FUZZY BASED EV CHARGING WITH REDUCED POWER … · FUZZY BASED EV CHARGING WITH REDUCED POWER FLUCTUATION UNDER RENEWABLE POWER CONSUMPTION CONSTRAINT 1Kavin.R 2Kesavan.T 3 Dr.Nand

FUZZY BASED EV CHARGING WITH

REDUCED POWER FLUCTUATION

UNDER RENEWABLE POWER

CONSUMPTION CONSTRAINT

1Kavin.R

2Kesavan.T

3 Dr.Nandagopal.V

4Malini.T

Email: [email protected]

[email protected]

[email protected]

[email protected]

1,2&4 Assistant Professor,

3Professor Department of Electrical and Electronics Engineering, Sri Krishna

College of Engineering & Technology

Abstract

Based on environmental condition we can reduce the power consumption in domestic

load by using light and temperature sensor in the logic of fuzzy and also stabilize the grid power

.In this paper proposes that more number of vehicle can charge at same time in different places.

Our proposed model aims to minimize the total electricity cost considering user comfort, house

occupancy and EV travel patterns, thermal dynamics, EV electricity demand, and other operation

constraints.

I. INTRODUCTION

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 1691-1706ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

1691

DEMAND SIDE management in the residential sector (i.e., residential buildings) is an

important research topic since buildings contribute a significant fraction of overall electricity

consumption. In fact, it accounted for 72% of total U.S. energy consumption in 2006 out of that

residential buildings accounted for 51% according to the U.S. Environmental Protection Agency

(EPA) [1]. This research topic has received lots of attention from the research community [2]–

[8]. In [2], Shengnan et al. assessed the use of demand response as a load shaping tool to

improve the distribution transformer utilization and avoid overloading for the transformer.

Mohsenian-Rad et al. [3] proposed an optimization framework that aims to minimize electricity

bills considering user comfort. However, the assumption on homogeneous appliances and using

waiting time to represent user comfort in this paper would be too simple to represent different

characteristics of grid appliances and user requirements. In a typical grid, thermostatically

controlled appliances (TCAs) including refrigerator, electric water heater, and the heating,

ventilation, and air-conditioning (HVAC) system account for more than half of total residential

energy consumption [1]. Research on optimal control for TCA loads has been a hot research

topic in the last several years. References [4] and [5] proposed optimal control schemes to

minimize the electricity cost for the HVAC system considering user climate comfort.

Dynamic programming was employed in [6] to compare several optimal control algorithms

applied to a thermostat. In [7], the authors introduced an appliance commitment algorithm that

schedules electric water heater power consumption to minimize user payment.

Electric vehicle (EV) is another important grid element that has significant economic and

environmental advantages compared to normal cars. The penetration of EVs is expected to

increase drastically in the next few years, which can reach one million by 2015 in US [9].

Therefore, EV charging will have significant impacts on the power distribution network if it is

not controlled appropriately [10]–[12]. EV travel pattern is an important factor to model potential

impacts of EVs on the grid [13], [14] and to develop efficient EV charging strategies [15].

Given electricity prices and EV driving pattern, Rotering et al. proposed a dynamic

programming based control scheme to optimize the charging for one EV [16]. In [17], Wu et al.

considered load scheduling and dispatch problem for a fleet of EVs in both the day-ahead market

and real-time energy market. In [18], an optimal charging strategy for EVs was proposed that

considers voltage and power constraints.

The problems of scheduling of grid energy usage and EV charging are often addressed

separately in the literature. In this paper, we propose a unified optimization model that jointly

optimizes the scheduling of EVs and TCAs. In particular, we utilize EVs as dynamic storage

International Journal of Pure and Applied Mathematics Special Issue

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facility to supply energy for residential buildings during peak hours where energy can be

transferred from EVs to charge other EVs and to provide energy for HVAC in a residential

community. There are some recent works that discuss potential benefits of vehicle to building

interactions [19], [20]. However, to the best of our knowledge, none of previous works have

considered detailed design and joint optimization of EV and building energy management.

The main contributions of this paper can be summarized as follows:

• We propose a comprehensive model to optimize the EV and HVAC scheduling in a

residential area. The formulation PIUTEA aims to achieve flexible tradeoff between minimizing

total electricity cost and maintaining user comfort preference.The model accounts for the

characteristics of the HVAC system, thermal dynamics, user climate comfort preference, battery

state model, user travel patterns, and grid occupancy patterns. We also discuss potential

extensions of the proposed framework to capture various modeling uncertainty factors.

