A

Project Report

on

Grid Integration Of Distributed Energy Resources(Project Evaluation-IV)

Supervised by: Submitted by:

Mr. V.K. JAIN KRITIKA PANDYA(2010UEE196)Associate Professor CHETNA GOYAL(2010UEE211)

EKTA CHOUDHARY(2010UEE194) SAMEEKSHA GUPTA(2010UEE192)

DEPARTMENT OF ELECTRICAL ENGINEERING

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY JAIPUR

APRIL 2014

CONTENTS CHAPTER Page

no.

1. Introduction 2

2. Literature Survey 5

3. Load Flow Analysis in Power System and Genetic Algorithm 103.1 Load Flow Analysis 103.2 Genetic Algorithm 12

4. Problem Formulation 13

5. Proposed Methodology 15

6. Analysis and Observations 17

7. Results 19

8. Progress made since last evaluation 26

References 27

Appendix (A) 29

1

CHAPTER - 1

INTRODUCTION

The basic objective of an electric power system is to meet the demand imposed on it in an

optimal, secured and efficient manner. Deregulation of Electricity industry has brought in

the culture of competition among multiple players in generation, transmission and

distribution under varied constraints. Within a restructured electricity industry, the trade of

electrical energy is commoditized using an economic model that is an abstraction of the

underlying physical process. The network effects such as network losses and network

congestion, which are subject to important physical realities and limitations, have also

played an important cause for market inefficiencies

Distributedgeneration units (also called decentralized generation, dispersed generation, and

embedded generation) are small generating plants connected directly to the distribution

network or on the customer site of the meter. DG has a lot of resources, which included,

renewable-energy resources, such as the heat and light from the sun, the force of wind,

organic matter (biomass), falling water, ocean energy and geothermal heat; other resources

such as CHP, micro-hydro units, diesel engines, fuel cells and battery storage

In recent years, DG has been one of the most attractive research areas in the field of power

generation. This ever-increasing attention can be related to several important reasons such

as its great potentials and advantages, increasing of electrical demands, technical and

economic constraints in construction of new power plants and new transmission lines.

Main reasons for the increasingly widespread usage of DG can be summed up as follows

It is easier to find sites for small generators.

Latest technology has made available plants ranging in capacities from 10 KW to 15

MW.

Some technology had been perfected and are widely practiced (gas turbines, internal-

combustion engines); others are finding wider applications in recent years (wind,

solar energy), and some particularly promising technologies are currently being

2

experimented or even launched (fuel cell, solar panels integrated into buildings).

DG units are closer to customers so that Transmission and Distribution (T&D) costs

are ignored or reduced.

Combined Heat and Power (CHP) groups do not require large and expensive heat

networks.

Natural gas, often used as fuel in DG stations is distributed almost everywhere, and

stable prices are expected for it.

Usually DG plants require shorter installation times, and the investment risks are not

so high.

DG offers great values as it provides a flexible way to choose wide ranges of

combining cost and reliability.

The DG advantages can be divided into technical, economic and environmental benefits.

Technical advantages include wide ranges of benefit such as efficiency, grid reinforcement,

power loss reduction, reliability, eliminating or deferring the upgrades of the power system,

improving load factors and voltage profile and thus increased power quality. The economic

advantages entail the reducing of transmission and distribution operating cost, saving the

fossil fuel and decreasing in electricity price. Environmental advantages cover the

reductions in emission of greenhouse gases and also sound pollutions.

Moreover, DGs are available in modular units, characterized by ease of finding sites for

smaller generators, shorter construction times, and lower capital costs. Decision about DG

placement is taken depending on site and primary fuel availability or climatic

conditions. Although the installation and exploitation of DGs to solve network problems

has been debated in distribution networks, the fact is that, in most cases, the distribution

system operator (DSO) has no control or influence about DG location and size below a

certain limit. However, DG placement impacts critically the operation of the distribution

network. Inappropriate DG placement may increase system losses and network capital and

operating costs. On the contrary, optimal DG placement (ODGP) can improve network

performance in terms of voltage profile, reduce flows and system losses, and

improve power quality and reliability of supply. The DG placement problem has therefore

attracted the interest of many research efforts in the last fifteen years,hence it can provide

3

DSOs, regulators, and policy makers useful input for the derivation of incentives and

regulatory measures.

