distributed generation integration in the electric grid: energy

Research ArticleDistributed Generation Integration in the Electric GridEnergy Storage System for Frequency Control

Maurizio Delfanti Davide Falabretti Marco Merlo and Gabriele Monfredini

Department of Energy Politecnico di Milano 20156 Milan Italy

Correspondence should be addressed to Marco Merlo marcomerlopolimiit

Received 31 January 2014 Revised 5 May 2014 Accepted 13 May 2014 Published 11 June 2014

Academic Editor Ned Djilali

Copyright copy 2014 Maurizio Delfanti et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

During the last few years generation from renewable energy sources (RESs) has grown considerably in European electrical networksTransmission system operators are greatly concerned about the impact of RESs on the operational security and efficiency of theirnetworks and more in general of the ENTSO-E interconnected system Grid codes are to be revised in order to harmonise therules regarding the connection of RES power plants A main issue concerns frequency control frequency is greatly affected byRESs intermittency and its deviations must be limited as much as possible in order to guarantee a suitable level of power quality Toimprove frequency stability in the future Grid codes could extend frequency control requirements also to RES units whereas todaythey are applied only to conventional power plants Energy storage systems can be a possible solution to increase the flexibility andperformance of RES power plants they allow generators to modulate their power injections without wasting renewable energy Inthis paper the authors studied the suitability of extending frequency control to RES units integrating them with energy storagesystems In particular the paper focuses on the impact of frequency control on the storage lifetime by analysing the powerchargedischarge in response to real frequency oscillations

1 Introduction

Renewable Energy Sources (RESs) are essential in the per-spective of a green fully sustainable and energetic approachHowever a high RES penetration introduces technical issuesand challenges many aspects of the operation of powersystems (eg power quality system reliability and safety andcontrol and protection strategies) [1] Distributed generation(DG) is not dispatchable like conventional generation anddoes not provide any ancillary service to the system there-fore a large amount of DG stresses the power system opera-tion bringing it closer to the stability margin In particularthe higher the percentage of DG is the lower the reservefor the primary frequency regulation becomes This fact ishighlighted by Figure 1 which shows the primary frequencyregulation reserve in the Italian electrical system in the lastfour years the increase of RES generation has caused thereserve to decrease up to 20ndash25 (eg for the year 2012 aprimary reserve equal to 500MW corresponds to a durationtime the grey dashed line in Figure 1 which for the year2008 had been characterised by a reserve power of 600MW)

Storage apparatuses could be a challenging option in orderto cope with the problem that is they could be adoptedto mitigate the frequency fluctuations in order to guaranteeadequate power quality and security of supply levels [2]

Concerning ancillary services support RES power plantsconnected to the medium voltage (MV) and low voltage (LV)distribution system do not allow an easy and feasible remotecontrollability the installation of a proper communicationsystemmay require long-term investments and large amountsof capital Nevertheless focusing on the primary frequencyregulation one way to address this issue over a short timeperiod is to adopt a local frequency control that exploits thefrequency measurements at the DGrsquos point of common cou-pling (PCC) as a feedback of the status of the system reactingto this signal DG could make a power margin available torestore the power imbalance It means an exploitation of localmeasurements to solve a global issue

Actually the problem is well known and deeply inves-tigated in the literature from the regulatory technical andeconomic point of view [3] reports an international overviewof the already in place practices concerning ancillary services

Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2014 Article ID 198427 13 pageshttpdxdoiorg1011552014198427

2 Journal of Applied Mathematics

0

100

200

300

400

500

600

700

800

900

1 8760

2008

2008

2009

2009

2010

2010

2011

2011

2012

2012

Time (h)

(MW

)

Figure 1 Frequency primary reserve duration curves in the Italiansystem 2008ndash2012 figures [8]

and [4 5] evaluate the possible integration of these servicesin the liberalised market structures References [5ndash7] deeplyinvestigate the market and policy barriersopportunities inorder to manage effectively the ancillary services exploitingalso the possible contribution of passive users (ie demandside management functions)

The goal of this paper is to focus on the DG role withrespect to ancillary services management with particularreference to the primary frequency control

Figure 2 depicts the distribution of DG power plants con-nected to the Italian system according to RES and size thereis clear evidence that the exploitation of the Photovoltaic(PV) renewable source is usually performed by small-sizepower plants (lower than 50 kW) connected to the MV andLV distribution system In 2012 the total amount of PVgeneration in the Italian system was about 16GW [9]

In order to achieve suitable network reliability in thefuture DG will have to provide the primary frequency reg-ulation service If a retrofit of all PV power plants already inplace in Italy were put in place (16GW) by making availablefor example 3 of the rated power for the regulation it wouldbe possible to recover 480MWof primary frequency reserveThis way a proper regulation capability would be reestab-lished and the system stability during frequency oscillationswould be considerably improved

Generally speaking a key factor involves the metricsdesigned in order to define planning and operational require-ments in an electric power system characterised by highpenetration of RESs [10]

In this scenario an effective solution could be to integrateenergy storage systems (ESSs) into DG power plants ESSscould improve the frequency regulation ensuring the energyneeded by the frequency control Furthermore ESSs copebetter with frequency control because they exhibit a rapidactive power response so they are ideal to compensate thegeneration rate constraints of slower generators and thefluctuations produced by RESs

Electric energy can be stored electromagnetically elec-trochemically kinetically thermally or as potential energyTwo factors characterise the application of an energy storage

0

25

50

75

100

Hyd

ro

Win

d

Phot

ovol

taic

Geo

ther

mal

Biom

asse

s

Biog

as

Biol

iqui

d

()

Lower than 50kWFrom 50kW to 200 kWFrom 200 kW to 1MW

From 1MW to 10MWHigher than 10 MW

Figure 2 Distribution [] of the power plants connected to theItalian system according to RES and size [9]

technology one is the amount of energy that can be storedin the device and the other is the rate at which energy canbe transferred into or out of the storage device These factorsare mainly determined by the characteristics of the ESS itself[1 11]

The primary frequency control service provided by anintegrated system of DG and ESS requires ldquopower perfor-mancesrdquo to make energy available over a short time period(ie a high ratio between the power injected and the energystored)

The basic issues regarding storage technologies consistof the cost of these technologies and their operational andmaintenance requirements [12] Since a large-scale ESS israther expensive defining requirements for the frequencycontrol service can become a critical issue it is also evidentthat the application should be tailored to minimise the ESSsize needed

Another important issue regarding the ESS is the state ofcharge (SoC) control It can be shown that the ESS can easilybe depleted or overcharged if no additional control actionsare performed [13]

The converter adopted for the connection of the ESS tothe network plays a key role in the definition of the powerset-point needed for the frequency regulation Different bidi-rectional converter structures are reported in the literaturewhile the control methods make the difference in the ESSrsquosdynamic performances [14] New DG units mainly PV andwind generators are usually coupledwith the ESS bymeans ofa DC-coupled system In this structure the power plant andthe storage are connected to a common DC bus (called DC-link) by proper DCDC converters the system is interfacedto the AC network through a common DCAC inverter Thisis the simplest and cheapest solution for the integration of theESS because it requires the minimum number of convertersand therefore the minimisation of the power losses On thecontrary in anAC-coupled system the generator and the ESSare connected at the AC level requiring a DCAC inverterand a DCDC converter for each component therefore thisscheme implies higher investments and operational costs

