simulation for smart grid integration of · simulation for smart grid integration of solar/wind...

SIMULATION FOR

SMART GRID

INTEGRATION OF

SOLAR/WIND ENERGY

CONVERSION SYSTEM

1Dr.V.Jayalakshmi,

2Sujeet Kumar

1Associate Professor,

2UG Student

Department of EEE

BIHER, BIST, Bharath University

Chennai- 600073.

[email protected]

Abstract

Due to the fact that solar and wind power are

intermittent and unpredictable in nature, could

cause and create high technical challenges.

Unfortunately the actual energy conversion

efficiency of PV and wind energy system using

traditional controllers is low. To overcome this

problem and to improve the efficiency of the

system, MPPT with an intelligent control

techniques are used with closed loop system. In

this paper, the system is designed for constant

wind speed and varying solar irradiation and

insolation. Maximum power point tracking

(MPPT) algorithm is used to extract the

maximum power from PV array. Fuzzy logic

controller and PI controller are used to control

the duty cycle of the converter switch thereby

extracting the maximum power from solar

array. The system consists of photovoltaic (PV)

array, wind energy conversion system (WECS),

boost converter, and LC filter. The entire

proposed system has been modeled and

simulated using MATLAB/simulink software.

Keywords:, Fuzzy logic controller,Maximum

power point tracking, PI controller,Photo voltaic

system.

I. INTRODUCTION

Globally we are facing two major issues now

regarding electric power generation. The existing

power systems are fossil fuels like coal, gas etc.

burning of these fossil fuels giving rise to the

emission of carbon dioxide into the environment.

This action is the major reason for global warming

effects that cause environmental impacts.[2-7].The

available global resources are decreasing day by

day. The limitation of global resources of fossil and

nuclear fuels, push us to go for alternative sources

of energy & new way has to be found to balance

the supply and demand without fossil and nuclear

fuels.[13]. The renewable energy offers alternative

sources of energy which are generally pollution

free. But solar and wind power is naturally

intermittent and can create technical challenges to

operate.[8-12]. The peak operating time for wind

and solar systems occur at different times of the

day for different places. Therefore the solar and

wind energy system can’t provide a continuous

supply, can generate electricity only during sunny

and windy days.[14-19]. Therefore integration of

both solar and wind energy system into an optimum

combination improves overall efficiency of the

system. [20-21].The integration of PV and wind

system become more economical to run since the

weakness of one system can be complimented by

the strength of the other one.[1].

II. SMART GRID

The continuous and expanded growth of the share

of renewables in centralised and decentralised grids

requires an effective new approach to grid

management, making full use of “smart grids” and

“smart grid technologies”. Existing grid systems

already incorporate elements of smart functionality,

but this is mostly used to balance supply and

demand. Smart grids incorporate information and

communications technology into every aspect of

electricity generation, delivery and consumption in

order to minimize environmental impact, enhance

markets, improve reliability and service, and reduce

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 7897-7910ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

7897

costs and improve efficiency. These technologies

can be implemented at every level, from generation

technologies to consumer appliances. As a result,

smart grids can play a crucial role in the transition

to a sustainable energy future in several ways:

facilitating smooth integration of high shares of

variable renewables; supporting the decentralised

production of power; creating new business models

through enhanced information flows, consumer

engagement and improved system control; and

providing flexibility on the demand side.The

introduction of smart Grid technology is an

essential requirement that reduces overall green

house gas emissions with demand management that

encourages energy efficiency, improves reliability

and manages power more efficiency and more

effectively. Smart Grid is the system comprising of

the communication technology, information

technology and power system. Smart Grids is the

combination of centralized power plants and

distribute power generators that allows multi

directional power flows. Its two ways power

combination system. Smart Grid is an electric

power system which accomplishes in the Grid

system that is generators, transmission, distribution

operators and electric consumers are communicate

and work with each other to raise the efficiency

and reliability of the Grid. The key characteristics

of smart grid include.

