Coordinated Voltage Control in Distribution Network with Renewable Energy based Distributed Generation

doi:10.4236/eng.2013.51B038

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Engineering, 2013, 5, 208-214

doi:10.4236/eng.2013.51b038 Published Online January 2013 (http://www.SciRP.org/journal/eng)

Coordinated Voltage Control in Distribution Network with

Renewable Energy Ba sed Dist rib ut ed Gen eration

Mohd Khairun Nizam Mohd Sarmin1, Worawat Nakawiro1, Mohd Zamri Che Wanik2,

Mohd FadzilMohd Siam1, ZahrulFaiziHussien1, Ahmad Asrul Ibrahim3, Ahmad Kamil Mat Hussin2

1Smart Grid Sectio n, TNB Research Malaysia, M alaysia

2Green Technology Section,TNB Research Malaysi a, Malaysia

3Power system research group, Uni vers itiKeb angsaan Malaysia, Mal aysia

Email: [email protected] m.my

Received 2013

ABSTRACT

This paper presentsa voltage control strategy for power distribution systems with interconnected renewable energy

based distributed generators (DGs). The control strategy coordinating conventional voltage control devices and reactive

po wer from DG.A mixed-integer nonlinear programming problem was formulated and solved by particle swarm opti-

mization (PSO). The code is written using DigSILENT programming language (DPL) and implemented inside DigSI-

LENT po wer factory simulatio n software. All syste m constraints and operating limits are considered. The optimal pow-

er flow based approach can incorporate various uncertainties such as intermittent po wer characteristics and varying load

demand. The proposed method is tested using real distribution network to demonstrate its effectiveness. T he merits of

the proposed method over the classical local-based control are presented in the simulation results. It is demonstrated

that the proposed method is capable of keeping the system voltage within operating limit. Power losses is at the same

time is minimized in comparison to the losses using conventional method.

Keywords: Photovoltaic; D istributed G e ner a tion; Particle Swarm Optimisation; Di gSI LE NT; Coordinated Control

1. Introduction

Renewable resources has gained significant attention in

recent years due to the cost increment and adverse en-

vironmental impacts of conventional fossil fuels [1].

Currently, Photovoltaic (PV)energy systems are considered

as opti mum sol ution to the ele ctricity supply i n most r ural

zones in developing countries [2,3].

PV application is gaining a lot of attention in the de-

veloping countr y like Mala ysia. Feed in Tariff introduced

in Malaysia in 2011 [5] is a sign that the government is

serious in promoting PV as a new energy source in sup-

porting Malaysian sustainable growth. Typicallya re-

newable energy resource such as PV system generatese-

lectricity as distributed generator (DG) is connected to

distribution networks. DG application is highlighted of-

fers various benefits to the distribution network where it

is connected to but harve sting the benefits is not without

chall enges [ 6].

Voltage contro l is one of well-documented operational

challenges [7]. Currently the voltage control strategies

are also developed based on the assumption of unidirec-

tional power flow. However in some extreme conditions

due to the intermittent and non-dispatchable characteris-

tics the excess DG power could flow in a reverse direc-

tion to the transformer high voltage side. Therefore a

new scheme for voltage control is definitely needed in

modern power distribution systems which comprising

DG. Some of these control schemes can be broadly cate-

gorized as tabulated in Table 1.

From practical viewpoints, the offline planning ap-

proach should be deployed in complimentary with an

advanced online contro ller. This coord ination will enable

the system to achieve economic operation and guarantee

security when subject to unexpected disturbances.

In this p a pe r , a n o ffline ( d a y-ahead) planning approach

based on particle swarm optimization (PSO) is proposed.

The approach determines optimal settings of all control

devices including on-load tap changers, switched shunt

capacitors, commonly found in most power distribution

network. System state quantities are considered and op-

erational limits is incorporated. PSO code is written in

DigSILENT programming language (DPL) and the pro-

posed approach is implemented in DigSILENT Power

Factory [15].

2. Voltage and Reactive Power Control

The concept of power loses can be explained by referring

to Figure 1. In a simple radial networ k shown in Figure

M. K. N. M. SARMIN ET AL.

209

1 the voltage drop across the feeder can be approximated

by [16-17]:

LL DG

RPX QQ

⋅+⋅ −

∆≈

()

(1)

where R and X are the line resistance and reactance, re-

spectively, and P and Q are the active and reactive power

generated from DG. It can be seen that any fluctuation in

reactive power will impact the voltage fluctuatio n. If the

constant power factor control is used, the ratio P/Q is

maintained. This tends to increase reactive power of DG

and there by aggravating the v olta ge rise.

