labels ”N” and ”P”(see Fig. 6b). Finally, for the output W(SoC) threesingleton membership function have been definedwith the linguistic labels ”S”, ”M” and ”H” (see Fig.6c).

0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.050

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

SoC

We

igh

t fa

cto

r W

(So

C)

DISCHARGE CHARGE

Figure 7: Control curves of the FIS.

To conclude, the proposed rule base is summa-rized as follows:

1. If SoC is Small and sgn(∆ω) is Nthen W(SoC) is H

2. If SoC is Small and sgn(∆ω) is Pthen W(SoC) is S

3. If SoC is Medium and sgn(∆ω) is Nthen W(SoC) is M

4. If SoC is Medium and sgn(∆ω) is Pthen W(SoC) is M

5. If SoC is High and sgn(∆ω) is Nthen W(SoC) is S

6. If SoC is High and sgn(∆ω) is Pthen W(SoC) is H

The SoC is estimated by ampere-hour (Ah)counting method [15],

SoC = SoC(0)−∫ t

0

Ibat(τ)

Cbatdτ (12)

where SoC(0) is the initial SoC, Cbat is the batteryarray capacity, and Ibat is the current of each battery.Fig 7 shows the control curve of the FIS. We can seefrom Fig. 6 and 7 that the droop coefficient m foreach ESS will be weighted between 0.1 to 0.9 of itsnominal value.

IV. RES DROOP CONTROL LOOPS

As mentioned before, all the RES units changetheir operation mode to VCM once all the batteryarrays reach the threshold voltage Vr. Despite, thedroop control action described by equation (9) al-lows to share equally the active power flow betweenunits, this is not practically possible when differentRES are used in the microgrid. To be more precise,each RES unit has its own power rating and its ownmaximum power point. For that reason, it is notpossible to ensure that all RES units can contributeequally on the power sharing. Therefore, the powercontribution of each RES should be arranged in

Table I: Parameters of the MicrogridParameter Symbol Value

Power StageNominal Bus Voltage E∗ 230 ∗

√2 V

Nominal Bus Frequency ω∗ 2 ∗ π ∗ 50 rad/sInverter inductors L 1.8mHFilter Capacitor C 27µFNominal Load PLoad 1587 WMaximum (RES) Power Rating PRESmax 2750 W

Battery ArrayNominal Voltage Vbat 672 VRegulation Voltage Vr 756 VNominal Battery Capacity Cbat 0.02 Ah

Power flowControl

(P−ω) Droop Coefficient m 1.25 ∗ 10−5 (rad)/(s)/(W)(Q− E) Droop Coefficient n 5 ∗ 10−4 V/(VAr))Reactive power Reference Q∗ 0 VAr

accordance to its maximum power point. For thatreason, a weighting factor 1000/PMPPTi is appliedto (9) then,

ω = ω∗ −m ·(

1000PMPPTi

)· PRESi (13)

where PMPPTi is the reference given by the MPPTalgorithm for each RES, and PRESi is the powersupplied by each RES under VCM operation.

V. SIMULATION RESULTS

A MATLAB/simulink model of the microgridhas been used in order to test and compare theperformance of the microgrid with and without theuse of the weighting factor W(SoC). The microgridis composed by a (PV+ESS) and a (WTG+ESS) gen-erators as shown in Fig. 2. The microgrid has beendesigned to supply a nominal resistive load in abalanced three phase system. Detailed models of theVRLA battery arrays are used as proposed in [15],for simulating the batteries. A bank of batteries of672V is used to ensure the nominal bus voltage witha modulation index around 0.9 in order to avoidover-modulation at the PWM signal. Table I sum-marizes the main characteristics of the microgrid.

Fig. 8 summarizes some results under changesat the power generated by RES. At this case, it isassume that the energy generated by both RES isthe same and they change from 500W to 1500Wat Time = 10s. An initial SoC of 75% for battery1 (Bat1) and 85% for battery 2 (Bat2) has beenestablished. At the top part of Fig. 8 (No FuzzyFactor), we can see the voltage at the DC bus ofeach ESS (VBat1 and VBat2), the power in the load(PLoad), the power at each RES (PRes1 and PRes2), andthe power at each ESS (PBat1 and PBat2), when thefuzzy factor is not used. In the middle of Fig. 8we can compare the SoC behavior with the fuzzyweighting factor (continuous line), and without thefuzzy weighting factor (dashed line). Finally, at thebottom of Fig. 8 (With Fuzzy Factor) we can see thevoltage at the DC bus at each ESS (VBat1 and VBat2),the power in the load (PLoad), the power at each RES(PRes1 and PRes2) and the power at each ESS (PBat1

0 5 10 15 20 25 30-1000

-500

0

500

1000

1500

2000

Time (s)

P (

W)

0 5 10 15 20 25 30

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-500

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500

1000

1500

2000

Time (s)

P (

W)

PBat2

PBat2

PBat1

PBat1

PRes1

, PRes2

PLoad

PLoad

PRes1

, PRes2

0 5 10 15 20 25 30-1000

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500

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2000

Time (s)

P (

W)

0 5 10 15 20 25 30

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-500

0

500

1000

1500

2000

Time (s)

P (

W)

PBat2

PBat2

PBat1

PBat1

PRes1

, PRes2

PLoad

PLoad

PRes1

, PRes2

0 5 10 15 20 25 3065

70

75

80

85

90

95

100

Time (s)

So

C (

%)

SoCBat2

SoCBat1

0 5 10 15 20 25 30700

720

740

760

Time (s)

VB

at (

V)

0 5 10 15 20 25 30700

720

740

760

Time (s)

VB

at (

V)

VBat1

VBat2

VBat1

VBat2

0 5 10 15 20 25 30700

720

740

760

Time (s)V

Bat (

V)

0 5 10 15 20 25 30700

720

740

760

Time (s)

VB

at (

V)

VBat1

VBat2

VBat1

VBat2

No Fuzzy

Factor

With

Fuzzy

Factor

1T

1T 2T 3T 4T

2T 3T 4T

Figure 8: Simulation results with (bottom figures)and without (top figures) fuzzy factor

and PBat2), when the fuzzy factor W(SoC) is used.The fuzzy factor is activated at Time = 3s.