This report provides a powerful approach to find the optimal size and location of

distributed generation units in a distribution system using Genetic Optimization algorithm

(GA). The total active and reactive power losses are minimized and

voltage profile is improved. GA fitness function is introduced including the active power

losses, reactive power losses and the cumulative voltage deviation variables with selecting

weight of each variable. GA fitness function is subjected to voltage

constraints, active and reactive power losses constraints and DG size constraint.

4

CHAPTER - 2

LITERATURE SURVEY

Different researchers have proposed different methods for grid integration of Battery and

Solar photovoltaic systems to overcome the challenges imposed by high penetration of

photovoltaic in distribution grids. Different operating schemes of battery energy storage

systems have been used to address different problems caused by the highly intermittent and

dispersed PV generation. A brief overview of the literature survey is listed here.

1. Cody et al.[1] proposed the application of battery energy storage system in ramp rate

control method, frequency droop response method and power factor control to overcome the

voltage and frequency swing problem due to high penetration of photovoltaic in low voltage

grids. They also devised a method to control power factor at point of coupling by reactive

power control of the inverter.

2. Paulo et al. [2] investigated the effect of large scale integration of DERs within low

voltage networks and the problem of voltage rise. They modeled LV networks as three phase

unbalanced systems and proposed strategies to control the voltage rise by optimal

curtailment of DERs in order to avoid cascading.

3. P. S. Georgilakis and N. D. Hatziargyriou [3] focused on the various optimization

techniques which can be used to find the optimal location and size of the distributed

generation units to be installed into the existing distribution network which are subjected to

various electrical and investment constraints. To minimize the total power loss of the

system, genetic algorithm techniques are employed in this system.

4. N. W. Miller [4] et al. proposed a grid integrated photovoltaic and battery energy storage

system which would continue to supply the critical load in case of outage of the main plant.

The main plant sheds all the non-critical loads as soon as possible after the occurrence of any

fault and all the critical loads are taken up by the BESS. All the loads are transferred back to

5

the utility as soon as power is restored, but this is to be done before the battery is completely

discharged. This is known as re-synchronizing.

5. Md. Anisa Shereen[5]The proposed methodology is so fast and efficient and at the same

time so accurate in determining the size, type, number and location of DG units using a

Genetic Algorithm (GA) based technique. Load flow algorithm is combined appropriately

with GA till access to acceptable results of this operation. The suggested method is

programmed under MATLAB software. The effectiveness of the proposed methodology was

tested on Standard IEEE33 bus system and found maximum loss reduction for each of two

types of optimally placed multi-DGs. The proposed methodology is fast and efficient and at

the same time accurate in determining the size, type, number and location of DG unit(s).

6. B. Venkatesh et al. This paper focuses on the aspects of loss minimization and voltage

enhancement of RDS by artificial intelligence methods. A sample 33-bus system and 69-bus

system are chosen for the study and the results are being compared. Two algorithms namely

FMGA and EP are used. The comparison of results with both the algorithms shows that

FMGA is able to find potentially good switching options. EP has good solution but it takes a

longer time to converge.

7. A. K. Singh and S. K. Parida [7] presented a method to select the load buses for the

placement of DGs based on loss reduction and voltage improvement sensitivity of the system.

Benefits of employing DGs are analyzed using voltage performance index (VPI) and loss

sensitivity factor (LSF) on an IEEE 24 bus system. With the optimal placement of DGs, it has

been observed that the line flows in the system become evenly distributed.

.

8. N. Miller et al. [8] proposed a paper which discusses about the use of energy storage in

smart grid and the advancement of power grid to accommodate the increasing penetration of

the variable distributed energy resources. It provides the basics involved in determining the

power and energy rating of a battery at a defined discharge rate.