Journal of Applied Mathematics 3

In the Italian framework technical regulations alreadyproposed a contribution of DG power plants to ancillaryservices (CEI 0ndash16 [15]) In particular some issues such asthe low voltage fault ride through and the reactive powermodulation are mandatory others such as the contributionto the frequency regulation are at present being defined(several consultation processes are ongoing) In order todevelop an effective technical regulation it is increasinglyimportant to develop realistic analyses concerning the ancil-lary services requirement for the electric grid and the impactof the ancillary services support on the DG This paperfocuses on the latter point in particular the integration ofan ESS in a PV power plant and the impact of the frequencyregulation on the storage device are evaluated The finalgoal is to provide realistic data useful for the ESS designThe paper is structured as follows Section 2 investigates theprimary frequency regulation concept Section 3 describesthe proposed control scheme and the ESS sizing the studycase for the numerical analysis and the test results aredescribed in Section 4 and finally proper conclusions arereported in Section 5

2 Primary Frequency Regulation Proposed forDGs Coupled with ESS

Power system generation portfolios are changing rapidly andthe inertial and dynamic characteristics of many new sourcesof electric generation differ from those of the past The lackof predictable and controllable energy sources together withthe different operating characteristic of DG power plantswith respect to their inertial response poses challengesto transmission system operators (TSOs) trying to controlsystem frequency [16]

In order to address the need to include DG power plantsmainly connected to distribution networks in the primaryfrequency control system the ENTSO-E grid code [17] sug-gests the primary frequency regulation strategy reported inFigure 3 the real power is modulated as function of thefrequency deviation from the nominal value The curve ischaracterised by the following parameters

(i) Frequency response deadband the real power is notmodulated in the case of small frequency deviationsfrom the nominal value

(ii) Droop control during underfrequency deviations thegenerator has to provide a positive real power outputchange according to a droop strategy and on thecontrary during overfrequencies a negative powerchange has to be provided

(iii) Maximum power capability the maximum powerthat the generator is capable of providing

The proposed scheme represents a local strategy forprimary frequency regulation which is possible only bymea-suring the frequency oscillations at the PCC It is importantto point out that in the perspective of each single generationunit the primary frequency service does not require alarge amount of energypower the grid code prescriptionstypically range from 15 to 3 of the generator rated power

Pf

Pmax

minusPmax

Δf

Deadband

minusDeadband

Droop

Droop

Δfmax

minusΔfmax

Figure 3 Local strategy for the primary frequency regulation

over a time ranging from 15minutes to 1 hourThe real powerfrequency response is activated as fast as technically feasiblewith an initial delay that is as short as possible (this delay willbe reasonably justified to the TSO if greater than 2 seconds)The maximum time of full activation has to be 30 seconds

It is worth noting that from the main grid perspectivethe collection of the regulating contribution from all thegenerating units provides a significant amount of poweruseful to guarantee the system stability and security

In this work a mathematical model of an ESS integratedin a PV plant is developed and connected to a simplifiedtest network A combination of static devices and ESSs hasbeen proposed proper control strategies are necessary inorder to achieve full benefits from these technologies A realfrequency oscillation is superimposed on the model Theoutput of the simulation represents the behaviour of the singlestorage module in response to the frequency regulation

The approach proposed is based on the assumptionthat locally (ie for each DG unit) the frequency controlsupport (ie rated at 15ndash3 of the generation unit nominalpower) could be provided by exploiting the ESS in particulartechnically speaking the same prescriptions already in placefor conventional power units have been assumed In such away the main grid behaviour is not supposed to change thatis an increase in DG penetration replacing the conventionalpower plants could be effective thanks to maintaining thesame ancillary service support we had in the past

3 Storage Integration Control Scheme

In this study RMS simulations are carried out consideringthe dynamic model built in the DIgSILENT PowerFactorysoftware environment reported in Figure 4 The model is aDC-coupled system and operates in a grid-connected mode

The PV and the ESS are connected to a simplifiedMV distribution network through an MVLV transformer(INVERTER-Trafo) and aDCAC converter (INVERTER) in agrid-followingmode operation therefore themodel requiresa main grid as frequency reference for the control of itsoutputs

In the configuration the PV system the ESS and theAC network are connected to the DC-link by means of


External grid

Bus 0

Bus 2

Line

2Load 2

V

AC bus

Load 1

Inve

rter

-traf

o

Inverter

C

DCLink

ESS

conv

erte

r

ESS bus

ESS PV

PV bus

VDCLink

MPP

T co

nver

ter

InactiveOut of calculationDe-energized

Voltage levels20kV1kV07 kV04 kV

PAC QAC

MPPT VPV

Figure 4 Network scheme of the PV system integrated with an ESSin a DC-coupled system

appropriate converters in order to match the PV and ESSvoltages and to control the AC side independently The PVsystem is connected to the common DC bus through aDC-DC converter (maximum power point tracking (MPPT)converter) to ensure the optimal power exchange whereasthe ESS is connected using a bidirectional DC-DC converter(ESS converter) to guarantee the charge and discharge of thestorage device

In the following the functions of each component pre-sented in the scheme are listed

(i) The PV system injects a constant power equal to itsown optimal power (119875MPPT)

(ii) The MPPT converter controls the voltage of the PVsystem 119881PV at its own MPPT

(iii) The INVERTER controls the real power 119875AC andreactive power 119876AC injected at the AC side

(iv) The ESS converter controls the voltage at the DC-link(119881DCLink) in order to set the power balance at the DC-link (1)

(v) The ESS injectsabsorbs the power required to matchthe power balance at theDC-link according to the ESSconverter control

The power balance equation at the DC-link is

119875PV minus 119875ESS minus 119875AC = 119862 sdot 119881DCLink sdot119889119881DCLink

119889119905 (1)

where 119875PV is the power produced by the PV system (con-trolled to a set-point equal to the MPPT power 119875MPPT) 119875ESSis the power absorbed by the ESS 119875AC is the power injectedto the AC network by the inverter 119862 is the capacitance ofthe DC-Link and 119881DCLink is the voltage measured at the DC-Link

The 119875PV power is assumed constant and represents anexternal and noncontrollable value of the power balanceequation The injected power 119875AC is determined by the PVpower 119875PV and the power required for the frequency controlSince 119875PV and 119875AC are external inputs in (1) the powerprovided by the ESS119875ESS is exploited to compensate the powermismatch between the PVproduction and the injection to theAC side

On the basis of the aforementioned functions a numeri-cal model of the proposed system has been developed and thecontrol laws of each converter are defined For this purposethe following assumptions are considered

(i) all converters are ideal (losses are not modelled)(ii) static switches commutation is not modelled and

only a model at fundamental frequency is taken intoaccount (assumption in accordance with the RMSdynamic)

(iii) the DC-link capacitor is modelled as an ideal capac-itance (the losses and the auto-discharge phenomenaare not considered)

(iv) the ESS is modelled as a passive first order dynamicmodel

(v) the PV power plant injects a constant power

In the following sections a detailed description of eachcomponent is given

31 PV System The PV system is modelled as a constant DCcurrent source with a parallel internal conductance Duringthe simulation the current source injects a constant currentand the 119881PV voltage is controlled by the MPPT converter tomeet the optimal power injections of the PV

32 MPPT Converter The MPPT converter is a DC-DCconverter that regulates the119881PV voltage to itsMPPT set-point119881MPPT In this way it is possible to extract the maximumpower available from the PV power plant (119875MPPT) and atthe same time to decouple the voltage 119881PV and the DC-linkvoltage 119881DC-Link The control is achieved by means of a PIregulator The output of the regulator is the 120572 ratio used ascontrol variable for the MPPT converter

120572 =119881DCLink119881PV

(2)


33 INVERTER The system is connected to the AC networkthrough an AC-DC inverter (INVERTER) This device pro-vides an interface to the ESS and the PV system for realand reactive power exchanges with the power system TheINVERTER is an ideal lossless PWM converter modelled asa DC-voltage controlled AC-voltage source