When reactive sources available properly compensate

the reactive power demand, the feeder current will de-

crease according to:

LL DG

P QQ

+−

=()

(2)

Therefore the feeder losses PLoss will also decrease be-

cause losses are directly related to current as follow:

Loss

P IR= (3)

3. Coordinated Voltage Control

Figure 2 shows three alternatives for coordinated voltage

control (CVC) in a distribution network with DG. The

baseline of this scheme is to control the on-load tap

changer (OLTC) position of transformer and substation

capacitors CS via SCADA as depicted in the red arrow.

However if the communication channel is available it

Table 1. Comparison of distribution network voltage con-

trol scheme.

Offline Planning

Online Control

Objective

Determine optimal

control set points to

achi eve min/max objec-

ti v e( s) whereby main-

taining all constr aints

(Optimal Power Flow)

Find the recourse of

controllers to achieve

th e t arget objective (i.e.

to elimina te voltage

violati on, line loa ding,

etc).

Time

scale Day-ahead Online and closed loop

Inputs/

Data

require-

ments

Offline : Complete

network info, load

forecast, predicted

power generation

(i.e.solar)

Offline : Histor ical data ,

operat ion rules and

expertise and/or simula-

tion studies.

Online: S el ected

measurements

Example

Opt imiza tion algorithm

• Mat hematical pro-

grammi ng (i.e.[ 8])

• Heuristic me-

th o ds(i.e.[ 9-10])

Intelligent system

• Multi agent ( i.e.[11])

• Ar tif icia l ne ur a l net-

work (i.e.[12])

• Fuzzy logic

(i.e.[13-14])

R+jX

+jX

+jQ

Grid U

Figure 1 . A simple network showi ng vol tage drop.

Figure 2. Coordinated voltage control with DG sources.

may be possible to control the capacitors and energy sto-

rage devices installed at feeders (see the blue arrows).

Moreover if DG can participate in ancillary service s, DG

reactive power output can also be controlled.

4. Optimization Formulation

The optimal coordination of all control devices can be

determined by solving the formulated optimal power

flow problem. The control variables consist of OLTC

position, status of substation capacitorsCs and feeder

capacitors CF and DG reactive po wer out put s (co ntinuous

variables). The objective can be set to minimize the

power loss Ploss as shown:

, ,,

( ,,,);

losst ttDtDGt

P ftT= ∀∈Min xud d

(4)

where all the subscripts trepresents the planning time

interval t in the set of all time intervals in a day T. The

vector xt represents the state variables as listed in (6)-(9)

at time t. The vector ut represents the control variables at

time tgiven by:

,,, ,

tt tttiti



=

uTap Cf Cs Qgθpv

(5)

where the subscripts i represent the index of fast (15

minutes; i = 1,...,4) response controllers. At the consid-

ered time interval t, Taptis the vecto r of all OLTC trans-

formers; Cftis the vector of all feeder capacitors; andCstis

the vector of all feeder capacitors. These discrete con-

tro llers ha ve a slo w resp onse t ime (i. e. 1 ho ur li ke in thi s

study). On the other hand, the continuous variables have

by nature fast response. Therefore, these controls are

M. K. N. M. SARMIN ET AL.

210

discretized with a smaller time step i (15 minutes; i =

1,...,4 for any planning hour t). At time t, the vector

Qgt,icontains reactive power output for all synchronous

DGs and the vector θpvt,i contains the power factor set

point for all solar PV co nverter.

The network should be operated within a narrow band

of voltage variation to ensure safety of power system

equipments and supply quality

min , maxit

U UUit≤≤∀∈ ∀∈ ,

(6)

whereUi,tis t he volta ge o f b usi at time t; Uminand Umax are

mini mum and maxi mum allo wable voltages, respectively

and NB is the set of controlled buses.

The current flo w in all lines must be maintained b elo w

the rating limit

,, ,,Ljt Lj

II jt≤∀∈ ∀∈

rat

(7)

where IL,j,tis the current flow on line j at time t; IL,j,rat is

the the rma l capacity of line j and NL is the set of all lines.

The apparent power transfer on the substation trans-

former is limited to prevent any overloading

SS t≤ ∀∈

TX TX,rat

, T

(8)

where STX,t is the apparent power flow on substation

transformer at time t and STX,rat is the capacity rating of

the substation transformer.