9. W. S. Tan et al. [9] carried out a novel multi population-based genetic algorithm for

optimal location and sizing of distributed generation in a radial distribution system. The

objective is to minimize the total real power losses in the system and improve voltage

6

stability within the voltage constrains. It was proven that the proposed method can save a

huge amount of power and achieve significant improvement in voltage stability. Results of

proposed method were verified by compared to the results from the analytical method

10. Chhabra et al.[10]demonstrated the use of energy storage devices as a solution to reduce

the frequency variations due to the supply fluctuations of renewable energy sources. Also a

simple dispatch strategy is provided, for operation of a wind farm coupled with a utility scale

battery.

11. K. Wang, et al [11] Low proposed a theory to match the uncertain supply and demand by

designing a stochastic network to deal with the fluctuations in both the power supply and

demand.It was motivated by the ability of the calculus to account for high fluctuations in

queuing systems, which are especially characteristic to the power grid when integrating

renewable energy sources.

12. FrancescoMarra et al. [12] proposed a solution to cope with the increased voltage profile

due high penetration of PV and reduced demand. To cope with this problem battery energy

storage system is used and then integrated with solar photovoltaic. The result in improved

voltage profile and reduced power curtailment tendencies.

13. U. EMINOGLU et al. [13] proposed a software package to be used for load flow analysis

using Graphical User Interface capabilities of matlab. This system when implemented on a

transmission and distribution network gives quite accurate results. It facilitates the use of

different algorithms for load flow analysis.

14. SumitPaudyalet. al. [14] proposed a generic and comprehensive distribution appropriate

power flow (DOPF) model that can be used by local distribution companies (LDCs) to

integrate their distribution system feeders into a Smart Grid. The results illustrate the benefits

of the proposed DOPF in terms of reducing energy losses while limiting the number of

switching operations.

15. WenpingQinet. al. [15] proposed a technique to evaluate the weakest bus and system

reliability based on system voltage stability under various system contingencies and operation

conditions. The IEEE 30-bus system has been modified and analyzed to illustrate the

7

proposed technique. The results provide system planners and operators very important

information for the weakest points at different contingencies.

16. Al-Mathnaniet. al. [16] proposed a new control algorithm for dynamic voltage restorer

(DVR) that mitigates voltage sag by low active power injection with short time delay. The

difference between the reference and measured voltage is injected to the line by the DVR. It

is shown that the proposed method improves the performance of the DVR.

17. ErhanDemiroket. al. [17] proposed a method which aims to achieve less grid voltage

violations, thus more DG connection on the electricity distribution networks can be allowed.

In case of high penetration of grid connected DGs, voltage support from the inverters near

transformer is almost invisible because the terminal voltage stays in admissible range

although the voltage at the end of feeder exceeds 1.1 p.u. limit.

18. John P. Barton and David G. Infield [18] proposed a probabilistic method has been

developed to predict the ability of intermittent energy sources increase the penetration of

intermittent embedded renewable generation on electricity grids. Different electricity storage

are compared for their suitability for different time periods.

19. Mohamed M. Aly et al. [19] presented a study of the voltage stability problem in

distribution systems with high penetration photovoltaic generations that are equipped with

reactive power support capabilities. A series of case studies is conducted and the calculated

results show that reactive power support can positively contribute to feeder voltage

regulation.

20. QiuyeSunet. al. [20] proposed a system for the voltage profile of the distribution networks

according to two typical load distribution models - uniform and isosceles models. The exact

expressions of voltage along the feeder are established. The simulation proves that DG can

influence the voltage profile and should be well planned to ensure the static voltage of each

node within the permitted range.

21. Kyung-Hee Jung et al [21] proposed a study for determining the installation site and the

optimal capacity of the battery energy storage system (BESS) for load leveling. Using lead-

8

acid batteries calculation of optimal capacity with the charging/discharging loads is also done

in this paper.

22. Pavlos S. Georgilaki and Nikos D. Hatziargyriou,[22] focused on several models and

methods have been suggested for the solution of the ODGP problem. This paper presents an

overview of the state of the art models and methods applied to the ODGP problem, analyzing

and classifying current and future research trends in this field. The most frequently used

techniques for the solution of the ODGP problem are the genetic algorithm and various

practical heuristic algorithms.