119881AC = (119875mr + 119895119875mi) sdot 119881DC-Link (3)

where 119881AC is the AC voltage phasor 119881DC-Link is the voltageat the common DC bus and 119875mr and 119875mi are the pulsemodulation factors on the real and imaginary axes

If a 119889-119902 reference-system fixed to the AC voltage phasoris introduced a decoupled 119875-119876 control can be obtained (asdetailed in [18]) This way the real power 119875AC is determinedonly by the 119889 current component 119868AC119889 whereas the reactivepower 119876AC is determined only by the 119902 current component119868AC 119902 119875AC and 119876AC can be controlled independently Thecontrol scheme adopted for the inverter is shown in Figure 5

Two control loops are employed by the INVERTER inorder to control the real and reactive power injected at theAC side

(i) the outer loop controls 119875AC and 119876AC by defining thecurrent set-points 119868AC119889-ref and 119868AC 119902-ref

(ii) the inner loop controls 119868AC119889 and 119868AC 119902 by defining thepulse modulation factors 119875m119889 and 119875m119902 used by thePWM technique of the inverter

The outer control loop employs slow bandwidth PI regu-lators whereas the PI regulators of the inner loop areusually designed to achieve faster bandwidths [19] Proper 119889-119902 transformations (119903-119894 to119889-119902) and antitransformations (119889-119902 to119903-119894) are adopted to compute 119868AC 119889 119868AC 119902 119875mr and 119875mi a phaselock loop (PLL) is exploited to measure the angle 120579 of the ACvoltage that is necessary for the axes transformations

The overall control has three operational objectives

(i) to perform the normal inverter operation by injectingthe power produced by the PV system (119875MPPT)

(ii) to provide the primary frequency control using adroop control

(iii) to maintain the state of charge (SoC) of the ESS toa predefined value (by means of the SoC restorationfunction)

The inverter controls the real power 119875AC with the follow-ing set-point

119875ACref = 119875MPPT + 119875119891 sdot 119870 + DPrefESS sdot (1 minus 119870) (4)

The reactive power is forced to zero (the system injects powerat power factor equal to one) The set-point 119875ACref of (4) isrepresented in the block scheme of Figure 6

(i) 119875MPPT is the MPPT power of the PV system thisis the power injected by the inverter during normalconditions (ie with no frequency oscillations orcritical conditions for the ESSrsquos SoC)

(ii) 119875119891 is the power change due to the frequency deviationΔ119891 with respect to the nominal value 119891119899 (Δ119891 =

119891 minus 119891119899) computed according to the local strategy ofFigure 3 where

(a) 119875max maximum power capability available forthe control

(b) deadband frequency range of no power modu-lations

(c) droop slope of the section of the curve withpower modulation (Δ119891Δ119875 ratio)

(iii) DPrefESS is a set-point component which leads to therestoration of the SoC of the ESS at the nominalvalue according to the SoC restoration function asexplained in the next section

(iv) 119870 is the flag for the selection among the 119875119891 and theDPrefESS set-points in particular it disables the fre-quency control enabling the SoC restoration functionor vice versa according to the SoC and frequencyoscillation entities

34 SoC Restoration Function In order to operate the systemthe charging level of the ESS has to be regulated not toexceed its operational range that is not to hit the maxi-mumminimum allowed SoC Reference [20] demonstratesthe impact of the SoC restoration function on the ESS designand operation and in particular it shows that the ESS capacitycan be reduced by a high recharge percentage available for thisfunction In the work proposed an SoC restoration functionhas been introduced in the control to keep the SoC of thestorage within its proper range while the storage provides thefrequency regulation serviceThe SoC restoration function isactivated during small frequency oscillation periods withouta degradation of the system frequency quality To this pur-pose the additional charging reference DPrefESS to the ACpower set-point (as depicted in the block diagramof Figure 6)is introducedThe implemented SoC restoration function hasthe following characteristics

(i) it reestablishes the target SoC (50) when the fre-quency is inside a noncritical window (ie the dead-band)

(ii) it uses a small recharge power (a few percentage of therated power)

(iii) if the storage reaches the SoC limits the function isactivated no matter what the frequency value is

The target level of the SoC is assumed in a range betweenthe maximum SoC limit (80) and the minimum SoC limit(20) in order to keep a proper charging reserve for facingboth overfrequency (ESS absorption) and underfrequencyevents (ESS injection)

The 119870 flag of (4) allows the activation of the SoC res-toration set-point as an alternative to the frequency control


Inverterd minus q

d minus q

to

to

r minus i

r minus i

1

1 + sT1

1

1 + sT2

1

1 + sT3

1

1 + sT4

+

Id

Idref

Iqref

Iq

+minus

+

minus

PACref

QACref +

minus

QAC

PAC

minus

Outer loop Inner loop

PLL

cos 120579 sin 120579

ImIr

PI1

PI2

PI3

PI4

Pmr

Pmi

Pmd

Pmq

Figure 5 Control frame of the INVERTER

PMPPT

++

Δf

P = f(Δf)

+

minusVESSref

VESS

K = 1

K = 0

PACref

1

1 + sT

Pf

PIDPrefESS

Figure 6 Control frame for the definition of the AC real power set-point 119875ACref The red box identifies the SoC restoration function

set-point 119875119891 In order to define it the ESS saturation flag119870ESSSat is introduced as follows

119870ESSSat = 119881ESS min le 119881ESS le 119881ESS max for 10 s119881ESS gt 119881ESS max or 119881ESS lt 119881ESS min for 10 s

(5)The ESS is modelled as an 119877-119862 series and its voltage 119881ESSis the variable directly linked with the SoC when themaximum SoC limit is reached the maximum voltage limit119881ESS max is also reached and when the minimum SoC limit isreached the minimum voltage limit 119881ESS min is also reachedTherefore 119870ESSSat is enabled when the ESS is completelycharged (maximum SoC) or discharged (minimum SoC) andit cannot make available energy for the primary frequencycontrol A temporal hysteresis of 10 s is introduced for theactivation and deactivation of this functionality in order toprevent instability Based on (5) the 119870 flag is defined asfollows

119870 = 1 for 119870ESSSat = 1

1003816100381610038161003816Δ1198911003816100381610038161003816 gt deadband

0 for 119870ESSSat = 0 or 1003816100381610038161003816Δ1198911003816100381610038161003816 lt deadband

(6)

Equations (5) and (6) allow the attainment of the afore-mentioned SoC restoration function As shown in the blockdiagram of Figure 6 the set-point DPrefESS is generated by aPI regulator this component is limited to the value DPmaxwhich is much lower than that of 119875max and it has nosignificant effect on the frequency oscillations

Finally in order to prevent important contingencies thatis events driving a significant frequency deviation fromovercharging the ESS thereby causing fast SoC variationsupper and lower bounds to the power output (119875max and119875min in Figure 3) have been introduced vice versa the SoCrestoration function does not directly manage these events

This issue is not considered critical in fact severe contin-gencies are events with a low probability and in modernpower systems are managed collecting the regulating contri-butions from all the resources available or by specific func-tions (eg emergency condition load shedding) Actuallythe proposed DG and ESS coupling would respond like aconventional power plant supporting the system up to itscapability limits 119875max and 119875min


35 ESS Converter TheESS Converter is a bidirectional DC-DC buck-boost converter which controls the119881DC-Link voltageby charging and discharging the ESSmoduleThe converter iscontrolled varying the duty ratio 120572 bymeans of a PI regulatorThe primary objective of the control of the ESS Converter isto maintain the power balance at the DC-Link (1) by meansof the energy stored in the ESS