When DGs are centrally dispatched, the operating re-

gion is restricted by the power factor operating limits

described as:

min,maxmin, maxkt kt

PFPF PF

θ θθ

≤≤⇒ ≤≤

∀ ∈∀∈

(9)

wherePFmin and PFmax are minimum and maximum op-

erating power factor of DGs (here assumed common for

all DGs for simplicity), respectively. To consider induc-

tive and capacitive operating range of DGs, the power

factor angle

is preferred because it is positive for lag-

ging (inductive) and leading (capacitive) power factors.

The angle

is constrained in the range between the min-

imum and operating power factor angles [

min,

max ]. The

NDGrepresents the set of all DGs.

As mentioned earlier that the proposed control meth-

odology will provide the set of opti mal control set -points

for the day-ahead operation. Therefore the necessary

input data is forecast of the load demand and predicted

generation pattern to be dispatched for the next 24 hours.

The developed optimization algorithm will iteratively

search for the best control variables which resulting in

the minimum total power losses while maintaining all

operational bounds and system security constraints.

The heuristic optimization approach is preferred be-

cause of its ability to handle complex problems without

any need for an explicit mathematical model. For every

iteration, the power system analysis (DigSILENT) is

called and load flow simulation is performed to deter-

mine the power system states corresponding to the con-

trol variable s updated by the optimization algorith m. T he

outputs of this method are the optimal position of trans-

former tap changers and the optimal status of capacitors

for the next da y of ope ration. The exp lanation given ear-

lier can be depicted as in Figure 3.

5. PSO Implementation

PSO algorith m flow char t is d epicted in Figure 4. A par-

ticle iflies in the search space between two successive

iterations acco rding to:

( )()( )

i ii

t tt+=+ +xxv

(10)

The or iginal eq uatio n for velocity update is given by:

()( )

( )

1() ()

() ()

ttctt

c tt

+= +−

+−

ii1pi

2g i

vvrx x

rx x (11)

The randomness in the search procedure is introduced

by two independent uniform random sequences, r1 and r2

in the range (0, 1). The weighting coefficients c1 and c2

are the acceleration coefficients which control the influ-

ence of cognitive and social parameters on the particle’s

velocity.

Figure 3. Co nceptual diagram of the developed OPF .

Figure4 . PSO algorithm flow chart.

M. K. N. M. SARMIN ET AL.

211

In thi s wo rk, t he sta nd ard PSO versi o n 200 6 was used .

The parameters w and c remain unchanged from [18].

The global best position in the swarm xg is considered as

xg∈{xp1, xp2 ,…….., xps} suc h that

( )()

fmin f(),f(),........,f()=

gp pps

xxx x

(12)

Constraint handli ng is the most important pa rt in sol v-

ing a constrained optimization problem. Fitness functions

are used to assign a qualitative measure to individuals in

the population. To properly handle constraints in PSO,

the fitness function should be carefully designed such

that it can help guide the swarm process to the promising

and feasible search space.

The self-learning penalty function proposed in [19] is

applied. The elegant feature of this technique is that it is

parameter-less and capable of adjusting the penalized

fitness function at different stages of the search process.

6. Distribution Network

This section presents the network model used for simula-

tions in this research work. A part of real distribution

network in middle region of peninsular Malaysia was

chosen as the test network and the schematic diagram is

shown in Figure 5. This is a distribution network sup-

plied by two substations. The system basically operates

as a radial network with options for feedback from the

other 33/11 kV substation in case of breakdown.

Since our assumption is to have high degree of DG

penetration, therefore 5x1 MVA synchronous DGs and

5x1 MW solar PV are assumed to be installed at various

loca tio ns thro ug hout t he net w or k. All DG s and sol ar P Vs

are connected at 11 kV busbars. The 11 kV busbar is

supplied by two 33/11 kV transformers namely Tr(L1)

and Tr (L2).

In the simulation, all the loads are assumed to be of

constant power type with the power factor of 0.9 induc-

tive. Average power factor was stated to be 0.9. A future-

istic load pattern with the peak of 12.8 MW (presently

about 5 MW) is assumed as shown in Figure 6(a). The

power output for each solar PV is illustrated in Fig-

ure6(b) . This data is obtained by fitting the real-meas-

urements from a test unit of 3 kW solar PV into the nor-

mal (Gaussian) distribution. This unit is installed on the

rooftop one of the building at UniversitiTenagaNasional

main ca mpus.

This assumption may not be accurate since the statis-

tical characteristics of solar energy is quite complicated

and varied due to several factors. Finally, the power gen-

eration dispatch of synchronous DG is included in the

study scope. Therefore, two generation levels are as-

sumed fo r all syn chro no us D Gs as s hown in Figure 6( c).

DigSILENT Power Factory [15] is used as the power

system analysis software and the PSO code is written in

DP L.

