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Dynamic analysis of renewable energy systems and their impact on

smart grid

Suhas Shirbavikar S. Ashok

M. M Babu Narayanan

Abstract – This paper presents the modeling and performance analysis of integrated renewable system connected with the

smart power distribution grid. The renewable energy sources considered in the analysis are wind and biomass generation.

The smart grid has been simulated using PSCAD/EMTDC software considering various dynamic conditions of renewable

energy sources. Results show that when Renewable energy sources are connected to the Distribution system, the power

flow gets altered and this would necessitate a change in the protection system settings. Also, sudden connection or

disconnection of renewable energy sources due to faults etc. may result in unacceptable transients in voltages in the

distribution system which needs to be mitigated. The study reported here makes an important contribution to the concept of smart grid in Indian power distribution system.

Keywords: Electromagnetic transients, Wind, Biomass, Smart grid

I. Introduction

Distributed power generation system is emerging as a complementary infrastructure to the traditional

central power plants. This infrastructure is constructed on

the basis of decentralized generation of electricity close to

consumption sites using Distributed Generation (DG)

sources [1].The increase in DG penetration and the

presence of multiple DG units in electrical proximity to

one another have brought about the concept of the Smart

grid.

A smart grid is a digital upgrade of power

system that is capable of assessing its health in real-time,

predicting its behavior, adaptation to new environment,

handling distributed resources, stochastic demand and optimal response to the smart appliances. A smart grid

also includes diverse and distributed energy sources like

wind, biomass, solar PV etc; to improve overall system

reliability and availability for the benefit of customers

and the environment. Integration of two or more DGs

improves reliability of smart grid but poses a variety of

issues like dynamic response and advanced protection to

take into account the bi directional flow of power.[1]

Transients during start-up might affect the

operation of these plants and other dispersed generation

sources connected at the distribution level. In case of distributed generation which comprises a significant part

of the generation system, their sudden disconnection

might lead to a large unbalance of power and in worst

cases to system collapse. This becomes more pronounced

in cases where the renewable energy sources are

connected to weak AC systems.

The study reported in this paper addresses some

of the above issues and attempts at parametric analysis.

Besides, the study is also aimed at investigating the

optimal location and sizing of renewable energy sources

in the context of typical distribution system in India.

Accurate model of biomass-wind generation suitable for

electromagnetic transient simulation has been developed

and the results are presented in this paper. As an introduction, the paper also gives a status of various

renewable energy sources in India.

II. Biomass energy in India

Biomass is a primary source of energy. Biomass

is very versatile in terms of variety of forms and number of options available for its utilization. Biomass is a

renewable energy source derived from various humane

and natural waste products [2]. Biomass is considered as

renewable source of energy because the organic matter is

generated every day. Present contribution of biomass

energy is between 4% and 18% of total primary energy

consumption of various developed and developing

countries respectively. By 2015 A.D. the situation is

likely to change with increase in the biomass energy

consumption to 25%-40% [2].The estimated potential of

Biomass based renewable energy options in India are as

follows: Biomass energy - 16,000 MW

Biogas Co-generation - 3,500 MW

Total - 19,500 MW

Electrical energy can be obtained from biomass

using one of several processes such as direct combustion,

gasification, pyrolysis, anaerobic digestion etc. One of the

popular methods is direct combustion. In this method

biomass is used to heat up water and generate steam and

the steam is used to rotate a turbine that is connected to a

synchronous generator. Electricity from biomass reduces

our dependence on fossil fuels. Being renewable source of energy there is no threat of running out of resources.

Electricity produced by biomass reduces the threat of

global climate change. Clearing biomass from forest areas

16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, 2010 62

Department of Electrical Engineering, Univ. College of Engg., Osmania University, Hyderabad, A.P, INDIA.

help to prevent forest fires. Biomass by-product methane

gas eliminates odor and reduces air pollution[2]. Use of

biomass waste for electricity generation eliminates the

need to place it in landfills.

Biomass based power generation has been considered to be an important component of the

Distributed generation being planned in India during the

coming decade. Besides, increased penetration of

renewable energy sources would go a long way in

reducing carbon emission from the conventional coal

based thermal power generating stations in India.

III. Wind energy conversion system

The wind-turbine based DG unit is one of the

fastest growing sources of power generation in the world

mainly due to (i) strong worldwide available wind

resources, (ii) environment friendly power generation

source especially suitable for remote areas, and (iii) rapid

technological development [4]. The continuous trend of

increase in the rate of DG connection and penetration

depth of wind-turbine based DG units can provoke

several technical concerns and adverse impact on the operation of distribution systems [4].

