desenvolvimento de controle e comunicação para regulação de frequencia - microgrid

8/13/2019 desenvolvimento de controle e comunicao para regulao de frequencia - microgrid

1/11

Published in IET Renewable Power Generation

Received on 24th June 2011

Revised on 28th January 2012

doi: 10.1049/iet-rpg.2011.0165

ISSN 1752-1416

Design of controller and communication for frequencyregulation of a smart microgridS. Mishra1 G. Mallesham1 A.N. Jha2

1Department of Electrical Engineering, Indian Institute of Technology, Delhi, India2Department of Electrical Electronics and Communications Engineering, ITM University, Gurgaon, Haryana, India

E-mail: [email protected]

Abstract:In this study an isolated microgrid comprising both controllable and uncontrollable sources, like solar, wind, dieselgenerator, fuel cell, aqua-electrolyser, hydrogen storage and battery is considered. To establish an efficient resource

management strategy, a central controller takes the decisions based on the status of the loads and sources. The status isobtained with the help of multi-agent concept (treating each load and source as an agent) through internet using UserDatagram Protocol/Internet Protocol (UDP/IP). The decisions are transmitted to the controllable sources to regulate their

power output for damping of frequency excursion following a disturbance. A control strategy is adopted to regulate the poweroutput from the battery only during transient, resulting in a floating battery scheme in steady state. This will reduce theampere hour rating of the battery and can improve the damping of frequency excursion following each load disturbance. Ina microgrid with generation rate constraint (GRC), tuning of controller parameters and frequency bias is a nonlinearoptimisation problem. Hence, this study attempts to tune the controller parameters using an evolutionary technique named

bacterial foraging optimisation (BFO). The tuned gains obtained utilising BFO method give satisfactory frequency excursionfollowing a disturbance in the microgrid.

Nomenclature

PG power generation (kW)

PL load power (kW)

Pw power from wind source (kW)

Ps power from solar photovoltaic panels (kW)

Pdg diesel generator power (kW)

Pfc fuel cell power (kW)

Pae aqua electrolyser (kW)

H2s hydrogen stored in tank

Pb battery power (kWh)

Qb state of the charge of the battery

DP power deviation (kW)

Df frequency deviation (Hz)

fsys system frequency (Hz)

Bi frequency bias (MW/Hz)

Ri P f droop in (MW/Hz)

KPi proportional gains for controllable source

KIi integral gains for controllable source

i stands for different controllable sources

Tsim simulation time (s)J performance index based on ITSE (Integral Time

Squared Error) criteria

1 Introduction

Todays world is very much concerned to reduce green housegas emission from the conventional thermal power plants ascutting down emissions from transport and heating sectormay not be realistic in the near future. To reduce pollutionfrom electrical power sources, the world is now marchingtowards usage of renewable energy sources (RESs) [1].These sources being small in capacity are mostly connectedat the distribution voltage level. This indirectly reducestransmission and distribution losses as the sources arearound the load. This distribution system having small scaleenergy sources is called as a microgrid or active distributionnetwork.

It operates generally in a grid connected mode. However,circumstances such as fault, voltage sag and large frequencyoscillations in the main grid may force the activedistribution network to be disconnected from the gridand operate as an isolated microgrid [2]. During thisisolation there will be change in power output from thecontrollable microsources, which are to be regulated

properly to have a stable operation with regard topower balance and frequency of operation. The solution isto have either a diesel generator/gas-based alternator to

bridge the gap between the power produced by the RESs

and loads.To operate the synchronous machine in synchronisation

with other sources, the frequency of operation should be

248 IET Renew. Power Gener., 2012, Vol. 6, Iss. 4, pp. 248258

& The Institution of Engineering and Technology 2012 doi: 10.1049/iet-rpg.2011.0165

www.ietdl.org


2/11

within a stipulated band. This can be achieved with the helpof frequency control strategy, implemented at the level ofenergy sources. Although performing frequency control, itis reasonable to assume that for a small magnitude of powermismatch, the cross-coupling between the frequency controland voltage regulation is negligible. With thisunderstanding, in this paper we are regulating the poweroutput of the controllable sources to maintain the microgrid

frequency. As the control instruction to the source cannotbe transferred to its output immediately because of theinherent time delay and ramp rate limit/generation rateconstraint (GRC), there will be oscillations in the frequency[3, 4]. These oscillations should be damped out using

proper controller parameters.As the sources are in parallel, to obtain a stable feedback

loop, there is a requirement for proper choice of powerfrequency (P f) droop characteristics (R) for the power-generating sources participating in frequency control [5, 6].Furthermore, to have steady-state load sharing according tothe rating of the sources, we need to follow the inverserelationship of R with respect to the rating of the source.Similarly, each controllable source can have a controlsignal, to regulate output power, proportional to frequencydeviation termed as frequency bias (B).