The DC-link voltage 119881DCLink is compared with the ref-erence 119881DCLink-Ref and the error is taken into a PI regulatorthat outputs the reference current 119868DC-Link which is absorbedat the DC-link by the converter (Figure 6) [19] During theboost mode the ESS provides energy to the DC-link whereasin the buck mode the ESS is charged in order to compensatethe power mismatch between the real power output of theINVERTER and the PV system

Themodel is completed with the power balance equation

119875ESS = 119881ESS sdot 119868ESS = 119881DC-Link sdot 119868DC-Link

120572 =119881DC-Link119881ESS

=119868ESS

119868DC-Link

(7)

where 119881ESS and 119868ESS are the voltage and current measured atthe ESS bus 119881DC-Link and 119868DC-Link are the voltage and currentmeasured at the DC-Link and 120572 is the duty ratio of the ESSconverter

During underfrequencies the INVERTER injects in thenetwork an amount of power equal to 119875MPPT + 119875119891 with 119875119891positive (obtained by the primary frequency control curve)since 119875MPPT is supplied by the PV system 119875119891 is deliveredby the ESS On the contrary during overfrequencies 119875119891 isnegative and the ESS is charged

36 ESSModel TheESS ismodelled through an ideal capaci-tance and an equivalent series resistance the dynamic modelis

119881ESS = 119881119862 + 119877 sdot 119868ESS = 119881119862 + 119877 sdot119875ESS119881ESS

119889119881119862

119889119905=

1

119862ESSsdot 119868ESS =

1

119862ESSsdot119875ESS119881ESS

(8)

where 119877 is the resistance of the ESS model 119862 is the capaci-tance of the ESS model and 119881119862 is the voltage of the capacitor119862ESS

For the purpose of the analysis a simple ESS model isadopted and fixed values of 119877 and 119862 are considered It is noteasy to develop a general model of ESS technologies usedfor power applications For instance the existing models inthe literature for chemical battery technologies are generallycomplicated to set up because of their relative complexity(several parameters to be determined [21]) so a simple firstorder dynamic circuit is considered appropriate to model thecharge and discharge phenomena of the ESS As mentionedabove the voltage 119881ESS represents the SoC of the ESS whenthe ESS injects power to the DC-link (discharge phenomena)the voltage decreases on the contrary in the case of powerabsorptions (charge phenomena) the voltage increases TheESS voltage will drop to zero when all the stored energy is

supplied in the same manner it will go to overvoltage valueswhen a big amount of energy is absorbed These two borderoperational points will affect the stability and the efficiency ofthe operation of the ESS converter and the performances ofthe ESSmodule therefore a lower limit119881ESS min and an upperlimit 119881ESS max are introduced in the model

4 Study Case

In this section the approach proposed is evaluated on a real-istic grid model In particular the dynamic model of an ESScoupled with a PV system is exploited to study the effect ofthe primary frequency regulation on the ESS To this purposedynamic RMS simulations are carried out In the model thePV system injects a constant amount of power whereas theESS injectsabsorbs power according to the control strategy ofFigure 3 The behaviour of the ESS is analysed by evaluatingits response to a real one-day frequency oscillation of theelectric system With this aim the external grid modelled asan ideal voltage source is used to impose a standard one-dayfrequency oscillation of the ENTSO-E systemThe frequencyoscillation is represented by a signal of 86400 samples with aresolution of one sample per second It is computed startingfrom data available in [22] where the frequency in thesynchronous grid of Continental Europe is measured onlineThemaximumerror of the frequencymeasurement is definedby the ENTSO-E (10mHz) The error of the measurementunder consideration is below 1mHz at a frequency of 50Hzwhich corresponds to an accuracy of 0002 The one-dayfrequency oscillation is depicted in Figure 7

The frequency oscillation vector is computed to obtain theprobability density function (PDF) reported in Figure 8 Inthe same graph the deadband and the maximum deviationΔ119891max of the frequency control strategy are shown 97 ofthe frequency samples fall inside the Δ119891max = 50mHzwhereas 611 of the oscillations is inside a deadband equalto 20mHz Adopting this setting more than half of thefrequency oscillations does not cause any power modulation(samples inside the deadband) on the contrary only 3of the frequency oscillations exceed the Δ119891max causing asaturation of the control

The main observation from a study of the data is that thefrequency quality in ENTSO-E is maintained very well Mostof the time (611) the frequency in ENTSO-E stays withinthe deadband (which is considered as a noncritical window)There are only few frequency excursions outside of plusmn50mHzper day A frequency deviation of plusmn200mHz was neverreached throughout 2005 [20] High and low frequency devi-ations are symmetrical over a long period (24 hours) In theshort term however there are many deviations and they arenot necessarily balancedThey can occur at random points intime with random amplitude and random repetition

41 Network Data and Component Sizing In this section thenetwork data and sizing of components are reported Typicalparameters and standard sizes of the components for thisuse are adopted In the following a list of parameter valuesadopted for the model is given


000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f

Figure 7 One-day frequency oscillation

0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()

Figure 8 PDF of the frequency oscillation vector deadband andΔ119891max of the frequency control strategy

411 AC Network

(i) MV network 20 kV rated voltage(ii) LV network 400V rated voltage(iii) INVERTER 250 kVA rated power and 200 kWpower

in the steady state(iv) External grid it operates as voltage and frequency

slack imposing a voltage with constant amplitude of102 pu and the frequency oscillations of Figure 7(it represents a real one-day frequency oscillation asdescribed in the following)

(v) Data of other elements connected to the AC networkare not significant for the purpose of the study

412 Local Control Strategy for Primary Frequency Regulation

(i) Droop = 2(ii) Deadband = 20mHz(iii) 119875max = 3

The set-point component DPrefESS related to the SoCrestoration function can assume a maximum value DPmax of

25 kW The values of droop deadband and DPmax are notimposed by any prescription but they are only adopted asreference settings for the numerical simulation these settingsare changed during the analysis in order to estimate thebehaviour of the ESS for different control laws

413 DC-Link

(i) 700V rated voltage(ii) 119862 = 30mF

414 PV System

(i) 1000V rated voltage(ii) 200 kW constant power injections equal to theMPPT

415 ESS The ESS capacity is modelled by following theENTSO-E prescriptions related to the primary frequencycontrol the storage has to provide the maximum capability119875max (3 of 119875119899) continuously for a period 119879 of 15 minutes

For the case under analysis a rated power of 250 kWimplies a maximum energy for the frequency regulationservice equal

119864 = 119875 sdot 119879 = 003 sdot 119875119899 sdot 15 sdot 60

= 003 sdot 250 sdot 103sdot 15 sdot 60 = 675MJ

(9)

The ESS has to provide the amount of energy of (9) duringboth a charge and a discharge phenomenon During thesteady-state (constant frequency) the power modulated bythe ESS is zero and it should stay at an optimum SoC valuein order to guarantee maximum energy both during under-frequency and overfrequency this optimum value is 50 Itis also assumed an ESS operational range between 20 and80 of its capability that is a 20 margin with respect toa complete charge and discharge The energy 119864 correspondsto the available band of the SoC between the optimum valueand the maximumminimum limit and therefore it is 30of the theoretical ESS capability As a consequence of this themaximum capability of the ESS should be 119864max ESS = 225MJ

Considering a rated voltage of the ESS 119881ESS119899 = 1000V(related to a SoC = 100) the minimum capacity 119862ESSnecessary to satisfy the energetic requirement is 119862ESS = 45 FTheminimum suitable capacitance results to be a realistic sizefor a supercapacitor storage technology it can be attained bycommercially available sizes Therefore the series resistancevalue adopted for the ESS model is set equal to a value of thecommercial supercapacitor (1mΩ) [23]