• We show the impacts of different design and system parameters, which control the

electricity cost and user comfort, on the system operation and performance as well as the

economic benefits of applying our proposed control framework compared to a non-optimized

control scheme for a single-house scenario.

• We illustrate the advantages of applying the proposed control model for the multiple-house

scenario compared to the case where each grid optimizes its energy consumption separately.

Specifically, we demonstrate that optimization of EV and grid energy scheduling for multiple

houses in a residential community can achieve the significant saving in electricity cost and

reduce the high power demand during peak hours.

II CONSTRUCTION OF CHARGINGCONTROL MODEL OF ELECTRIC

VEHICLES

The hourly charging/discharging quantity of each electric vehicleconnected to the system

is used as the state variables in this formulation. The established optimal

Charging/discharging control is expressed as

A. State Variable


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The charging/discharging power of EVs are chosen as state variables shown

in

Where and represents the charging and dischargingpower of No. i EV at time

t, respectively, andrepresents the maximal charging and discharging power of No.i EV

respectively, , and represent the arrival time and departuretime.

B. Operating Costs

The operating costs consist of energy cost and battery dischargingdegradation cost, described

as follows.

1) Energy Costs: This paper considers the charging/dischargingenergy cost as part of the

objective function, and the

Three-stage electricity price of Taiwan Power Company is used, as shown in Fig. 1. The

energy cost is expressed as

Where represents the electricity price at time t.

2) Battery Discharging Degradation Costs:


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The V2G operationof an electric vehicle battery will influence battery lifeand increase

operating costs. Reference [17] discussed the revenueand cost of V2G, and proposed a battery

degradation costmodel. This model is suitable for the economic analysis of V2G.Therefore, the

V2G battery degradation cost is calculated bythis model in this paper. The charging and

discharging powerare under the rated values to protect the battery in the proposedmodel.

However, if the power company wants to draw morepower form vehicles, the battery

degradation cost can be calculatedbased on models proposed in [26] and [27] that have

discussedeffects of currents and temperatures on the battery cyclelife.Degradation cost is

calculated as wear for V2G due to extracycling of a battery. For a battery vehicle, is expressed as

[17]

Where is the capital cost of the battery, and is the battery life expressed as kWh

(described below).Battery lifetime is often expressed in cycles, and measured ata specific depth-

of-discharge. For (4), we express battery life in

Energy throughput defined as follows [17]:

Where is the lifetime in cycles , the total energy storageof the battery, and DoD is the

depth-of-discharge for which was determined.

To sum up, the objective function of this paper is expressedAs

Where is the battery discharging cost per kWh of vehicle .Take the Li-ion battery as an

example? The battery cost perkWh is USD 350, Nisson Leaf battery capacity is 24 kWh, 2000

Life cycles in 80% DoD, , and

0.2188 USD.


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b. Constraint of Convenient Driving for EV Owner

When an EV is charged or discharged, the SOC value of thebattery must be between a

minimum value and maximum valuewhen the EV is connected to the power grid, and the set

SOCvalue must be reached before the electric vehicle owner leaves, which is expressed as

Where represents the initial value of SOC of theelectric vehicle,

represents the minimum value of SOC of the th EV, represents the charging

anddischarging efficiencies of the battery, respectively,

Represents the battery capacity of No. i EV, andrepresents the

expected SOC value of the battery at departureof the th EV owner.

D. System Constraints

When the aggregator controls EV charging and discharging, the constraints of system voltage

and line overload must beconsidered, as the power company hopes to reduce

equipmentinvestment expenses through appropriate charging and dischargingcontrol, these

constraints are described as follows.

1) Constraint of System Voltage: The voltage drop of eachline can be determined by [28]

Where is the voltage drop, R is the line resistance, X is theline reactance, P is the active

power through the line, Q is thereactive power through the line, and is the system voltage.