In modern power system, the secondary control forfrequency regulation is carried out with the help ofsupervisory control and data acquisition (SCADA) andremote terminal unit (RTU) being employed at the loaddispatch centre and substations, respectively. The scanningrate of SCADA to fetch the data from the RTUs is quiteslow (around 3 5 s). As SCADA houses the secondarycontroller, their influence over the dynamics of loadfrequency control is slow[7].

Moreover, the data obtained at the SCADA level may notbe of much use for improving the dynamic performance of

the system as it do not bear any time stamping. Toovercome this limitation, the technology is marchingtowards phasor measurement units (PMUs). Therefore incase of a microgrid if PMUs are placed in strategiclocation, there is always a possibility to use dedicated link/internet between different entities in a microgrid for thedata exchange. Owing to the higher sampling rate themicrogrid central controller (MGCC) analogous to SCADAcan handle both primary as well as secondary controllers.However, the decision depends on the reliability and

bandwidth of the communication channels. Hence, a smartMGCC can be developed for efficient resource utilisation,monitoring and control of power sources based on multi-agent system (MAS) concept with agents being connectedover internet through User Datagram Protocol/InternetProtocol (UDP/IP). The effect of data loss and wrongsequence, which may arise because of implementation ofUDP/IP, over the frequency excursion of the microgrid, isstudied in this paper.

In [3, 4] the modelling of microgrid in the context offrequency control without GRC and droop has already beentried with controller gains and frequency bias decidedthrough trial and error approach. The limitations of trial anderror approach were overcome to some extent in our

previous paper using Zieglers-Nichol method [8], wherewe have adopted a sequential approach to get the tunedcontroller parameters in the absence of GRC. However, this

method is suitable for lower order systems in the absence ofnonlinearity like GRC. Furthermore, the approach to applyZieglers-Nichol method for a multi controller system is notyet known.

For the first time bacterial foraging optimisation (BFO) isused for simultaneous optimisation of frequency bias andcontroller gains of a microgrid modelled considering GRC.As the battery cannot be used under steady state for powerexchange with the microgrid, a battery power controllersimilar to that proposed in[9] is implemented.

This paper is organised as follows. Section 2 illustrates themicrogrid modelling and its main components. Section 3

presents design of P f droop by the bode plot method.Section 4 deals with the application of BFO to tune thecontroller parameters. In Section 5, simulation results aredemonstrated for various cases. The conclusions are

presented in Section 6.

2 Modelling of microgrid

In this paper, we have considered a 100% self-sufficientisolated microgrid consisting of wind power source(300 kW), solar power source (300 kW), diesel generator(400 kW), fuel cell (200 kW), aqua-electrolyser (100 kW)and battery (30 kWh). The total generation from renewablesources and from the controllable sources is equal to600 kW. The battery is used for supplying power onlyduring transient period.

To implement the frequency control strategy and forefficient use of available resources, the concept of MASs as

proposed in [10, 11] is used. In this scheme each load andsource defined as an agent, is assigned with an IP address.MGCC acts as a server, fetches the status of the agents andgenerates appropriate control signals which will betransmitted to the respective sources using IP addresses.The two-way transmission is carried out using UDP/IP. Thecontrol variable (frequency), real power produced bydifferent sources and power consumed by loads are sensedthrough different transducers. An analogue-to-digital

converter (ADC) and digital-to-analogue converter setup isrequired for interfacing with the internet. Apart from thecontrol unit, MGCC also maintains the states of source aswell as load and acts as a monitoring system. Thesimplified block diagram of the smart microgrid, neglectingreal power loss, from the prospective of real powerexchange and consequently the frequency deviation isshown inFig. 1a.