Control and protection, stability issues and

power quality of the supply are the main concerns.

However, the presence of an electronically-interfaced DG

unit in a Smart grid environment an ensure stability of the

grid and maintain power quality of the system.

IV. Renewable integration in

smart grid

Smart Grid technology is recognized as a key component of the solution to challenges such as

increasing electric demand, an ageing utility

infrastructure and workforce, and the environmental

impact of greenhouse gases produced during electric

power generation. Integrated Smart Grid solutions

combine advanced sensing technology, two-way high-

speed communications using the utilities assets, 24/7

monitoring and enterprise analysis software and related

services to provide location-specific, real-time

actionable data as well as home energy management

solutions to provide enhanced services for the end-users. As a result, these solutions increase the efficiency

and reliability of the electric grid while reducing the

environmental impact of electric usage benefiting

utilities, their customers, and the environment [6].

Renewable energy sources such as wind or solar

are variable and thus the operating schedules of such

plants are largely dictated by the changing “fuel” supply. This is especially pertinent in the case of wind,

photovoltaic solar and run-of-the-river hydro, none of

which have inherent storage in their power plant design.

These systems cannot be controlled in the same manner

as a conventional generation facility. With low levels of

wind or solar energy penetration the overall effect on

grid operations is limited, yet as the penetration levels

increase so too do the effects. It has been recognized

that as the penetration levels increase, more advanced

control of the power system will be required to maintain

system reliability [6]. These controls include more efficient use of transmission, use of demand response

and intelligent energy storage, all of which can be

enabled through the application of a smart grid. In fact,

the ability to better integrate renewable energy is one of

the driving factors in some smart grid installations.

V. Study system

KIADB industrial feeder

The problems associated with integration of

renewable energy sources in a smart grid have been

studied by considering an actual 11 kV power distribution

feeder in Karnataka. The KIADB Industrial Feeder (Appendix1) in Tumkur District of BESCOM is fed from

Antharasanahalli 220/66/11kV Substation. This is an

industrial feeder which contains most of the HT

consumers. The radial network consists of 8km length of

11 kV feeder. Although there are more than 19 nos. of

Distribution Transformer Centres (DTC) of various

ratings, transformers of lower ratings have been lumped

with their equivalent ratings being considered for the

study without changing the characteristics of the loads.

Ratings of transformers are 250kVA, 500kVA and 1000

kVA respectively. The utility substation is represented as

a 11kV source with its equivalent power frequency short circuit capacity of 750 MVA.

The 11 kV feeder includes two DG units. DG1 is

a 1.875-MVA conventional Biomass synchronous

generator equipped with excitation and governor control

systems. DG2 represents a fixed-speed induction

generator type wind-turbine set with rated capacity of

1.25-MVA.

VI. System model

The well known PSCAD/EMTDC software

package is used for the simulation of the Smart-grid



system of Fig. 6. The component models used for the

simulation are as follows:

The main grid is represented by an equivalent model of

an 11-kV three-phase voltage source with the short-

circuit capacity of 750 MVA and appropriate X/R ratio.

The loads are modelled as constant impedances. DG1 is

modelled as a single-mass synchronous machine. The

machine electrical system is represented in the d-q-0

frame with one rotor winding on each axis. The excitation and governor systems of the machine are also

included in the model. During start-up procedure, the

synchronous generator is treated as a source where the

rotor speed is constant. After 0.3 s the machine model is

activated and at 0.4 s the rotor speed is released to be

adjusted by the governor. The synchronous machine

parameters are given in table given below. DG2 is

modelled as a squirrel cage induction generator with a

wind source, turbine and a governor.

A. Biomass generator model

The electrical part of DG1 is represented by a

synchronous generator connected to the utility grid. The biomass system consists of synchronous generator,

exciter, steam turbine and governor. The rated power is

1.875 MVA, rated voltage 11 kV (L-L rms) and

generated power is 1.5 MW.

B. Wind generator Model

The electrical part of DG2 is represented by a

squirrel cage induction generator connected to the utility

grid. The mechanical systems of the DG2 are also

modelled. The variable nature of the wind speed and its

reflection on the input mechanical torque of the induction

generator are also modelled by a wind-speed control

panel. The rated power is 1.25 MVA, rated voltage is 11

kV and generated power is 1.4 MW.