As shown in the distribution system of Fig. 2, the voltage drop of Line 1 is expressed as

Where, and are the original real and reactive power of node n at time t, and is the power

fromEVs of node n at time t. For the sake of explanation, Fig. 2 is


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a simplified system diagram. A practical distribution system of TPC used as the test studies is

given in Section IV.The constraint of system voltage drop is established by (9), take node 4 as an

example, the constraint of voltage drop isexpressed as

Constraint of System Line Current:

The cable of a systemline has its acceptable maximum current value, where

overloadedoperation may damage the line, causing accidents. Becausethe real power is much

larger than the reactive power, thereactive power is neglected in order to maintain the linearity

ofthis formulation. The line current constraint is expressed as


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III.EVPARAMETER SIMULATION

Based on the survey and measured data, EV usage scenariosare randomly simulated in this

paper, and stochastic models ofthese parameters are described [9] below.

1) EV Start Charging Time: The starting time of electricvehicle charging is related to the life

style of electric vehicleowners, and information can be obtained from measured valuesand

statistics [29], [30]. Fig. 3 is the distribution diagram of thestarting time of EV charging in an

EPRI report [30]. A distributionbased on Roulette wheel selection concept that depicts

theoccurrence frequency of the start charging time is used in thesimulations. The Roulette wheel

surface is divided into wedgesrepresenting the probabilities for each individual. The wedge kof

the stochastic model is calculated by

Where is the probability of the the representative load profile. Fig. 4 shows an unequally

divided uniform distribution ofstart charging time that is based on the probability densityshown

in Fig. 3. Random numbers can be generated to determinestart charging time of each vehicle. If

the number is between and , the th start charging time is selected.A start

charging time with higher probability is more likely tobe selected.

where is the charging power, is the

conversion efficiencyof charger, BatCap is the EV's battery capacity, and denote the

starting time and finishing time of charging, respectively.Based on measured values, Fig. 5

shows the probabilitydensity function of SOC values of a battery at the start ofcharging. Using

the same Roulette wheel selection conceptdescribed above, Fig. 6 shows an unequally divided

uniformdistribution of SOC at start charging time that is based on theprobability density shown

in Fig. 5. A.


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IV. PROPOSED SYSTEM

BLOCK EXPLANATIONS Grid power: This is the grid power from where the source 230V Ac is obtained.

Ac –Dc converter: This is a rectifier, which converts 230V ac into 320V Dc.

Dc-Ac inverter: This is an inverter whose switching pulses can be controlled through

external drive circuits. So output easily controllable through PWM.

Load: A CFL lamp or any common AC load (230V)

E VEHICLE BATTERY – This is 12V battery, which is charged to drive a BLDC or

Switched reluctance motors. (May be we can such devices in series to build the voltage.

Fuzzy control – This is a method of Artificial intelligence to decide the PWM pulses based

on the various constraints inside a grid related to power consumption. Eg. Temperature, lighting

conditions, user comfort etc.


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HARDWARE IMPLEMENTATION

VCC

VCC

VCC

U1

8052

31

19

18

9

12131415

12345678

3938373635343332

2122232425262728

171629301110

EA/VP

X1

X2

RESET

INT0INT1T0T1

P1.0/T2P1.1/T2XP1.2P1.3P1.4P1.5P1.6P1.7

P0.0P0.1P0.2P0.3P0.4P0.5P0.6P0.7

P2.0P2.1P2.2P2.3P2.4P2.5P2.6P2.7

RDWR

PSENALE/P

TXDRXD

U2

555alt1

2

3

4

5

6

7

8

GND

TRIGGER

OUTPUT

RESET

CONTROL

THRESHOLD

DISCHARGE

VCC

R31k

R410k

C10.1

R9

100k

1 234

MOSFET DUAL G/N23

1

4

T1

TRANSFORMER

1 5

4 8

1k

1k1k

1k1k1k

R61k

Q2BC547

12

3

K1

RELAY SPDT

35

412

Q2BC547

12

3

K1

RELAY SPDT

35

412

Q2BC547

12

3

K1

RELAY SPDT

35

412

K1

RELAY SPDT

35

412

Q2BC547

12

3

Q2BC547

12

3 Q2BC547

12

3

K1

RELAY SPDT

35

412

K1

RELAY SPDT

35

412

BT3

4V1

2

BT3

4V

12

LDR

R810k

Thermister

R810k

Switch1 2

Switch

1 2

1k 1k

11.0592Mhz

33pf

33pf

Switch

1 2

SoCEoC

D1

D2

D3

D4D5

D6

D7

D8

A0A1A2

100809

photovoltaicLoad

IN0

IN1

Grid

Fan

Step down transformer

Step down transformer converts a line voltage of 230 V into a voltage of 4.5 volts ac without

any change in the frequency. It remains unchanged as 50 Hz. The current capability that it can

withstand is about 500 mA. The voltage will be usually slightly higher than the specified voltage.