2.1 Microgrid central controller

As shown inFig. 1athe status of all the agents is obtained atMGCC. On the basis of the information of power generation,storage and load, MGCC will take decision and give on/offsignals to the circuit breakers via UDP/IP. The MGCC

produces the signals based on the following rules:

R1. If the contribution of RESs (wind+ solar) is higher thanthe load (PL0) in steady state, the aqua-electrolyser willfunction whereas the fuel cell and diesel generator shouldnot contribute any power to the microgrid. This rule isframed based on the fact that in the microgrid there isexcess power from the renewable sources and should bestored in the form of hydrogen.R2. If the contribution of renewable energy (wind+ solar) islower than the load in steady state, the aqua-electrolysershould not function. The fuel cell and diesel generator

should start contributing power to the microgrid. When thehydrogen stored in the tank comes to a level such thatthe pressure is not sufficient for the fuel cell to worksatisfactorily the MGCC should give an alarm.

IET Renew. Power Gener., 2012, Vol. 6, Iss. 4, pp. 248 258 249

doi: 10.1049/iet-rpg.2011.0165 & The Institution of Engineering and Technology 2012

www.ietdl.org


3/11

2.2 Smart microgrid having communicationchannel

Treating the loads and sources present in the microgrid asagents, MAS is formulated. Each agent (in our case wind,solar, diesel generator, fuel cell, aqua-electrolyser, batteryand MGCC) is modelled as shown in Fig. 1a and theexchange of the data between these agents and MGCC isachieved by using UDP/IP.

In each agent the UDP send and UDP receive blocks areused to send and receive the data to and from the agents.UDP send block has one input and the parameters to bespecified in the block are IP address of the receiving agentand the port address through which the data will enter thereceiving agent. Similarly UDP receive block, has one inputand the parameters to be given are IP address of the

sending agent and the port address through which the dataare to be received by the receiving agent.

2.3 Rotating machine-based controllable source(diesel generator)

Diesel generator can follow the load demand by means of itsgovernor control and speed droop. The governor regulates thefuel input to an engine via a valve mechanism. The engineacts as a turbine and drives the synchronous generator. Thegovernor of the diesel generator can be modelled with afirst order transfer function [4], as depicted in (1).

Gdgs(s) =1

1+sTdg(1)

Similarly, the turbine of the diesel generator can be modelled

as presented in (2).

Gdt(s) = 11+sTdt(2)

Therefore the overall transfer function of a diesel generatorwill be

Gdgt(s) =1

1+sTdg

1

1+sTdt(3)

The control signal generated based on frequency and powerdeviation at MGCC along with the locally derived signalobtained through P f droop is sent as an instruction to

the governor of the diesel generator which ultimatelyregulates the turbine power output. This scheme is proposedinFig. 1b.

2.4 Voltage source converter-based controllablesource

Whenever a source is producing DC voltage, we need avoltage source converter (VSC) to convert the DC to an ACvoltage. In VSC we have a control over the magnitude and

phase angle of output AC voltage as a result it can regulatereal and reactive power injected to the microgrid. In thiscase, real power needs to be produced from the source

whereas reactive power can be handled independently bythe converter. A simple control loop based on an integralcontroller can be considered for the real power regulation.The transfer function between the reference power and

Fig. 1 Block diagram of the smart microgrid

a Basic block diagram of the smart microgrid ( represents the communication link)b Detailed block diagram of diesel generator in the smart microgrid



www.ietdl.org


4/11

actual power can be obtained as in (4)

G(s) =1

1+sT (4)

It is to be noted that the integral gain used for control of VSCof various sources can be different. Hence, for each VSC-

based sources we need to calculate T with due respect to

practical considerations and to be included in the model[3].Moreover, the time constant T should also include thedelay introduced by the source feeding to the VSC. AsVSC-based system does not have any rotating part, the Pfdroop need to be obtained electronically [5].

2.5 Aqua electrolyser

It is a device to store the excess power from the RESs in theform of hydrogen (H2). The decomposition of water intohydrogen and oxygen can be achieved by passing electriccurrent between the two electrodes separated by aqueouselectrolyte [12]and will be governed by (5) and (6)

H2O+ 2e H2 + 2OH

(5)

2OH 1

2O2 +H2O+ 2e

(6)

The overall chemical reaction that takes place in theelectrolyser based on (5) and (6) is depicted in (7)

H2O H2 +1

2O2 (7)

The hydrogen so produced can be stored in a tank and can beused by the fuel cell to meet the load demand.