Grid optimization, system reliability and

operational efficiency. Distributed generation not

only traditional large power stations, but also

individual PV panels, micro-wind, etc. Advanced

metering infrastructure (AMI) including smart

meters. Grid-scale storage. Demand response.

Plug-in hybrid electric vehicles (PHEVs) and

vehicle to grid (V2G).

Intelligent and efficient: Smart Grid is capable of

sensing system over loads and rerouting power to

prevent or minimize outage. It is efficient to meet

the increasing demand without adding any

infrastructure.

Accommodating: It can accommodate energy from

fuel sources as well as R E sources.

Reduce Global Warming: Possible to integrate

large scale R E into Grid that reduces global

warming effects.

Reliability It improves power quality and

reliability as well as enhance the capacity of

existing network.[27-29]

Increasing renewable electricity generation is an

essential component in achieving a doubling of the

renewable energy share in the global energy mix.

Such a transition is technically feasible, but will

require upgrades of old grid systems [30-37]and

new innovative solutions to accommodate the

deferent nature of renewable energy generation. In

particular, smart grids are able to incorporate the

following characteristics:

Variability.Some forms of renewable electricity,

notably wind and solar, are dependent on an ever-

fluctuating resource (the wind and the sun,

respectively). [32-34]As electricity supply must

meet electricity demand at all times, efforts are

required to ensure that electricity sources or

electricitydemand is available that is able to absorb

this variability.

Distributed generation.Distributed renewable

generation smaller-scale systems, usually privately

owned and operated represent a new and different

business model for electricity. [35-37]Traditional

utilities are often uneasy about allowing such

systems to connect to the grid due to concerns over

safety, effects on grid stability and operation, and

the difficulties in valuing and pricing their

generation.

High initial cost.Renewable electricity generating

technologies typically have higher first costs and

International Journal of Pure and Applied Mathematics Special Issue

7898

lower operating costs than fossil-fuelled electricity

generating technologies. Although renewable may

be “cost effective” on a lifecycle basis, some

electricity systems particularly in developing

countries simply do not have access to sufficient

capital to invest in renewables.

Smart grid technologies can directly address these

three challenges of[25-29] renewable electricity

generation. In addition, smart grids over added

benefits that can further ease the transition to

renewables.

Applications of smart grid technologies can be

found across the world, from isolated islands to

very large integrated systems. For developed

countries, smart grid technologies can be used to

upgrade, modernize or extend old grid systems,

while at the same time providing opportunities for

new, innovative solutions to be implemented. For

developing and emerging countries, smart grid

technologies are essential to avoid lock-in of

outdated energy infrastructure, attract new

investment streams, and create efficient and

flexible grid systems that will be able to

accommodate rising electricity demand and a range

of different power sources. Smart grid technologies

are already making significant contributions

toelectricity grid operation in several countries.

Denmark, Jamaica, the Netherlands, Singapore, and

the United States (New Mexico and Puerto Rico)

are have successful combinations of smart grid

technologies with renewable energy integration.

The successful implementation of smart grid

technologies for renewables requires changes in

policy and regulatory frameworks to address non-

technical issues, particularly with regards to the

distribution of benefits and costs across suppliers,

consumers and grid operators.

Many of the benefits of smart grids and renewable

depend largely on how projects are implemented.

Effective project planning and execution are key to

realizing these benefits.[28-29] It is crucial to

perform tests to ensure that smart grid technologies

will integrate successfully with legacy hardware

and back-office systems before developing a new

project. Power system data with good spatial and

temporal granularity is important for analyzing the

potential benefits of smart grid projects. Grid

operators considering smart grid projects should

start gathering hourly load data as [11-19]soon as

practical, preferably at the feeder level. Once smart

grid projects are in progress, success often depends

on realising the substantial value of the large

amounts of data generated. Smart grid technologies

can enable renewables, and make better use of

existing infrastructure and hence increase the

efficiency of the system.

III.THE PHOTOVOLTAIC MODULE

The general mathematical model for the solar cell

has been studied over the past three decades. The

circuit of the solar cell model, which consists of a

photocurrent, diode, parallel resistor (leakage

current) and a series resistor; is shown in Fig. 1.