At this point it is necessary to compute the voltage 119881ESSrelated to each of the aforementioned SoC values the voltage119881ESS119909 related to a generic state of charge SoC119909 (the latterexpressed in ) is

119881ESS119909 = radic2 (SoC119909100) 119864max ESS

119862ESS (10)

Figure 9 shows the 119881ESS119909 voltage corresponding to ageneric SoC119909 value In particular the voltage correspondingto a SoC = 50 is 7071 V (07071 pu) which corresponds to


0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)

Figure 9 Axis of the generic SoC119909 values [] of the ESS and thecorresponding voltage value 119881ESS119909 [V]

Table 1 PI regulator settings of each converter

Converter 119870 (pu) 119879 (s)INVERTER inner loop 02 0005INVERTER outer loop 005 0015SOC restoration 1 01MPPT converter 2 001ESS converter 2 01

the optimumSoC to guarantee a proper energymargin for thefrequency regulation service A discharge from 50 to 20of the SoC implies a voltage decrease from 7071 V to 4471 Vvice versa a charge cycle to reach the upper SoC limit of 80entails a voltage increase to 8944V

416 Converter Control Parameters Proper settings of PIregulators of each static converter are defined in order tosatisfy the dynamic requirements of the primary frequencycontrol service The main dynamic performances accordingto the ENTSO-E grid code are

(i) to provide 100 of the required power within 30seconds

(ii) to provide 50 of the required power within 15seconds

In Table 1 the control gain 119870 and the time constant 119879adopted of each PI controller are reported

42 Numerical Analyses Several dynamic simulations werecarried out considering different settings of the frequencycontrol strategy (droop and deadband) and different param-eters of the regulatorrsquos converters (119870119901 and 119870119902 gains of theINVERTER outer loop and the DPmax power available forthe SoC restoration function) The modified parameters arereported in Table 2 whereas the rest of the parameters are setto the values reported in Section 3The aim of the simulationis to verify (i) the impact of the primary frequency regulationon the ESS and (ii) how this effect varies based on the settings

On the basis of the settings of Table 2 the probabilityof the frequency oscillations being within the deadbandand Δ119891max of the frequency control curve is computed (asreported in Table 3)

During the 24 h simulation the inverter reads the fre-quency oscillations and modulates the real power on the ACside 119875AC according to the frequency control strategyThe realpower required for this service is absorbedinjected by theESS connected to the DC side of the inverter while the PVsystem injects a noncontrollable and fixed power

In order to evaluate the impact of the regulation on theESS the power delivered by the ESS is elaborated and the

000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000

CM ESS P ESS in pu (base 000)

Y = 7500 pu

Y = minus7500pu

Figure 10 One-day power absorbed by the ESSmdashSet 1

following energetic indices are computed (they represent theenergetic response of the ESS to frequency oscillations)

(i) 119864ch the total charge energy of the ESS(ii) 119864dis the total discharge energy of the ESS(iii) 119864 the total energy of the ESS(iv) 119864mch the energymarginwith respect to themaximum

availability 119875max during the charge phase(v) 119864mdis the energy margin with respect to the maxi-

mum availability 119875max during the discharge phase(vi) ℎch the equivalent hours at the maximum capability

119875max during the charge phase(vii) ℎdis the equivalent hours at the maximum capability

119875max during the discharge phase(viii) 119873ch the equivalent number of complete charge cycles(ix) 119873dis the equivalent number of complete discharge

cycles

The real power delivered by the ESS in compliance withthe frequency control strategy of Set 1 is shown in Figure 10while Figure 11 reports the zoomof the power absorbed by theESS The two power limits 1198791 and 1198792 of the control strategyare highlighted the former is activated when the maximumpower capability available for the frequency control 119875maxis reached (75 kW) and the latter is activated by the SoCrestoration function when the frequency oscillations arewithin the deadband (25 kW) The voltage of the ESS isshown in Figure 12 it can be noticed that during the one-daypower modulation the maximum voltage limit (0849 pu) isreached and the saturation flag119870ESSSat of the SoC restorationfunction (5) becomes zero

In Table 4 the equivalent chargedischarge cycles of theESS are reported for each of the settings of Table 2

The index 119873ch is exploited for a comparative analysis ofthe results obtained by the different settings this index isdirectly connected to the energy adopted for the frequencyregulation On the basis of the results of Table 4 it can be


Table 2 Simulation settings

Droop DfDP() Deadband (Hz) Δ119891max (Hz) 119881ESSmin (pu) 119881ESSmax (pu) DPmax (MW)

119870119901-119870119902

P Q Ctrl(pupu)

Set 1 2 002 005 08944 04472 00025 005Set 2 2 001 004 08944 04472 00025 005Set 3 2 004 007 08944 04472 00025 005Set 4 1 002 0035 08944 04472 00025 005Set 5 4 002 008 08944 04472 00025 005Set 6 2 002 005 08944 04472 00025 08Set 7 2 002 005 08944 04472 00025 0015Set 8 2 002 005 1 0 0 005Set 9 2 002 005 08944 04472 000025 005Set 10 2 002 005 08944 04472 000125 005

1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2

Figure 11 Power absorbed by the ESS in 5 hmdashSet 1

000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447

Figure 12 One-day voltage of the ESSmdashSet 1

Table 3 Probability of the frequency values for different settings

Deadband(Hz)

119891 indeadband Δ119891max (Hz) 119891 in

Δ119891max

Set 1 002 6113 005 97Set 2 001 3296 004 9172Set 3 004 9172 007 9978Set 4 002 6113 0035 8706Set 5 002 6113 008 9995Set 6 002 6113 005 97Set 7 002 6113 005 97Set 8 002 6113 005 97Set 9 002 6113 005 97Set 10 002 6113 005 97

stated that the parameters deadband and droop of the curvehave a significant impact on the energymodulated by the ESSin particular

(i) by increasing the deadband (from Set 1 to Set 3) thenumber of equivalent cycles 119873ch strongly decreases(from 5645 to 2818) similarly by increasing thedeadband value (from Set 1 to Set 2)119873ch increases to7484

(ii) by increasing the droop (from Set 1 to Set 5) thenumber of equivalent cycles 119873ch strongly decreases(from 5645 to 3995) similarly by increasing thisparameters (fromSet 1 to Set 4)119873ch increases to 6530

The SoC restoration function also affects the energyinvolved by the ESS when it is disabled (from Set 1 to Set 8)the number of equivalent cycles 119873ch decreases (from 5645to 3109) and the ESS is less stressed for chargedischargecycles nevertheless the SoC is not under control and theenergy availability for the primary frequency regulation canbe compromised


Table 4 Energetic indices computed from the 119875ESS for each simulation setting

Setting 119864ch (MJ) 119864dis (MJ) 119864 (MJ) 119864mch (MJ) 119864mdis (MJ) ℎch ℎdis 119873ch 119873dis

Set 1 7620 minus7490 130 57180 57310 2822 2774 5645 5549Set 2 10102 minus9628 475 54698 55172 3742 3566 7484 7132Set 3 3805 minus4125 minus320 60995 60675 1409 1528 2818 3056Set 4 9060 minus8815 244 55740 55985 3355 3265 6711 6530Set 5 5393 minus5229 164 59407 59571 1997 1937 3995 3873Set 6 7571 minus7421 150 57229 57379 2804 2748 5609 5497Set 7 7582 minus7449 133 57218 57351 2808 2759 5616 5518Set 8 4197 minus4317 minus120 60603 60483 1554 1599 3109 3198Set 9 4772 minus4507 266 60028 60293 1767 1669 3535 3338Set 10 63417 minus6014 328 58458 58786 2349 2227 4698 4455