VII. Study cases

A. Optimum location of REs

In the first study we will find the optimal locations of

both the renewable. The objective function is to achieve

minimum total losses in the network. There is a

constraint for the optimal location.

(i) Voltage should be within permissible limits (10.5 kV

to 11.5 kV).

Annual energy loss calculation is given in table 1.

TABLE 1

ANNUAL ENERGY LOSS CALCULATION

Table 1 show that when we integrate renewable energy

sources into the system, the losses reduces the losses in the system and the optimum location of biomass

generator is Bus 5 and wind generator is Bus 8.

B. Steady state analysis Base case consists of 11 kV radial distribution network

without the integration of any renewable energy sources. Power flow study was conducted. The PSCAD/EMTDC

representation of the 11 kV study system of fig 1 is

shown in fig 4.The bus 5 and bus 8 voltages, active and

reactive powers are given in table 2.



TABLE 2

COMPARISON OF VOLTAGE, POWER AND REACTIVE POWER IN RENEWABLE INTEGRATION

Table 2 shows that when we integrate renewable into the

system, the voltage profile on remote buses will improve. Simultaneously reliability of the system increased.

Comparison graphs of voltages, active powers and

reactive powers during integration of renewables are

shown {fig 1(a), 1(b) and 1(c)} below.

Fig 1(a) Voltages at all buses

Fig 1(b) Active powers at all buses

Fig 1(c) Reactive powers at all buses



C. Dynamic Analysis

In this case, power flow studies were initially

carried out by including the two renewable energy

sources. Subsequently, the impact of disconnection of

renewable sources is simulated by disconnecting each of

the two sources, one at a time and observing the change

in the load flow as also the voltage profile of the buses to

which these sources are originally connected in the

distribution system. At t=1 sec, wind system is

disconnected and remains in the same condition.

Dynamic analysis of renewable integration with the

smart grid is studied. Comparison graphs of voltages, active powers and reactive powers during integration of

renewables are shown in Table 3 and same is represented

as fig. 2(a)(b) and(c) in graphical representation.

TABLE 3

COMPARISON OF VOLTAGE, POWER AND REACTIVE POWER IN RENEWABLE DISCONNECTION

Fig 2(a) Voltages at all buses

Fig 2(b) Active power at all buses



Fig 2(c) Reactive power at all buses

We observe that when renewable are disconnected from

the network, voltage profile of all buses reduces and

reliability of the system weakens. Simultaneously load

on the grid increases in view of disconnected renewable.

D. Fault analysis

Case 1: In this case a 3phase symmetrical fault is

applied on the bus 5. The fault occurs at t= 1 sec and

remains for 5 cycles in the system. The bus voltage and

fault current without renewable are shown in fig below:-

Fig 3(a) Fault Voltage at Bus 5 without REs

Fig 3(b) Fault current at Bus 5 without REs




fault current with biomass renewable integration are

shown in fig below:-

Fig 3(c) Fault Voltage at Bus 5 with biomass integration

Fig 3(d) Fault current at Bus 5 with biomass integration




fault current with wind renewable are shown in fig

below:-

Fig 3(e) Fault Voltage at Bus 5 with wind integration



Fig 3(f) Fault current at Bus 5 with wind integration

We observe from all the above mentioned 3 cases that

when fault is cleared at t=1 sec, in that case time of

recovery voltage with biomass renewable integration is

very less but with wind integration time of recovery

voltage is very high. Table 4 shows the comparison of

TRVs with biomass and wind integration.

TABLE 4

COMPARISON OF TIME OF RECOVERY

VOLTAGES

Faulted

bus

number

Without

REs

integration

(TRV) in

sec

With only

biomass

integration(TRV)

in sec

With only

wind(TRV)

in sec

Bus 5 0.001 sec 0.001 sec 2.5 sec

It is seen from table 4 that time of recovery voltage is

more in case of wind induction generator.

E.Unbalance voltage

In this case voltage unbalance occurs in the system and

Fig 4.1(a) shows the unbalance in voltage without

renewable integration and fig 4.1(b) shows the unbalance

with integration of renewables.

Time in sec

Time ... 0.225 0.250 0.275 0.300 0.325 0.350 0.375 0.400 0.425 ...

...

...

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

Vo

lta

ge

(kV

)

Es

Fig 4.1(a) Voltage unbalances at bus 1 without REs

Time in sec

1.750 1.775 1.800 1.825 1.850 1.875 1.900 1.925 ...