According to both the[25-29] PV cell circuit shown

in Fig. 1 and Kirchhoff’s circuit laws, the

photovoltaic current can be presented as follows

Where Igcis the light generated current, Io is the

dark saturation current dependant on the cell

temperature, e is the electric charge = 1.6 x 10-19

Coulombs, K is Boltzmann’s constant = 1.38 x 10-

23 J/K, F is the cell idealizing factor, Tcis the cell’s

absolute temperature, vdis the diode voltage, and

Rpis the parallel resistance.


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Fig. 1: Single diode PV cell equivalent circuit

The photocurrent (Igc) mainly depends on the solar

irradiation and cell temperature, which is described

as

Where μscis the temperature coefficient of the

cell’s short circuit current, Trefis the cell’s

reference temperature, Iscis the cell’s short circuit

current at a 25°C and1kW/m2, and G is the solar

irradiation in kW/m2. Furthermore, the

cell’ssaturation current (Io) varies with the cell

temperature, which is described as

Where Ioα is the cell’s reverse saturation current at

a solar radiation and reference temperature, Vg is

the band-gap energy of the semiconductor used in

the cell, and Vocis the cells open circuit voltage. In

this study, a general PV model is built and

implemented using MATLAB/SIMULINK to

verify the nonlinear output characteristics for the

PV module. The proposed model is implemented,

as shown in Fig. 2. In this model, whereas the

inputs are the solar irradiation and cell temperature,

the outputs are the photovoltaic voltage and

current. The PV models parameters are usually

extracted from the manufactures data sheet.

Fig.2: Block diagram of the proposed system

IV. MODELING AND DESIGN OF A WIND

TURBINE AND INDUCTION

GENERATOR

Wind energy is not a constant source of energy. It

varies continuously and gives energy in sudden

bursts. About 50% of the entire energy is given out

in just 15% of the operating time. Wind strengths

vary and thus cannot guarantee continuous power.

It is best used in the context of a system that has

significant reserve capacity [36-37]such as hydro,

or reserve load, such as a desalination plant,

tomitigate the economic effects of resource

variability.The power extracted from the wind can

be calculated by the given formula:

Pw = 0.5ρπR3Vw3Cp(λ,β)

Pw = extracted power from the wind,

ρ = air density, (approximately 1.2 kg/m3 at

20¤ C at sea level)

R = blade radius (in m), (it varies between

40-60 m)

Vw = wind velocity (m/s) (velocity can be

controlled between 3 to 30 m/s)

Cp = the power coefficient which is a function

of both tip speed ratio (λ), and blade

pitch

angle, (β)(deg.) Power coefficient (Cp) is defined

as the ratio of the output power produced to the

power available in the wind. No wind turbine could

convert more than 59.3%of the kinetic energy of

the wind into mechanical energy turning a rotor.

This is known as the [37-39]Betz Limit, and is the


7900

theoretical maximum coefficient of power for any

wind turbine. The maximum value of CPaccording

to Betz limit is 59.3%. For good turbines it is in the

range of 35-45%. The tip speed ratio(λ) for wind

turbines is the ratio between the rotational speed of

the tip of a blade and the actual velocity of the

wind. High efficiency 3-blade-turbines have tip

speed ratios of 6–7.

Wind turbine generators (WTGs) extract energy

from wind and convert it into electricity via an

aerodynamic rotor, which is connected by a

transmission system to an electric generator.

Today’s mainstream [12-18]WTGs have three

blades rotating on a horizontal axis, upwind of the

tower Two-blade WTGs and vertical-axis WTGs

are also available. In general, a WTG can begin to

produce power in winds of about 3 m/s and reach

its maximum output around 10 m/s to 13 m/s.

Power output from a WTG increases by the third

power of wind speed, i.e. a 10 % increase in wind

speed increases available energy by 33 %, and is

directly proportional to the rotor-swept area (the

area swept by the rotating blades). Power output

can be controlled both by rotating the nacelle

horizontally (yawing) to adapt to changes in wind

direction, and rotating the blades around their long

axes (pitching) to adapt to changes in wind

strength. Offshore wind power generation sites

generally have better wind resources than onshore

sites, [11-17]so WTGs installed in offshore sites

can achieve significantly more full-load hours.