Furthermore different dynamic responses of the invertercontrol are tested by acting on the 119870119901 and 119870119902 gains of theouter loop of the INVERTER By speeding up the controlperformances (from Set 1 to Set 6) or slowing down thetransient response (from Set 1 to Set 7) the number ofequivalent cycles119873ch does not significantly change It meansthat the dynamic performances of the control do not affectthe ESS energy modulation and lifetime In Table 5 the timeperiods in which the119875ESS reaches 100 and 50 ofmaximumcapability 119875max are reported

In addition to the energetic indices computed by exploit-ing the power 119875ESS the voltage oscillations at the 119877-119862 system119881ESS are analysed in order to evaluate the SoC behaviourduring the primary frequency control for each of the settingsunder study The minimum value maximum value meanvalue and standard deviation are reported in Table 6 themonitoring of the voltage 119881ESS is very important to supervisethe state of the ESS and the availability of energy for thefrequency regulation service The red cells indicate that themaximum or minimum SoC are reached and that the ESSis not able to provide the amount of energy required for theserviceThe results show that in almost all cases the SoC limitsof 20 and 80 are reached and only in the cases in whichthe ESS is less stressed the SoC does not saturate that is withlarge deadbands (Set 3) and high droops (Set 5)

Table 7 reports for each setting the percentage of samplesin which 119881ESS exceeds the maximum and minimum thresh-olds In the settings with the highest energy involved (Set 2and Set 4) the number of violations is higher than 2 InSet 8 the SoC restoration function is not activated and thevoltage reaches the minimum value for the highest amountof time (1069) this demonstrates the usefulness of an SoCrestoration function

By comparing 119864ch and 119864dis it can be concluded that onthe basis of the parameters of Set 1 the primary frequencycontrol requires a similar amount of energy both during thecharging and the discharging processes The energy margins119864mch and 119864mdis are quite high and the equivalent hours inthe one-day simulation ℎch and ℎdis are lower than 3 h (Set1 Table 4) it means that many partial charge and dischargecycles occur As a consequence of this and considering anoperative range of 20ndash80 of the SoC 5645 and 5549equivalent charge and discharge cycles per day are completed

Table 5 Percentage of 119875ESS values at 100 and 50 of 119875max

Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max


Table 6 Indices computed from the voltage value 119881ESS (equivalentSoC) for each simulation setting

Setting Min (pu) Max (pu) Mean (pu) Std (pu)Set 1 04846 08963 07073 00866Set 2 04434 09044 06964 01052Set 3 05964 07895 07068 00485Set 4 04309 08986 07010 01073Set 5 05293 08831 07140 00697Set 6 04819 08963 07075 00863Set 7 04844 08963 07074 00865Set 8 02425 08946 06755 01633Set 9 04434 08962 06810 01324Set 10 04439 08962 06971 00832

The available storage solutions can manage 5000 charge anddischarge cycles which means that if they are employed forthis application they last 24 yearsThe setting values also playan important role in the amount of energy involved by theESS the system lifetime can be even lower if the ESS is moreinvolved in the service on the contrary it could be higher butpaying with lower regulation performances The ESS lifetimecomparison for each simulation setting is reported in Table 8

This application may have an important impact on thestorage life durationTherefore the simulation outcomes119873ch


Table 7 Percentage of 119881ESS values 119881ESS max and 119881ESS min

Setting 119906+ 100 119906max 119906minus 100 119906min

Set 1 019 0Set 2 276 240Set 3 0 0Set 4 221 215Set 5 0 0Set 6 026 0Set 7 018 0Set 8 138 1069Set 9 035 180Set 10 042 019

Table 8 ESS lifetime for each simulation setting

Setting ESS lifetime (years)Set 1 24Set 2 19Set 3 47Set 4 21Set 5 35Set 6 25Set 7 25Set 8 43Set 9 4Set 10 3

and119873dis are useful for an economic and technical evaluationof the ESS sizing Anyway a suitable trade-off between thefrequency regulation service and the ESS costs is needed

Finally a stability issue is worth noting for electric gridscharacterised by a decentralised control of a large amountof DG Reference [24] demonstrates that DGs may startinteracting with each other andor with conventional powerplants leading to small-signal instability problems but thesephenomena are limited to electrically close generators Toensure stability of future distribution systems new cen-tralised and decentralised control schemes will be requiredOne of the possible solutions for the short-term scenario is tointroduce a time-constant decoupling between conventionalpower plants and DG in particular as in the work reportedin this paper exploiting the fast dynamic of the ESS in orderto perform frequency control faster As detailed in Table 4such a solution does not significantly affect the ESS energymodulation and lifetime Moreover in order to avoid DGoscillations DSOs (distribution system operators) will haveto technically check the local distribution grid in order toavoid the connection of ESSs electrically close to each other

5 Conclusion

The provision of an adequate power reserve through DGpower plants and ESSs is important for a secure network

it allows TSOs to keep the power system load and genera-tion always balanced avoidinglimiting frequency deviationsfrom the rated value

This work explores the effect on the ESS related to thefrequency control to be extended to renewable DG units sothat they can provide the frequency regulation service like aconventional generator

The study also shows that the control settings are otherimportant parameters that contribute to the ESS perfor-mances and lifetime duration The results of the numericalsimulations show that chargedischarge frequency expressedas number of daily equivalent chargedischarge cycles canbe relatively high in this application and hence the servicemeans the apparatus involved has a relevant effect in terms oflifetime maintenance and operation costs

The control scheme considered introduces the SoC resto-ration concept to restore the optimal energy state of the ESSThe SoC restoration function makes a compromise becauseit deteriorates the ESS lifetime in order to guarantee a fullutilisation of the ESS capacity for the frequency regulationservice

Finally note that this paper focuses on the impact ofthe frequency regulation service which could become amandatory task for all power plants to the ESS and on how itscontrol should be designed Hence this paper could be usefulto provide the engineering data needed for the economicassessment of deploying such an ESS in practice for networkservice applications

Conflict of Interests

The authors declare that they have no conflict of interestsregarding the publication of this paper

References

[1] S Teleke M E Baran A Q Huang S Bhattacharya and LAnderson ldquoControl strategies for battery energy storage forwind farm dispatchingrdquo IEEE Transactions on Energy Conver-sion vol 24 no 3 pp 725ndash732 2009

[2] G Callegari P Capurso F Lanzi M Merlo and R ZaottinildquoWind power generation impact on the frequency regulationstudy on a national scale power systemrdquo in Proceedings ofthe 14th International Conference on Harmonics and Quality ofPower (ICHQP rsquo10) pp 1ndash6 Bergamo Italy September 2010

[3] CIGRE Ancillary Services An Overview of International Prac-tices CIGREWorking Group C5 06 2010

[4] ldquoSurvey onAncillary Services ProcurementampBalancingmarketdesignrdquo 2012 httpswwwentsoeeufileadminuser uploadlibraryresourcesBAL121022 Survey on AS Procurementand EBM designpdf

[5] R Raineri S Rıos and D Schiele ldquoTechnical and economicaspects of ancillary services markets in the electric powerindustry an international comparisonrdquo Energy Policy vol 34no 13 pp 1540ndash1555 2006

[6] P Cappers A Mills C Goldman R Wiser and J H Eto ldquoAnassessment of the role mass market demand response couldplay in contributing to the management of variable generationintegration issuesrdquo Energy Policy vol 48 pp 420ndash429 2012