From the Faradays law, the rate of hydrogen production byaqua-electrolyser can be given by (8)

dH2dt = he

I

2F (moles/s) (8)

where he is the number of participating electrons and inour case they are two, I is the current flow and F isthe Faraday constant. The Laplace transform of (8) is

presented in (9)

sH2(s) =2

2FI(s) (9)

Assuming that the DC voltage (V) given to the device doesnot change significantly during our control operation, wecan write

H2(s) =1

VF

1

sP(s) (10)

Hence, in time domain

H2ae(t) =1

VF

Pae(t) dt (11)

Furthermore, as aqua-electrolyser is a DC-voltage-baseddevice, there will be a VSC to interface it with themicrogrid. As a result, the transfer function of an aqua-electrolyser will be in the same format as that of (4) and

reproduced in (12)

Gae(s) =1

1+sTae(12)

2.6 Fuel cell

It is an environmental friendly static energy conversiondevice which converts chemical energy stored in thehydrogen to DC electrical energy [13]. When there is less

power generation from the renewable sources or in peakload periods, the fuel cells begin to produce the power bytaking hydrogen stored in the storage tank. As this is also aVSC-based system, the transfer function will be again sameas that of (4) with properly defining T and is given by (13)

Gfc(s) =1

1+sTfc(13)

Moreover, in (11) if the power delivered by the fuel cell is

used instead of Pae it will give the amount of H2consumed by it and will be expressed as in (14)

H2fc(t) =1

VF

Pfc(t) dt (14)

2.7 Hydrogen storage

From the above discussion based on aqua-electrolyser andfuel cell, we observe that one is a producer and other is aconsumer of hydrogen. As the rate of production andconsumption may not be same, there is a requirement toknow exactly how much hydrogen is stored. This is becauseof the fact that the fuel cell will stop working when the

pressure on the hydrogen input is less than a particular level[14]. The pressure of hydrogen in the storage tank is relatedto the number of hydrogen molecules available for usethrough ideal gas law. Moreover, as per the mastercontroller the aqua-electrolyser will only work when thereis excess renewable energy-based power available in thesystem. Under this circumstance to regulate the input powerto the aqua-electrolyser, control signal is generated and

passed through GRC and aqua-electrolyser block to give theinformation about the actual power taken by the device.This power being used in (11) will indicate how much H2has been produced during the time of interest. Similarly, the

consumption of H2 by the fuel cell can be evaluated by(14). Now subtracting (14) from (11) we can know howmuch H2 has either been taken or given to the tank. TheDC voltage used in (11) and (14) is assumed to be 400 V inour case.

2.8 Battery

Battery is a DC-voltage-based device used to suppress thefluctuations in power imbalance. It can be assumed to beequivalent to a low-pass filter. As it is utilised for transientmitigation, the power output from it must be zero understeady-state condition. Similarly, to handle the next

disturbance properly we must bring back its energy storedto its steady-state value. To address this issue, two controlloops have been proposed. Similar to the other VSC-basedsources discussed above, the transfer function of the battery



www.ietdl.org


5/11

can be given by (15)

Gb(s) =1

1+sTb(15)

2.9 Wind power-based uncontrollable source

In case of wind energy conversion system (WECS), wegenerally implement the maximum power point tracking(MPPT). As result of this scheme, the WECS loses its

power output controllability. As a result, in general, itcannot support for frequency regulation of the microgridunless otherwise some modification is made into its controlloops. Therefore in this paper we have treated the WECS asan uncontrollable source, not participating in frequencycontrol.

2.10 Photovoltaic-based uncontrollable source

Similar to the WECS, MPPT is also used in case of

photovoltaic (PV) systems. As MPPT is also used in caseof PV-based system, we do not have a control over the

power output. Hence, in this paper the solar system istreated as an uncontrollable source, not participating infrequency control of the microgrid.