Simulation time is split into 4 stages in orderto indicate the changes at the operation mode ofeach RES and ESS. Table II summarizes the changeson the operation mode of each ESS and RES inaccordance with times T1 to T4. We can see from Fig.8 how the SoC of both ESS approach asymptoticallyone to the other. Additionally, it is possible to seethat the system with fuzzy weighting factors reducesthe depth of discharge at battery 1. Moreover, bothbatteries are charged faster taking into account thatT1 + T2 + T3 is smaller when the fuzzy factor isused. Apart from that, the peak power of battery 1is smaller in the system that uses the fuzzy weightfactors. We can see after T3 and T4 that the volt-age at the battery arrays is kept constant such asrecommended in [14]. In T4 it is possible to see thatboth RES share power equally, since the same MPPTreference value is assumed for both RES.

Fig. 9 presents the behavior of the microgridwhen two RES with different maximum power areused. At this case, only the operation with the fuzzy

Table II: Changes at the operation control mode forRES and ESS

T1 T2 T3 T4RES1 CCM CCM CCM VCMRES2 CCM CCM CCM VCMESS1 VCM VCM CCM CCMESS2 VCM VCM VCM CCM

factor is considered. The changes at the controloperation mode are also in accordance with Table II.For this case, it is assume that the MPPT referencefor RES1 and RES2 are 500W and 250W respectively.At Time = 10s the references values changes to2000W and 1000W respectively for RES1 and Res2.Such as in the previous case, an initial SoC of 75%for battery 1 (Bat1) and 85% for battery 2 (Bat2) hasbeen established. From Fig. 9, when the RES changetheir operation mode to VCM (T4), we can see thatthe power contribution of each RES is establishedin accordance to their maximum power point. Asa matter of fact, RES1 contribute with more activepower than RES2.

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Figure 9: Simulation results with diferent PMPPT ateach RES

A more realistic scenario is presented in Fig. 10in which the behavior of the microgrid is evaluatedby emulating a single day. RES1 corresponds to aPV generator and RES2 corresponds to a WTG. At

Table III: Changes at the operation control modeduring a day

T1 T2 T3 T4PV CCM VCM CCM CCM

WTG CCM VCM VCM CCMESS1 VCM CCM CCM VCMESS2 VCM CCM CCM VCM

this case, the maximum power at each RES changeevery hour by emulating the behavior of real PVand WTG in a day. The nominal power of each RESis 2750W and the battery capacity of each ESS hasbeen set to 20(Ah). From Fig. 10 it is possible to seethe fuzzy equalization process for the SoC during aday. Initial SoC of 85% for battery 1 (Bat1) and 95%for battery 2 (Bat2) has been defined. We can seethat the SoC is completely equalized after 12 hours.On top of that, Table III summarizes the changes atthe control operation mode for all the distributedenergy resources in one day.

At the beginning (T1), RES units operate underMPPT, and the batteries are charged or dischargedin accordance to the power unbalance betweenpower generation and consumption. Meanwhile,distributed FIS balance the stored energy betweenbatteries, and the SoC is equalized. At (T2) bothbatteries have reached the threshold voltage at thesame time since the SoC has been equalized. At thispoint, all the distributed energy units change theiroperation mode as is shown in Table III. At the endof T2, the power reference given by the MPPT algo-rithm is smaller than the power supplied by the PVgenerator under VCM operation (PMPPT < PRES1).Because of this, RES1 changes its operation modeto CCM but RES2 continuous its operation underVCM during T3. Finally, at T4 RES2 changes itsoperation mode to CCM, since the reference givenby the MPPT algorithm is smaller than the generatedpower. At the same time, both ESS change theiroperation mode to VCM, and they start to supportthe voltage regulation.

VI. CONCLUSIONS

The proposed adjustment of the droop coeffi-cient by using a fuzzy inference system, ensuresgood storage energy balance for distributed ESS.Additionally, this strategy is absolutely modular,expandable, and it is not required a centralizedcontrol. As a matter of fact, it can be used directlywhen a new active generator has to be added tothe microgrid without any modification. Likewise,the proposed method shows additional advantagescompared to traditional methods such as asymp-totic approximation of the SoC under process ofcharge and discharge, faster charge in the total ofdistributed ESS and reduction of the deep of dis-charge for the ESS with smallest initial SoC amongothers. Another advantage of the proposed FIS isthat the same FIS can be applied to different valuesof droop coefficient and even more the same FIS

can be applied for AC or DC microgrids. On topof that, the microgrid can operate in a stable wayunder different scenarios without using communi-cations. The use of decision making strategies ateach distributed energy resource is required in orderto ensure adequate transition between operationcontrol mode and reliable operation.

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