[7] P Cappers J MacDonald C Goldman and O Ma ldquoAnassessment of market and policy barriers for demand responseproviding ancillary services in US electricity marketsrdquo EnergyPolicy vol 62 pp 1031ndash1039 2013

[8] F Del Pizzo and CEO Terna Storage Build-Up of Italian PowerMarket and Interconnections to Neighboring Systems DubaiUnited Arab Emirates 2013

[9] GSE Gestore Servizi Energetici Statistical report 2012 Renew-able power plantsmdashElectrical sector in Italian

[10] J Eto J Undrill P Mackin et al Use of Frequency ResponseMetrics to Assess the Planning and Operating Requirements forReliable Integration of Variable Renewable Generation LawrenceBerkeley National Laboratory Berkeley Calif USA 2010

[11] P F Ribeiro B K Johnson M L Crow A Arsoy and YLiu ldquoEnergy Storage systems for Advances PowerApplicationsrdquoProceedings of the IEEE vol 89 no 12 pp 1744ndash1756 2001

[12] Bonneville Power Administration ldquoRenewable Energy Tech-nology Roadmap Technology Innovation Officerdquo 2006 httpwwwbpagovcorporatebusinessinnovationdocs2006RM-06 Renewables-Finalpdf

[13] K Yoshimoto N Toshiya G Koshimizu and Y Uchida ldquoNewcontrol method for regulating State-of-Charge of a battery inhybrid wind powerbattery energy storage systemrdquo in Proceed-ings of the IEEE PES Power Systems Conference and Exposition(PSCE rsquo06) pp 1244ndash1251 Atlanta Ga USA November 2006

[14] I Serban and C Marinescu ldquoAn enhanced three-phase batteryenergy storage system for frequency control in microgridsrdquo inProceedings of the 13th International Conference onOptimizationof Electrical and Electronic Equipment (OPTIM rsquo12) pp 912ndash918Brasov Romania May 2012

[15] Italian Electrical Committee (CEI) Technical Standard CEI 0-16 Reference Technical Rules for the Connection of Active andPassive Consumers to the HV and MV Electrical Networks ofDistribution Company CEI Milan Italy 2012

[16] RDoherty AMullaneGNolanD J Burke A Bryson andMOMalley ldquoAn assessment of the impact of wind generation onsystem frequency controlrdquo IEEE Transactions on Power Systemsvol 25 no 1 pp 452ndash460 2010

[17] ENTSO-E ldquoNetwork Code for Requirements for Grid Connec-tion applicable to all Generatorsrdquo 2012 httpswwwentsoeeu

[18] A H M A Rahim and M A Alam ldquoFast low voltage ride-through of wind generation systems using supercapacitor basedenergy storage systemsrdquo in Proceedings of the 4th InternationalConference on Modeling Simulation and Applied Optimization(ICMSAO rsquo11) pp 1ndash6 Kuala Lumpur Malaysia April 2011

[19] P SrithornM Sumner L Yao andR Parashar ldquoThe control of astatcomwith supercapacitor energy storage for improved powerqualityrdquo in Proceedings of the CIRED Seminar 2008 SmartGridsfor Distribution Frankfurt Germany June 2008

[20] A Oudalov D Chartouni and C Ohler ldquoOptimizing a batteryenergy storage system for primary frequency controlrdquo IEEETransactions on Power Systems vol 22 no 3 pp 1259ndash12662007

[21] M B Camara H Gualous F Gustin and A Berthon ldquoDesignand new control of DCDC converters to share energy betweensupercapacitors and batteries in hybrid vehiclesrdquo IEEE Transac-tions on Vehicular Technology vol 57 no 5 pp 2721ndash2735 2008

[22] ldquoNetzfrequenzmessung Beispieltag der Netzfrequenzmessungmit Minuten- und Sekundenwertenrdquo 2011 httpswwwnetz-frequenzmessungde

[23] V Musolino L Piegari and E Tironi ldquoNew full-frequency-range supercapacitormodel with easy identification procedurerdquoIEEE Transactions on Industrial Electronics vol 60 no 1 pp112ndash120 2013

[24] M Honarvar Nazari M Ilic and J Pecas Lopes ldquoSmall-signalstability and decentralized control design for electric energysystems with a large penetration of distributed generatorsrdquoControl Engineering Practice vol 20 no 9 pp 823ndash831 2012

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


0

100

200

300

400

500

600

700

800

900

1 8760

2008

2008

2009

2009

2010

2010

2011

2011

2012

2012

Time (h)

(MW

)

Figure 1 Frequency primary reserve duration curves in the Italiansystem 2008ndash2012 figures [8]

and [4 5] evaluate the possible integration of these servicesin the liberalised market structures References [5ndash7] deeplyinvestigate the market and policy barriersopportunities inorder to manage effectively the ancillary services exploitingalso the possible contribution of passive users (ie demandside management functions)

The goal of this paper is to focus on the DG role withrespect to ancillary services management with particularreference to the primary frequency control

Figure 2 depicts the distribution of DG power plants con-nected to the Italian system according to RES and size thereis clear evidence that the exploitation of the Photovoltaic(PV) renewable source is usually performed by small-sizepower plants (lower than 50 kW) connected to the MV andLV distribution system In 2012 the total amount of PVgeneration in the Italian system was about 16GW [9]

In order to achieve suitable network reliability in thefuture DG will have to provide the primary frequency reg-ulation service If a retrofit of all PV power plants already inplace in Italy were put in place (16GW) by making availablefor example 3 of the rated power for the regulation it wouldbe possible to recover 480MWof primary frequency reserveThis way a proper regulation capability would be reestab-lished and the system stability during frequency oscillationswould be considerably improved

Generally speaking a key factor involves the metricsdesigned in order to define planning and operational require-ments in an electric power system characterised by highpenetration of RESs [10]

In this scenario an effective solution could be to integrateenergy storage systems (ESSs) into DG power plants ESSscould improve the frequency regulation ensuring the energyneeded by the frequency control Furthermore ESSs copebetter with frequency control because they exhibit a rapidactive power response so they are ideal to compensate thegeneration rate constraints of slower generators and thefluctuations produced by RESs

Electric energy can be stored electromagnetically elec-trochemically kinetically thermally or as potential energyTwo factors characterise the application of an energy storage

0

25

50

75

100

Hyd

ro

Win

d

Phot

ovol

taic

Geo

ther

mal

Biom

asse

s

Biog

as

Biol

iqui

d

()

Lower than 50kWFrom 50kW to 200 kWFrom 200 kW to 1MW

From 1MW to 10MWHigher than 10 MW

Figure 2 Distribution [] of the power plants connected to theItalian system according to RES and size [9]

technology one is the amount of energy that can be storedin the device and the other is the rate at which energy canbe transferred into or out of the storage device These factorsare mainly determined by the characteristics of the ESS itself[1 11]

The primary frequency control service provided by anintegrated system of DG and ESS requires ldquopower perfor-mancesrdquo to make energy available over a short time period(ie a high ratio between the power injected and the energystored)

The basic issues regarding storage technologies consistof the cost of these technologies and their operational andmaintenance requirements [12] Since a large-scale ESS israther expensive defining requirements for the frequencycontrol service can become a critical issue it is also evidentthat the application should be tailored to minimise the ESSsize needed

Another important issue regarding the ESS is the state ofcharge (SoC) control It can be shown that the ESS can easilybe depleted or overcharged if no additional control actionsare performed [13]