2.11 Power and frequency deviation

In a power system consisting of synchronous generator, if thebalance between the generation and load demand is notmaintained, the frequency deviates depending on thedomination of generation or load. The power deviation isthe difference between the power generation PG and the

power demand PL. From the swing equation of asynchronous machine, the generator mathematical modelcan be written as

Df =fsys

2Hs[DPG DPe] (16)

where

PG = PW +Ps +Pdg +Pfc Pae+Pb (17)

Generally, the loads are of mixed type like frequencydependant and non-dependant. So speed load characteristicsof composite load is approximated by

DPe = DPL +DDf (18)

where the first term of (18) is the non-frequency dependentpart of the load and the second term corresponds to thefrequency sensitive part of the load. Combining (16) and(18), we obtain (19)

DPG

DPL =

2H

fsys s+D D

f (19)

Therefore the transfer function for system frequency variation

to per unit power deviation is given by (20)

Gsys(s) =Df

DPG DPL=

1

D + (2H/fsys)s=

Kps

1+sTps(20)

where Kps andTps are 1/D and 2H/Dfsys, respectively.It is to be noted here that (16) is valid only when there is

a synchronous machine in the microgrid. Therefore theresearchers should be careful in using (20) for simulatingthe microgrid.

3 Designing of P f droop for differentcontrollable sources in the microgrid

In the considered microgrid, there are three sources namelydiesel generator, fuel cell and aqua electrolyser with P fdroop. The P f droop characteristics are required in thesystem when multiple power sources are connected in

parallel like in microgrid [5, 6]. The individual powergenerators are responsible for maintaining the frequency.Conventionally, it is implemented as in (21)

m2m1=

P1ratedP2rated

(21)

where m1, m2 are Pf droop coefficients andP1rated, P2ratedare power ratings of generating sources in the microgrid. Asthe droops are related to the rating of the sources, we needto calculate at least one at the outset and then examine forthe others. In this paper, we start with the diesel generatoras it has second order transfer function which may lead toinstability. The closed-loop signal flow graph of the diesel

generator along with the power system is shown in Fig. 2a.

Fig. 2 Signal flow graph of

a Diesel generatorb Fuel cellc Aqua-electrolyser along with power system



www.ietdl.org


6/11

From Fig. 2a, open-loop transfer function of the diesel-generator system can be as in (22)

Gdg =

Kps

(1+sTdt)(1 +sTdg)(1+sTps) (22)

Similarly, the characteristic equation for the closed-loop

system ofFig. 3a will be

1+Gdg 1

Rdg= 1+

Kps

Rdg(1 +sTdg)(1+sTdt)(1 +sTps) (23)

The bode plot corresponding to Gdg andGdg_dr Gdg/Rdg of(23) is depicted in Fig. 3a.

Fig. 3a shows that when the droop is unity, the dieselgenerator along with the power system is unstable as boththe phase and gain margins are negative. However, whenthe droop is 20.4918 the stability margin is well into the

positive zone presenting a stable system. Further using (22),the P f droop values of fuel cell (200 kW) and aqua-electrolyser (100 kW) are calculated and found to be40.9836 and 81.9672, respectively. The signal flow graphsand bode plots corresponding to fuel cell and aqua-electrolyser are shown inFigs. 2and3, respectively.

4 Application of bacterial foragingoptimisation

For the first time, the BFO technique is used in microgrid foroptimisation of multiple parameters. BFO is proposed byPassino [15] and has been applied to many problems. InBFO technique, we assign each bacterium with a set ofvariables to be optimised. In our case they are KPi, KIi, and

Bi (i corresponds to different controllable sources in themicrogrid) and set of KP, KI for stabilising the battery

power and its state of charge. Each bacterium is allowed totake all possible values and the objective function which isIntegral Time Squared Error defined by (24) is minimised.

J=

Tsim

0

{(Df)2 + (DPb)2+ (DQb)

2}tdt (24)

The coefficients so optimised for a particular disturbance maynot give the satisfactory performance for some otherdisturbance. This is particularly significant in our case as themaster controller is making the sources on and off dependingon the situation as per the rules defined in Section 2.1.

Therefore the objective function (24) is evaluated for twodifferent disturbances J1 (5% increase in load) and J2 (3%increase in RES), so that the controllers corresponding toall sources will be tuned. The two objective functions soobtained are added to obtain the cumulative cost functionwhich needs to be minimised [16]. Hence, the total costfunction becomes (25)

J= J1 +J2 (25)

The bacterium for which the cost function value is minimumin the last BFO iteration estimates the parameters to be used.In this paper, the BFO algorithm is implemented withfollowing initialisation:

1. Number of parameters (p 16) to be optimised-6 PIcontrollers (i.e. total 12 gains), 4 frequency bias.2. Number of bacteria (S 8) to be used for searching thetotal region.3. Swimming length (Ns 3) after which tumbling of

bacteria will be undertaken in a chemotactic loop.