Offshore wind farm development can also relax

many constraints faced by onshore wind farms,

such as transport and land occupation.

V. MPPT TECHNIQUES.

Solar and wind energy system is naturally

intermittent.

The P V system does not provide constant energy

because its output power changes into temperature

and insulations level. The actual energy conversion

efficiency of P V Module is low and it ranges from

7% to 15%.[18-24] To overcome these problems

MPPT with intelligent control techniques are used

to extract maximum available power from PV

System. The power output from the solar panel is

function of irradiation level and temperature. Given

operating conditions, we have a curve which gives

the voltage level maintained by the panel for a

particular value of current. This is known as

characteristics of cell. The intersection point of

load line with characterizing plot is the operating

point.

MPPT is an electronic system that operates the PV

panel in a manner that allows the module to

produce all the power they [11-24]are capable of. It

varies the electrical operating point of the module

so that the modules are able to deliver maximum

available power.

Many techniques are used to track MPP have been

proposed in early nineties. Conventional algorithm

such as P and O fail to reach the maximum power

point of PV system because of using fixed step

size. These problems are overcome by using

intelligent control techniques to track MPP of PV.

The conventional algorithm changes the value of

duty cycle of switching signal of convertor to track

MPP by the fixed step each time. Because of this

the optimal values are not reached. The output

power can be increased by tracking MPP of PV

module by using a controller connected to a DC –

DC convertor. [40-42]The MPP changes with the

insolation level and temperature due to the non

linear characteristics of PV module. Each type of

PV module can have its own specific

characteristics. In general there is a single point in

the V-I or V-P called the maximum power point at

which the entire PV system is operated with

maximum efficiency and produces its maximum

output power. This point can be located with the

help of MPPT.


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VI. PI CONTROLLER:

PI controller is mainly used to eliminate the steady

state error resulting from P controller. However, in

terms of the speed of the response and overall

stability of the system, it has a negative impact.

This controller is mostly used in areas where speed

of the system is not an issue. Since P-I controller

has no ability to predict the future errors of the

system it cannot decrease the rise time and

eliminate the oscillations. If applied, any amount of

I guarantees set point overshoot. A PI controller

attempts to correct the error between a measured

process variable and a desired set point by

calculating and then outputting a corrective action

that can adjust the process accordingly. The

proportional term makes the change in output that

is proportional to the current error value. The

proportional response can be adjusted by

multiplying the error by a constant value, Kp called

as proportional gain. The integral term causes the

steady-state error to reduce to zero, which is not the

case for proportional-only control in general. [25-

29]The integral term is proportional to both the

magnitude of error and the duration of the error.

The magnitude of the contribution of the integral

term to the overall control action is determined by

the integral gain Ki.

PI controller is the co-operation of both propotional

and integral action. The analytical expression can

be given

as:

𝑃 = 𝐾𝑝 𝑒 + 𝐾𝑝 𝐾𝑖 𝑒 𝑑𝑡 + 𝑝 ( 0 )

Where,

P = controller output

Kp = proportional gain

Ki = integral constant

E = error input

P(0) = initial value of controller output

The PI controller gain and integral gain are

obtained by Zeigler Nichols method. The output

voltage is compared with reference voltage and the

error signal is given as input to the PI Controller.

The PI Controller minimizes the error and the

output of the PI controller is given to discrete

power generator.

VII. FUZZY LOGIC CONTROLLER

The O/P voltage of PV module is varies with

varying temperature and varying insulation. So, the

output power also varies. To extract the maximum

power from PV modules MPPT with FLC is used.

The error and change in error values are calculated

using

Error (K) = P(K) – P(K-1) / V(K) – V(K-1)

Change in error (K) = error (K) – error (K-1)

where P(K) is the instant power of PV system. The

input error (K) shows whether the operating point

is located to the left or right of the MPP at the

instant K.