...

...

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

Vo

lta

ge

(kV

)

Es

Fig 4.1(b) Voltage unbalances at bus 1with REs

It is observed that at t= 1sec, renewables are integrated

into the system and percentage unbalance reduced

significantly. Table 5 shows the comparison of

percentage unbalance without renewable and with

renewables.

TABLE 5

COMPARISON OF PERCENTAGE UNBALANCE

WITH AND WITHOUT RENEWABLES

Bus No Voltage unbalance

without REs(in %)

Voltage unbalance

with REs (in %)

Near feeder 6.4 1.3

At biomass

bus

6.5 1.24

At wind bus 6.71 1.0



F. Islanding phenomenon

An island is “That part of a power system

consisting of one or more power sources and load that is,

for some period of time, separated from the rest of the

system.” An effect of large power unbalance in a newly

formed island can be serious; such an island may not

survive very long. On the other hand an island with

perfect production balance can very well survive for a

long time, even if there are no voltage or frequency

controllers. Islanding condition occurs when breaker is

intentionally opened to create an island. A controller has

been designed in order to shed the loads to bring the

frequency at steady state condition i.e.50 Hz. A priority

index for shedding of loads has been shown in table 6.

TABLE 6

PRIORITY INDEX FOR LOAD SHEDDING

Fig 5 shows the algorithm for load shedding.

Main : Graphs

5.60 5.80 6.00 6.20 6.40 6.60 6.80 7.00 ...

...

...

49.900

49.920

49.940

49.960

49.980

50.000

y

Frequency

Fig 5.1(a) Frequency before controller operation

Fig 5.1 (a) shows the condition when grid is

disconnected and renewables are not sufficient to supply

the entire load of the network.

Main : Graphs

5.900 5.950 6.000 6.050 6.100 6.150 6.200 6.250 ...

...

...

49.940

49.950

49.960

49.970

49.980

49.990

50.000

50.010

50.020

50.030

y

Frequency

Fig 5.1(b) Frequency after controller operation

Fig 5.1 (b) shows the condition with controller operation

and shedding of the load as per priority index(Table 6) in

order to bring the frequency back to 50 Hz.

VII Results & Discussion

This paper investigates dynamics of a 11-kV

multiple DG smart-grid system and performance of the

adopted power management strategies in two analysis,(a)

steady state analysis,(b) dynamic analysis. In steady state

analysis when we connect renewable energy sources at

bus 5 and bus 8.In dynamic analysis the radial network

consists of biomass and wind generators. During

disconnection of renewable voltage profile is reduced.

When 3phase symmetrical fault is applied on bus 5, it is

observed that time of recovery voltage is very high in

case of wind integration. During unbalance percentage

unbalance reduced with renewable integration. In

Islanding phenomenon algorithm is developed for load

shedding of non-sensitive loads.



Fig 5 Algorithm for load shedding



A sudden fluctuation in power and voltage is observed

which may cause severe disturbance in the systems,

which needs to be mitigated. The future work is to

mitigate these transients with different techniques.

VIII. Conclusion

The smart grid has been simulated using

PSCAD/EMTDC software considering various dynamic

conditions of renewable energy sources. Results show

that when Renewable energy sources are connected to

the Distribution system, the voltage profile gets

improved and this improves reliability of the system.

Also, sudden connection or disconnection of renewable energy sources due to faults etc. may result in

unacceptable transients in voltages in the distribution

system which needs to be mitigated.

IX. Acknowledgement

This work is carried out at Central Power Research

Institute, Bangalore. The authors wish to thank CPRI and

NITC for permitting to publish this work. The assistance

rendered by Ms. Reshma, Project Engineer in simulation is gratefully acknowledged.

XII. References [1] Robert H. Lasseter, “Microgrids and Distributed

Generation” Transaction on journal of Energy

Engineering, American Society of Civil Engineers, Sept.

2007

[2] Dr. Mrinalini Das ,Nripen Das,” Biomass : A

sustainable source of energy”IEEE-transaction ,Volume

1 ,issue 3,pp 978-982,2009. [3] Amit Kumar Jindal, Aniruddha M. Gole and

Dharshana Muthumuni,”Modeling and performance

analysis of an integrated wind/diesel power system for

off-grid Locations Fifteenth National Power Systems

Conference (NPSC) Transaction pp 574-579,

December,2008.

[4]Badrul Chowdhury,Srinivas Chellapilla”Double-fed

induction generator control for variable speed

wind power generation” Science@Direct on Electric

Power Research,Vol.76,PP.786-800,2006.