4. The number of iterations to be undertaken in a chemotacticloop Nc 5(Nc . Ns).5. Nre 4, the maximum number of reproduction to beundertaken by bacteria.

Fig. 3 Bode plots of

a Diesel generatorb Fuel cellc Aqua-electrolyser along with power system and Pf droop



www.ietdl.org


7/11

6. Ned 8, the maximum number of elimination anddispersal events to be imposed over bacteria.7. The probability with which the elimination and dispersalwill continue (Ped 0.02).8. The location of each bacterium p which is specified byvalues obtained from the trial and error method.9. The value ofC(i), which is assumed to be constant in ourcase for all bacteria to simplify the design strategy.

10. The magnitude of secretion of attractant by a celldattract 0.01.11. The chemical cohesion signal diffuses (smaller makes itdiffuse more vattract 0.04.12. The repellant (tendency to avoid nearby cell)hrepelent 0.01.13. vrepelent 10 is the width of the repellant.

The details about above parameters are available in [16].The optimised parameters are given in Appendix (Table 1).On the basis of above discussions, the detailed blockdiagram of the smart microgrid is shown in Fig. 4a withagents, MGCC and communication link and itsimplementation in MATLAB/SIMULINK is shown inFig. 4b.

5 Simulation results and analysis

To develop the smart microgrid model in MATLAB/SIMULINK as shown inFig. 4b, the models of the sourcesdiscussed in Section 2 and nominal parameters given inAppendix taken from [3] and [4] are considered. They areintegrated with frequency bias, Pf droop, and controllerswith GRC as proposed. For the battery, there is no P f

droop because battery action is involved only in improvingtransient performance of the system. Different case studiesare considered in the simulations analysis for the durationof 1000 s to show the necessity of battery, modelling ofGRC, secondary control, effect of sampling time, data lossand wrong sequence in UDP/IP.

5.1 Case 1: study of microgrid with GRC, batteryand secondary control

The variation of frequency of the microgrid for a suddenincrease in load by 5% considering with and without GRC/

battery/secondary control is discussed in this section.

5.1.1 Microgrid with/without GRC:FromFig. 5a, in theabsence of GRC, frequency oscillations are minimum,

Fig. 4 Block diagram of microgrid

a Detailed block diagram of microgrid ( line represents the communication link)b Block diagrams of MGCC and agents in smart microgrid using MATLAB/SIMULINK



www.ietdl.org


8/11

whereas there is a considerable frequency oscillation underGRC. For the representation of actual behaviour of thesystem, GRC must be included. Hence, in this paper weaddress the limitations of[3, 4], for representing the sources

by including GRC in the model.

5.1.2 Microgrid with/without secondary control:To see the effect of both primary and secondarycontrollers over the frequency control of the microgrid, thedisturbance is created with and without secondarycontrollers. In Fig. 5a frequency response of the systemfollowing the disturbance is shown. When the system isonly under the primary controllers, the frequencystabilises very fast (7580 s) with a steady-state error. Toreduce this steady-state error, the secondary controllers willact and will bring the frequency deviation to zero in near300 s.

5.1.3 Microgrid with/without battery: To see thetransient handling capacity of the battery, we haveconsidered the scenarios of with and without a battery in

the microgrid. The power response of controllable sourcesand microgrid frequency deviation are shown in Figs. 5ac.From the simulation results, the steady-state power

produced by the diesel generator and the fuel cell are same

for with and without battery in the system. This implies thatthe battery only works during the transient period.

FromFig. 5a, the peak frequency deviation reduces from1.5 to 0.815 Hz at the cost of injecting only 9.4641 kW of

power from the battery. As the power injection isinstantaneous, it can be supplied even by a less kilowatt-hour battery. However, as the magnitude of disturbance thatmay come in the system is unknown; we have taken a 30-kWh battery for our purpose. Moreover, the battery needsto operate both in charging and discharging modes. Toachieve this functionality, initial charge of the battery isconsidered to be 50%, that is 15 kWh in this paper. It isclear that even a small capacity of battery can improve thefrequency response of a microgrid. Furthermore, fromFigs. 5b and c we can see that both the battery power Pband state of the charge Qb come to their references,respectively. This implies that battery can handle thetransient of subsequent disturbance.