The change in error (K) indicates the movement of

operating point.

The error and change in error are given as inputs to

FLC. The FLC examines the output power at each

instant (K) and determines the change in power

relative to voltage dP/dV.

If dP/dV is greater than Zero the controller change

the duty cycle of pulse width modulation until the

power is maximum or dP/dV is equal to Zero.

If value is less than Zero then the controller

changes the duty cycle to decrease the voltage and

the power is maximum.[7-14]

The FLC’s output is given to PWM generator and

fed as duty cycle to the switch corresponding to the

solar input of the convertor.


7902

The basic function of FLC are fuzzification, rule

base, defuzzification.

Fuzzification: Fuzzification includes the design of

input and output membership function.

Rule base: It defines relationship between the input

and output membership function. The control rules

are evaluated by the inference mechanism.

Defuzzification: it uses center of gravity to

compute the output of FLC. The output is nothing

but duty cycle.

Fuzzification stage converts input variable into

linguistic variable based on a[14-18] membership

function. As fuzzy levels increases, the accuracy

will increase.

The fuzzy logic controller output is converted from

a linguistic variable to a numerical variable using

membership function in the defuzzification stage.

By defuzzification, the controller produces an

analog output signal which can be converted to a

digital signal and controls the power convertor of

MPPT system.

The effectiveness of FLC is depends upon the

accuracy of the calculation of error and its variation

and rule based table developed by users.[21-29] For

better efficiency the membership functions and the

rule base table can be continuously updated to

achieve optimum performance.

Fig 3: Block diagram of fuzzy logic control

based MPPT

VII. SIMULATION RESULTS PV AND

WIND ENERGY CONVERSION

SYSTEM USING PI CONTROLLER

Fig 4 Circuit diagram for PV and wind energy

system using PI controller

PV and wind energy system with PI controller has

shown in fig. 4 For any PV system, the output

power can be increased by tracking the MPP

(Maximum Power Point) of the PV module by

using a controller connected to a dc- dc converter.

However, the MPP changes with insolation level

and temperature due to the nonlinear characteristic

of PV modules. The boost converter is a type of

DC-DC converter that has an output voltage

magnitude that is either greater than or less than the

input voltage magnitude. It is a switch mode power

supply with a similar circuit topology to the boost

converter. The output voltage is adjustable based

on the duty cycle of the switching MOSFET. Also,

the polarity of the output voltage is opposite to the

input voltage. Neither drawback is of any

consequence if the power supply is isolated from

the load circuit as the supply and diode polarity can

simply be reversed. The switch can be on either the

ground side or the supply side. While in the On-

state, the input voltage source is directly connected

to the inductor (L). [34-38]]This results in

accumulating energy in L. In this stage, the

capacitor supplies energy to the output load. While

in the Offstate, the inductor is connected to the

output load and capacitor, so energy is transferred

from L to C and R.


7903

Fig 5 output voltage of PV panel

The figure 5 shows the output voltage of pv panel.

The irradiation disturbance occurred at 0.25

second. The output voltage varies from 12 V to

14V.

Fig 6 Output voltage of RL load

The figure 6 shows the output voltage of RL load.


second. The output voltage varies from 60V to 63V

at 0.25 second due to the irradiation disturbance.

Due to PI controller action the voltage settled to

normal value after a rise time of 0.35 second at 0.6

second.

Fig 7 Output current of RL load

The figure 7 shows the output current of RL load.


second. The output current changes at 0.25 second

due to the irradiation [31-39]disturbance. Due to

PIcontroller action the current settled to normal

value after a rise time of 0.35 second at 0.6 second

Fig 8 Output power of RL load

The figure 8 shows the output power of RL load.


second. The output power changes at 0.25 second

due to the irradiation disturbance. Due to PI

controller action the power settled to normal value

after a rise time of 0.35 second at 0.6 second

The figure 4 shows the circuit arrangement of PV

and wind energy system with PI. To test the

performance of the proposed algorithm, the PV

system has been simulated in Matlab/Simulink. The

model as shown in figure 4 is composed of PV and

wind system, boost converter, MPPT controller,

and resistance and inductive load. Controlled

voltage source is utilized to connect PV system

with boost converter.[40-42]

The key specification of PV panel is taken in this

study is listed below.