Figure 5. Schematic diagram of the test net work.

M. K. N. M. SARMIN ET AL.

212

7. Simulation Results

7.1. Losses and Cost Comparison

This section demonstrates the effectiveness of the devel-

oped OPF method based on PSO (named ‘PSO’ hereafter)

in co mparison with the existing control method based on

local information (named ‘Local’ hereafter). Two cases

are considered namely ‘noDG’ (no DG is connected) and

‘withDG’ (all DGs a nd PVs are co nnected). For the local

control method, solar PV and synchronous DG are as-

sumed to operate in the capacitive mode with 0.9 power

factor.

Figure 7 displays the total power losses for the daily

operation according to the generation and load pattern

shown in Figure 6. It is obviously shown that based on

the local approach synchronous DGs do not play a role in

reducing the losses during the night times. This is be-

cause they are not located close to the load center. On the

other hand, the solar PV with a fixed power factor con-

trol can help reduce losses significantly during day time.

This is due to their stra tegic locations near to load c e nters

in the feeders 5 and 6.

With the PSO approach, it is very clear that the power

losses are minimized throughout the day. This is due to

the optimal settings of all control variables. The total

energy losses (in terms of kWh) of the four cases in Fig-

ure 7 are calculated as shown in Table 2 demonstrates

the sa ving i n cost due to ener gy losse s. T he energy p rice

is assumed to be RM 0.3/kWh. It can be shown that the

savings due to introduction of PSO are 3.06% in case of

no DG and 2.94% in case with DG.

Figure 6. Input data (a) total load demand (b) solar power

output of each PV (c) power dispatch of each synchronous

DG.

7.2. Optimal operation

The optimal setting of all control variables at each time

interval is the output of the developed methodology. The

optimal tap position for the transformer Tr(L1) is shown

in Figure 8. Notice that two changes are required in the

local method whereby the total number of tap changes is

11 for the PSO method. This number of change is ac-

ceptable in real operational practices for a transformer

with similar r a ting [13]).

7.3. Voltage Profile

Voltage magnitudes at the two substations are compared

based on the local and PSO methods as shown in Figure

9. It shows that the substation voltage s based o n the local

control method a re maintained at a higher level to ensure

that voltage at the last bus of all feed ers is not belo w the

statutory limit (-5%). Ho wever, the s ubstation volta ge in

the PSO method is on lower average. This is beca use the

PSO optimizes all available reactive sources in the net-

work and help support voltage at different buses. More-

over the reactive current will be reduced due to the op-

timal reactive and voltage support. Therefore it reduces

the current magnitudes in most feeders and thereby re-

duces the total power losses.

Figure 7. Power losses for a daily operation based on dif-

ferent control strategies.

Table 2. Cost comparison.

Method Energy

losses

(kWh)

Energy

Cost

(RM/kWh)

Cost of

energy

losses

(RM/d ay)

Cost

saving

(%)

local_noD G 21047.10

0.3

6314.13 3.06

pso_noDG 20402.21 6120.66

local_withDG 20428.17 6128.45 2.94

pso_withDG 19826.97 5948.09

M. K. N. M. SARMIN ET AL.

213

Voltage profiles at the last bus of the feeder 5 are

shown in Figure 10. It is clear that the voltage level of

the PSO method is lower than the local method. This

figure also shows possibility of experiencing an under

voltage problem when the solar power output highly

fluctuates.

8. Conclusion

This paper focuses on the offline (day-ahead) planning

approach. An optimal power flow (OPF) is formulated to

Figure 8. optimal control set points f or (a) OLTC transfor-

mer (b) substatio n capacitor(c) solar PV inverter.

Figure 9. S ubstation voltage s.

Figure 10. Voltage at one of the feeders.

optimize the desired objectives such by searching for the

optimal schedule of all control devices. Power system

state quantities are considered and operational limits is

incorporated. The optimal power flow based approach

can incorporate various uncertainties suc h as intermitte nt

power characteristics and varying load demand. It is

demonstrated that the proposed method is capable of

minimizing power losses and voltage deviation in com-

parison to the conventional method. However the main

obstacle for this approach is that it relies on sensors and

communication infrastructures which may not be readily

available in many power util ities.

9. Acknowledgement

This project is funded by TNB Research Malaysia

through research funding TNBR/SF 52/2012 and partly

supported by UniversitiKebangsaanMalaysia via re-

sear ch gra nt G GP M-2011-071. Dr.-Ing. WorawatNakawiro

was a principal researcher at TNB Research Malaysia

between Sept 2011 and Aug.2012.

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