The converter adopted for the connection of the ESS tothe network plays a key role in the definition of the powerset-point needed for the frequency regulation Different bidi-rectional converter structures are reported in the literaturewhile the control methods make the difference in the ESSrsquosdynamic performances [14] New DG units mainly PV andwind generators are usually coupledwith the ESS bymeans ofa DC-coupled system In this structure the power plant andthe storage are connected to a common DC bus (called DC-link) by proper DCDC converters the system is interfacedto the AC network through a common DCAC inverter Thisis the simplest and cheapest solution for the integration of theESS because it requires the minimum number of convertersand therefore the minimisation of the power losses On thecontrary in anAC-coupled system the generator and the ESSare connected at the AC level requiring a DCAC inverterand a DCDC converter for each component therefore thisscheme implies higher investments and operational costs










Pf

Pmax

minusPmax

Δf

Deadband

minusDeadband

Droop

Droop

Δfmax

minusΔfmax











External grid

Bus 0

Bus 2

Line

2Load 2

V

AC bus

Load 1

Inve

rter

-traf

o

Inverter

C

DCLink

ESS

conv

erte

r

ESS bus

ESS PV

PV bus

VDCLink

MPP

T co

nver

ter



PAC QAC

MPPT VPV











119889119905 (1)












120572 =119881DCLink119881PV

(2)
































Inverterd minus q

d minus q

to

to

r minus i

r minus i

1

1 + sT1

1

1 + sT2

1

1 + sT3

1

1 + sT4

+

Id

Idref

Iqref

Iq

+minus

+

minus

PACref

QACref +

minus

QAC

PAC

minus


PLL

cos 120579 sin 120579

ImIr

PI1

PI2

PI3

PI4

Pmr

Pmi

Pmd

Pmq


PMPPT

++

Δf

P = f(Δf)

+

minusVESSref

VESS

K = 1

K = 0

PACref

1

1 + sT

Pf

PIDPrefESS





119870 = 1 for 119870ESSSat = 1

1003816100381610038161003816Δ1198911003816100381610038161003816 gt deadband


(6)









120572 =119881DC-Link119881ESS

=119868ESS

119868DC-Link

(7)





119889119881119862

119889119905=

1


1


(8)




4 Study Case






000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f


0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()


411 AC Network









413 DC-Link


414 PV System






(9)





119862ESS (10)



0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















Pf

Pmax

minusPmax

Δf

Deadband

minusDeadband

Droop

Droop

Δfmax

minusΔfmax











External grid

Bus 0

Bus 2

Line

2Load 2

V

AC bus

Load 1

Inve

rter

-traf

o

Inverter

C

DCLink

ESS

conv

erte

r

ESS bus

ESS PV

PV bus

VDCLink

MPP

T co

nver

ter



PAC QAC

MPPT VPV











119889119905 (1)












120572 =119881DCLink119881PV

(2)
































Inverterd minus q

d minus q

to

to

r minus i

r minus i

1

1 + sT1

1

1 + sT2

1

1 + sT3

1

1 + sT4

+

Id

Idref

Iqref

Iq

+minus

+

minus

PACref

QACref +

minus

QAC

PAC

minus


PLL

cos 120579 sin 120579

ImIr

PI1

PI2

PI3

PI4

Pmr

Pmi

Pmd

Pmq


PMPPT

++

Δf

P = f(Δf)

+

minusVESSref

VESS

K = 1

K = 0

PACref

1

1 + sT

Pf

PIDPrefESS





119870 = 1 for 119870ESSSat = 1

1003816100381610038161003816Δ1198911003816100381610038161003816 gt deadband


(6)









120572 =119881DC-Link119881ESS

=119868ESS

119868DC-Link

(7)





119889119881119862

119889119905=

1


1


(8)




4 Study Case






000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f


0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()


411 AC Network









413 DC-Link


414 PV System






(9)





119862ESS (10)



0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










External grid

Bus 0

Bus 2

Line

2Load 2

V

AC bus

Load 1

Inve

rter

-traf

o

Inverter

C

DCLink

ESS

conv

erte

r

ESS bus

ESS PV

PV bus

VDCLink

MPP

T co

nver

ter



PAC QAC

MPPT VPV











119889119905 (1)












120572 =119881DCLink119881PV

(2)
































Inverterd minus q

d minus q

to

to

r minus i

r minus i

1

1 + sT1

1

1 + sT2

1

1 + sT3

1

1 + sT4

+

Id

Idref

Iqref

Iq

+minus

+

minus

PACref

QACref +

minus

QAC

PAC

minus


PLL

cos 120579 sin 120579

ImIr

PI1

PI2

PI3

PI4

Pmr

Pmi

Pmd

Pmq


PMPPT

++

Δf

P = f(Δf)

+

minusVESSref

VESS

K = 1

K = 0

PACref

1

1 + sT

Pf

PIDPrefESS





119870 = 1 for 119870ESSSat = 1

1003816100381610038161003816Δ1198911003816100381610038161003816 gt deadband


(6)









120572 =119881DC-Link119881ESS

=119868ESS

119868DC-Link

(7)





119889119881119862

119889119905=

1


1


(8)




4 Study Case






000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f


0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()


411 AC Network









413 DC-Link


414 PV System






(9)





119862ESS (10)



0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra








































Inverterd minus q

d minus q

to

to

r minus i

r minus i

1

1 + sT1

1

1 + sT2

1

1 + sT3

1

1 + sT4

+

Id

Idref

Iqref

Iq

+minus

+

minus

PACref

QACref +

minus

QAC

PAC

minus


PLL

cos 120579 sin 120579

ImIr

PI1

PI2

PI3

PI4

Pmr

Pmi

Pmd

Pmq


PMPPT

++

Δf

P = f(Δf)

+

minusVESSref

VESS

K = 1

K = 0

PACref

1

1 + sT

Pf

PIDPrefESS





119870 = 1 for 119870ESSSat = 1

1003816100381610038161003816Δ1198911003816100381610038161003816 gt deadband


(6)









120572 =119881DC-Link119881ESS

=119868ESS

119868DC-Link

(7)





119889119881119862

119889119905=

1


1


(8)




4 Study Case






000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f


0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()


411 AC Network









413 DC-Link


414 PV System






(9)





119862ESS (10)



0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra














120572 =119881DC-Link119881ESS

=119868ESS

119868DC-Link

(7)





119889119881119862

119889119905=

1


1


(8)




4 Study Case






000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f


0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()


411 AC Network









413 DC-Link


414 PV System






(9)





119862ESS (10)



0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










000 500 1000 1500 2000 2500

495

49850

502

505

(Hz)

(h)

Deadband

Δfmax

CM fctrl f


0

2

4

6

8

10

12

14

16

18

20

4985 4990 4995 5000 5005 5010 5015

DeadbandΔfmax

Frequency (Hz)

PDF

()


411 AC Network









413 DC-Link


414 PV System






(9)





119862ESS (10)



0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










0 4472 7071 8944 1000

0 20 50 80 100 SOCx ()

VESSx (V)













000 500 1000 1500 2000 2500

(h)

minus1500

minus1000

minus500

000

500

1000


Y = 7500 pu

Y = minus7500pu








cycles







119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












119870119901-119870119902

P Q Ctrl(pupu)


1400 1500 1600 1700 1800 1900

(kW

)

(h)

minus1500

minus1000

minus500

000

500

1000


T1

T2


000 500 1000 1500 2000 2500

(h)

0400

0525

0650

0775

0900

1025

CM ESS V ESS

Y = 0894

Y = 0707

Y = 0447



Deadband(Hz)


Δ119891max















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















Setting 119875+ 100119875max

119875minus 100119875max

119875+ 50119875max

119875minus 50119875max














5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra

















5 Conclusion









References

































Volume 2014




Journal of











Journal of


Function Spaces






Algebra



































Volume 2014




Journal of











Journal of


Function Spaces






Algebra









distributed generation integration in the electric grid: energy

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