Parameter Values are

Maximumpower (Pm) 59.12 (W)

Open circuit voltage (Voc) 12.19 (V)

Short circuit current (Isc) 5.45 (A)

Current at Pm (Iamp) 4.85 (A)

Temp coefficient for Pm -0.46 (% / oC)

Temp coefficient for Voc -0.129 (V / oC)

Temp coefficient for Isc + 0.052 (% / °C)

By using the PI controller the error has been

minimized in the system and the efficiency is

improved.

PV AND WIND ENERGY CONVERSION

SYSTEM USING FUZZY

LOGICCONTROLLER

Fig 9 PV and wind system with Fuzzy logic

controller


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The figure 9 shows the circuit arrangement of PV

and wind energy system with Fuzzy logic

controller. To test the performance of the proposed

algorithm, the PV system has been simulated in

Matlab/Simulink. [40-45]The model as shown in

fig 9 is composed of PV and wind system, boost

converter,MPPT with fuzzy logic controller, and

resistance and inductive load. Controlled voltage

source is utilized to connect PV system with boost

converter.

The key specification of PV panel is taken in this

study is listed below.

Parameter Values are

Maximumpower (Pm) 59.12 (W)

Open circuit voltage (Voc) 12.19 (V)

Short circuit current (Isc) 5.45 (A)

Current at Pm (Iamp) 4.85 (A)

Temp coefficient for Pm -0.46 (% / oC)

Temp coefficient for Voc -0.129 (V / oC)

Temp coefficient for Isc + 0.052 (% / °C)

Fig 10 Output voltage of solar panel

The figure 10 shows the output voltage of pv panel.


second. The output voltage varies from 12 V to

14V.

Fig 11 Output voltage across RL load

The figure 11 shows the output voltage of RL load.


second. The output voltage varies from 60V to 66V

at 0.25 second due to the irradiation disturbance.

Due to fuzzy logic controller action the voltage

settled to normal value after a rise time of 0.09

second at 0.33 second.

Fig 12 Output current of RL load

The figure 12 shows the output current of RL load.


second. [12-19]The output current varies at 0.25

second due to the irradiation disturbance. Due to

fuzzy logic controller action the current settled to

normal value after a rise time of 0.09 second at

0.33 second.

Fig 13 Output power of RL load

The figure 13 shows the output power of RL load.


second. The output power varies at 0.25 seconddue

to the irradiation disturbance. Due to fuzzy logic

controller action the power settled to normal value

after a rise time of 0.09 second at 0.33 second. [9-

11]

By using the Fuzzy logic controller the error has

been minimized in the system and the efficiency is

improved.

The following Table 2 shows the comparison of PI

and Fuzzy logic controller with settling time and

steady state error. [14-19]From the table it is

concluded that the rise time for fuzzy controller is

very less comparing with PI controller and hence


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the transient response of the fuzzy logic controller

is fast.

Converter Tr Ts Tp Ess

PI controller 0.35 0.6 0.39 3.3

FUZZY

controller 0.08 0.33 0 0.03

Table 2 Comparison of PI and Fuzzy logic

controller

VIII. CONCLUSION

The results of maximum power output of PV

incorporating FLC are compared with the output of

PV module with PI controller when it is connected

to load under varying irradiance levels. It is clear

from the results in Table 1that significant increase

in the power output is obtained by using FLC.

Itshows the transient response of MPPT algorithm

with PI and FLC when PV is subjected to different

irradiation. It can be seen that Fuzzy logic

controller has extremely fast response with rise

time equals to 0.08 second. The proposed system

showed its ability to reach MMP under sudden

changes of irradiation and partial shading.

Simulation results have shown that using Fuzzy

logic controller has great advantages over

conventional methods and PI controller.. The Fuzzy

Logic Controlled closed loop system is more

effective for the non – linear system and it can find

the point of Maximum Power Point in a shorter

time.

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