5.2 Case 2: sudden increase in solar power

The dynamic responses of the microgrid for a sudden increasein solar power by 3% are shown in Figs. 6a and b. On the

basis of the master controller decision for this case, thediesel generator and fuel cell will not supply any power to

Fig. 4 Continued



www.ietdl.org


9/11

the microgrid. The transients are handled by battery and aqua-electrolyser. As a result the total increase in solar power isconsumed by aqua-electrolyser to obtain hydrogen and sent

for storage. This fact is clear by looking at Fig. 6a.Furthermore, from Fig. 6b, the maximum frequencydeviation is observed to be 0.1 Hz which is quite acceptablein any realistic microgrid.

5.3 Case 3: effect of sampling time over thecontroller, uncertainty in data transmission usingUDP/IP

In a smart microgrid the digital data are obtained throughADC using sample and hold circuit. Therefore it isimperative to see the effect of sampling time over the

performance of the controller. The frequency response for asudden increase in load by 5% for different sampling timeof the UDP/IP block used inFig. 4b is depicted in Fig. 7a.It shows that if the sampling time is less than the smallesttime constant of the sources used in the microgrid (in this

paper battery has the smallest time constant of 0.1 s), the

responses in both continuous and discrete modes areidentical. So in this paper, the sampling time of 0.1 s orsampling rate of 10 Hz is considered.

The effect of packet loss and the sequence missingcondition which may occure in UDP/IP has been simulatedin the signal that is coming from the MGCC to fuel cell.The control signal transmitted from the MGCC andreceived at fuel cell has the following sequence of eventsfor the case of increase in load by 5%:

1. Data transmitted at 15.2 s is lost and donot reach thereceiver.2. At 15.4 s the data of 15.2 s reached the receiver creating a

wrong sequence.

The transmitted and received signals are depicted inFig. 7b. For this case, we have assumed that when there is

Fig. 5 Microgrid with GRC, battery and secondary control

a Comparisons of frequency deviations of microgrid with/without battery,GRC and secondary controlb Battery power outputc Response of state of the charge of the battery

Fig. 6 Responses of the microgrid for a sudden increase in solar

power

a Hydrogen produced by the aqua-electrolyser and stored in hydrogenstorage in the tank (H2s)b Frequency deviation



www.ietdl.org


10/11

a data loss the previous data will continue to act as thereference. Therefore in Fig. 7b the refrence power to thefuel cell remains same during 15.1 15.3 s. As the power

remains same, the influence of data loss over the frequencyexcursion is almost negligible. Moreover, even when thereis a problem in sequence, it will not influence the frequncyexcursion as long as the time delay is small. Besides, when

we use UDP/IP in real-time application there will beanother layer to take care of the sequence. In this type ofscheme, there will be a time stamping and if the data arenot reaching the receiver within a stipulated time period, thereceiver will not receive it at all. Looking to frequencyexcursion presented in Fig. 7c, we can conclude that thereis no change in frequency deviation due to either data lossor wrong sequencing in the received signal.

6 Conclusions

This paper establishes the necessity of considering P f droopand modelling of GRC for different sources used forfrequency control in a microgrid. As the battery cannotsupply or absorb power under steady-state condition, twocontrol loops based on battery power and state of thecharge in the battery has been considered in this paper. Asin a microgrid there can be aqua-electrolyser and fuel cell(one producing and other consuming hydrogen), theformula for calculating net hydrogen available in thestorage tank is derived in this paper.

For getting a satisfactory frequency response following aperturbation in the microgrid, the controller parameters,frequency bias and droop characteristics with GRC are to

be tuned properly. As it is difficult to tune many numberof parameters through conventional approaches, anevolutionary algorithm termed as BFO is used. From thesimulation results, it is observed that the frequencyresponses of the tuned microgrid to different disturbancesare satisfactory.

Further smart microgrid based on MAS and UDP/IPwith uncertainty in data transmission is simulated and aguideline for choosing the sampling time has beenestablished.

7 Acknowledgment

This research work is supported by DST Govt. of India underthe project Voltage and Frequency control of microgridhaving file no: SR/S3/EECE/0040/2010.