At load conditions the voltage will be the same as it has been mentioned in the transformer. The

value specified in the transformer is just the RMS value of the voltage.

Rectifier

Rectifier is of two types, as it is known already as center tapped rectifier and bridge rectifier

in the case of full rectifier. It is known that we are not going for half wave rectifier because it

will give an efficiency of only 40% approximately. Bridge rectifier needs four diodes whereas

the center tapped rectifier requires only two diodes. We have used a center tapped transformer.

Filter:

The rectified components are still Ac in nature because it never stays constant at a particular

voltage so it may be told as a varying DC or pulsating DC. So it has to be properly filtered. In


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other words we can say that the line frequency has to be eliminated from the voltage. In order to

get the pure DC we have to employ the capacitor filter. Because it is the cheapest filter available

in the market. Even we can go for inductive filter. We are not doing so because it is bulky in

nature and also by cost wise it is not compatible with capacitors,

The power supply is extracted from a step down transformer via a rectifier and 7805 voltage

regulator. +5V supply is given to all the ICs used in the project.

Temperature is sensed from a thermistor and its voltage equivalent is sent to the

COMPARATOR to input PORT ‘. Similarly light is sensed through a LDR. These voltages will

be in the order of 0 to 5V. For example when the temperature exceeds 35 deg, C comparator

sends a ‗1‘ to a pin, if light exceeds a threshold, it sends ‗1‘.

Any information or status will be displayed in 2x16 LCD via port P0. For switching on the

load based on the light and temperature, Resistor divider based control chosen by choosing a gate

resistance via relay. In the same way, if any abnormal voltage arises, the relay is switched off via

another port as shown in the circuit. Buzzer is also done as in the same way. Anything abnormal

will be informed via a buzzer alert.

The power saved in excess can be utilized to charge the batteries of the E – vehicle.

As soon the panel is exposed to light, the battery gets charged, and simultaneously the

temperature and light in lumens value is sensed through the thermistors and LDR.

Also an MPPT controller using a mosfet is realized, for which the gate pulses are applied

from IC 555 which works in astable mode.

The speed of the fan and intensity of the light is controlled through a current controller

realized by a 40W, 35 ohms resistor. Here fuzzy logic is applied based on a rule base,

programmed in 89s52. Hence this saves almost 20% of power consumption and absorbs less

power from the Grid supply. The tappings from the series resistor is controlled through the

relays activated by micro controller 89s52 via ports p2.0, p2.1, p2.3 to p2.6

V. CONCLUSION

If EVs are popularized, uncontrolled charging will bring aserious impact on the power

system. In order to reduce the impactof EVs on the power system, the EV client-side must

beappropriately managed. An optimal charging/discharging controlmodel of EVs is proposed in

this paper, which can reduce the impact of EVs on the power systems. The test results showthat

the present electricity price of the Taiwan Power Companyand battery costs are inapplicable to


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the V2G operation, butthrough the charging-only control still can save a great amountof energy

cost. With rising environmental considerations, thedevelopment of nuclear power plants and

thermal power plantswill certainly be severely challenged, and such power sources atlower

generating costs will be more and more difficult to obtain.Thus, the power companies must put

efforts in managing thedemand response. This paper implements optimal charging/discharging

Control for EVs, assuming the power company purchasesV2G energy at 1.5 times the

electricity price in peak loadhours, and the future battery costs decrease to half of the

currentlevel. In this case, V2G becomes a feasible scheme, as operationcost can be greatly

reduced for users, and the power companycan suppress the increase in peak load, thus reducing

systemlosses, and both the power company and users are benefited.

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Documents

FUZZY BASED EV CHARGING WITH REDUCED POWER … · FUZZY BASED EV CHARGING WITH REDUCED POWER FLUCTUATION UNDER RENEWABLE POWER CONSUMPTION CONSTRAINT 1Kavin.R 2Kesavan.T 3 Dr.Nand