[5] F. Mei, B. C. Pal: Modeling and small-signal analysis of a grid connected doublyfed induction

generator, presented at Proceeding of IEEE PES General

Meeting 2005, San Francisco, USA, 2005.

[6] X. P. Zhang,” A Framework for Operation and

Control of Smart Grids with Distributed

Generation”,IEEE transaction Vol 29,issue 2,pp 1-

5,2008.

Authors’ information

1 Suhas shirbavikar, department of Electrical Engg., NIT

Calicut ([email protected])

2 S.Ashok, department of Electrical Engg. NIT Calicut

3 M. M Babu Narayanan, central power research

institute, Bangalore.



mailto:[email protected]

#1

#2

#1

#2

#1

#2

#1

#2

#1

#2

P = 1.528Q = 1.332V = 10.53

V

A

P = 0.8802Q = 0.9511V = 10.37

V

A

BUS 6 BUS 7

0.099[ohm] 0.27[ohm]

1.16[ohm] 1.008[ohm]

0.54 [mH]0.19 [mH]

2.33 [mH] 2.35[mH]

0.305[ohm]

0.61 [mH]

#1

#2

P = 0.5635Q = 0.7653V = 10.26

V

A

BUS 8

1.12[ohm]

2.34[mH]

BUS 1

#1

#2

0.2[ohm]

0.4 [mH]

0.26[ohm]

0.52[mH]

P = 0.9407Q = 1.785V = 10.92

V

A

EF IF

VTIT 3

IfEfEf0

Vref

Exciter_(AC1A)

Vref0

S2M

STe

3

AV

Tm

Tm0

Ef0

Tmw

Ef If

1.875 MVA1.5 MW6.3kV0.1 kA

S / Hinhold

out

P = 1.571Q = 1.359V = 10.79

V

A

TIME

BETA

ES Vw

TmVw

Beta

W P

Wind TurbineMOD 5 Type

GR

TIME

Wind TurbineGovernor

Beta

PgMOD 2 Type

PwindA

B

Ctrl

Ctrl = 1

CNT

0.72

VwES

Wind SourceMean

1.0

A

B

Ctrl

Ctrl = 1

CNT

*N

D

N/D

3.0Pole pairs

1.01308

*-1

I M

W

S

Twind gen

*-1

2 Pi *50.0

P = 0.03516Q = -0.4148

V = 10.26

V

A

P = 0.9496Q = 1.79

V = 10.97

V

A

P = 1.467Q = 0.07325V = 10.79

V

A

W

W

1.0

w

Wref

Cv

Steam Gov 2

Iv

Steam_Tur_2

w Tm1

Tm2WrefIv

CvTmstdy

Ef

POUT QOUT

P =

0.1

64

3Q

= 0

.08

17

1V

= 0

.41

24

V A

P =

0.3

29

3Q

= 0

.16

22

V =

0.4

10

4

V A

P =

0.1

63

5Q

= 0

.08

08

4V

= 0

.41

V A

P =

0.1

58

8Q

= 0

.07

99

4V

= 0

.40

89

V A

P =

0.6

31

6Q

= 0

.30

76

V =

0.3

99

V A

P =

0.3

07

Q =

0.1

49

4V

= 0

.39

31

V A

P =

0.5

98

7Q

= 0

.29

19

V =

0.3

88

7

V A

Main : Controls

30

0

Es

9.52381

200

0

GR

25.3968

RLC

P+jQ

P+jQ P+jQ

P+jQ

P+jQ

P+jQ

P+jQ

BUS 5P = 0.2618Q = 1.382V = 10.79

V

A

P = 0.4298Q = 1.481V = 10.82

V

A

BUS 4BUS 3P = 0.7609Q = 1.677V = 10.83

V

A

BUS 2P = 0.9338Q = 1.781V = 10.88

V

A

Fig 6.PSCAD/EMTDC model of biomass and wind integrated system



Appendix

Study system is taken as KIADB feeder in Tumkur

district in Karnataka. Rabbit conductor is used for

transmission line .The total length of distribution

network is 8 kms.17 distribution transformers are taken

into consideration for the study. Ratings of transformers

are 250kVA, 500kVA and 1000 kVA respectively.

Loading on the transformer is 90% as per the survey

conducted. The utility substation is represented as a

11kV source with its equivalent power frequency short

circuit capacity of 750 MVA.

Study system



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