8 References

1 Bull, S.R.: Renewable energy today and tomorrow, Proc. IEEE, 89,(8), pp. 12161221

2 Valenciaga, F., Puleston, P.F.: Supervisor control for a stand-alonehybrid generation system using wind and photovoltaic energy, IEEETrans. Energy Convers., 2005, 20, (2), pp. 398405

3 Lee, D., Wang, L.: Small signal stability analysis of an autonomous

hybrid renewable energy power generation/energy storage system timedomain simulations, IEEE Trans. Energy Convers., 2008, 23, (1),pp. 311 320

4 Senjyo, T., Nakaji, T., Uezato, K., Funabashi, T.: A hybrid powersystem using alternative energy facilities in isolated island, IEEETrans. Energy Convers., 2005, 20, (2), pp. 406414

5 Majumder, R., Chaudhuri, B., Ghosh, A., Majumder, R., Ledwich, G.,Zare, F.: Improvement of stability and load sharing in an autonomousmicrogrid using supplementary droop control loop, IEEE Trans.Power Syst., 2010, 25, (2), pp. 796808

6 Diaz, G., Gonzalez-Moran, C., Gomez-Aleixandre, J., Diez, A.:Scheduling of droop coefficients for frequency and voltage regulationin isolated microgrids, IEEE Trans. Power Syst., 2010, 25, (1),pp. 489 496

7 Chowdhury, S.P., Crossley, P., Chowdhury, S., Clarke, E.: Microgridsand active distribution networks (Institution of Engineering and

Technology, London, 2009)8 Mallesham, G., Mishra, S., Jha, A.N.: Maiden application of Ziegler-Nichols method to AGC of distributed generation system. Proc. Int.Conf. IEEE PES Power Systems Conf. and Exposition, March 2009,pp. 15 18

Fig. 7 Effect of sampling time over the controller, uncertainty in

data transmission using UDP/IP

a Comparison of frequency deviations of microgrid for different samplingtimesb Control signal with data loss received by fuel cell from MGCCc Fuel cell output and frequency deviation of microgrid with /without dataloss and wrong sequencing



www.ietdl.org


11/11

9 Kawabe, K., Yokoyama, A.: Effective utilization of large-capacitybattery systems for transient stability improvement in multi-machinepower system. Proc. Int. Conf. IEEE Trondheim Power Tech, June 2011

10 Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems ina distributed smart grid: design and implementation. IEEE PES PowerSystems Conf. and Exposition, March 2009, pp. 1518

11 Dimeas, A.L., Hatziagyriou, N.D.: Operation of a multi-agent systemfor microgrid control, IEEE Trans. Power Syst., 2005, 20, (3),pp. 1447 1455

12 Ulleberg, .: Modeling of advanced alkaline electrolyzers: a system

simulation approach, Int. J. Hydrog. Energy, 2003, 28 , pp. 213313 Li, Y.H., Choi, S.S., Rajakaruna, S.: An analysis of the control and

operation of a solid oxide fuel-cell power plant in an isolated system,IEEE Trans. Energy Convers., 2005, 20, (2), pp. 381387

14 Agbossou, K., Kolhe, M., Hamelin, J., Bose, T.K.: Performance of astand-alone renewable energy system based on energy storage ashydrogen, IEEE Trans. Energy Convers., 2004, 19, (3), pp. 633640

15 Passino, K.M.: Biomimicry of bacterial foraging for distributedoptimization and control, IEEE Control Syst. Mag., 2002, 22, (3),pp. 52 67

16 Nanda, J., Mishra, S., Saikia, L.C.: Maiden application of bacterialforaging-based optimization technique in multiarea automaticgeneration control,IEEE Trans. Power Syst., 2009,24, (2),pp. 602609

9 Appendix

9.1 Nominal parameters of the microgridinvestigated

fsys 50 Hz, Pbase 1 MVA, D 0.012 MW/Hz, H 5 s,Tdg 2 s, Tfc 4 s, Tb 0.1 s, Tae 0.2 s, Tdt 20 s,GRCdg 3%, GRCae 10%, GRCfc 10%, GRCb 30%.

Table 1 Microgrid parameters after the optimisation/design

Parameters Automatic generation control Battery state of

the charge controller

Battery power

controller

Diesel generator Fuel cell Aqua electrolyser Battery

KP 0.7506 0.2757 75 0.0351 21.9338 0.0101KI 0.0025 0.0022 0.3578 0.0482 14.9394 0.4889

B(MW/Hz) 0.1051 0.1609 1.3524 0.1041

R(%) 16.3934 16.3934 16.3934



www.ietdl.org

Documents

desenvolvimento de controle e comunicação para regulação de frequencia - microgrid