23. Gopiya Naik S. et al.[23] presents a method based on analytical approach for real power

loss minimization of distribution networks by injecting power by the Distributed

Generation(DG) operating at a given power factors. This method does not require calculation

of Zbus matrix, inverse of Ybus matrix or Jacobean matrix as well as requires less

computation time. Comparison of results for loss reduction with other reported methods

shows the effectiveness of the proposed method. The proposed method can be extended for

the allocation of multiple DG units having of types and characteristic considering different

load level is the scope for the future work.

9

CHAPTER - 3

LOAD FLOW ANALYSIS IN POWER SYSTEM & GENETIC ALGORITHM

3.1. LOAD FLOW ANALYSIS

The power flow equation being non – linear must be solved by iterative techniques. Power

flow studies, commonly, referred to as load flow, are the backbone of power system analysis

and design. They are necessary for planning, operation, economic scheduling and exchange

of power between utilities.

The bus admittance matrix of the node voltage equation is formulated, and a MATLAB

function named ybus is developed for the systematic formation of the bus admittance matrix.

Next Newton Raphson method for the solution of nonlinear algebraic equation is discussed.

Hence for an n- bus system the node voltage equation in matrix form is:-

Ibus = YbusVbus

where,Ibus = the vector of the injected bus currents (i.e. external current sources)

Ybus = Bus admittance matrix

Vbus = vector of bus voltages measure from the reference node (i.e. node voltages)

10

11

Fig 3.1: Flowchart for Newton Raphson Load flow Algorithm

3.2. GENETIC ALGORITHM:

Optimization is the selection of a best element (with regard to some criteria) from some set of

available alternatives. In the simplest case, an optimization problem consists of maximizing

or minimizing a real function by systematically choosing input values from within an allowed

set and computing the value of the function. The generalization of optimization theory and

techniques to other formulations comprises a large area of applied mathematics. More

generally, optimization includes finding "best available" values of some objective function

given a defined domain (or a set of constraints), including a variety of different types of

objective functions and different types of domains.

The solving of placement and sizing of DG units problem requires to define the Fitness

Function that can be optimized in the presence of some constraints. The fitness function is

selected for reducing power losses and increasing of voltage stability margin in the system or

reducing cumulative voltage deviation. GA starts the process by automatically proposing

different DG sizes within the proposed DG size limits and internally executes the load flow

program which is properly linked with GA package till the minimum solution is obtained for

the suggested location. This process is repeated for each of the proposed locations. Different

scenarios are proposed to consider multiple of DG’s at different suggested locations and to

consider both DG PV and PQ models to determine the best location and size of the DG’s. The

suggested algorithm is programmed under MATLAB software. Comparison between the

proposed GA approach and the traditional method is conducted.

A precise evaluation for the Objective Function has been selected. The main goal of the

proposed algorithm is to determine the best locations and size for new Distributed Generation

resources by minimizing different function, related to project aims. Two main goals are taken

into considerations to determine the Objective Formula that is used in point of start: Power

Losses reduction and voltage profile improvement.

The active and reactive power losses are obtained from load flow program. The cumulative

voltage deviation norm is defined as “the normalized sum of the deviations of the obtained

value from the desired value at every node on the feeder. The desired value being 1.0 p.u and

the obtained value being the value obtained from the three-phase distribution power flow.

12

CHAPTER - 4

PROBLEM FORMULATION

Considering the different locations on which the storage system can be integrated, it is

possible to calculate the storage power that lowers the nodal voltages below the limit. The

main problem is to identify the minimum storage power Ps that meets the voltage target,

according to the distributed concepts. This can be implemented with the storage participation

in different locations in the grid, or with multiple storage participation.

The proposed solution is a linear problem. This algorithm is implemented in Matlab with load

flow simulations. The algorithm can be summarized as follows:-

1) Enter the LV grid model of IEEE 33 bus system under study

2) Run the load flow grid simulation

3) Identify the critical node/s where the voltage is above Vmax

4) Add storage system at one critical location.

5) Solve the objective function for Ps which is to find the minimum power that keeps the

critical voltage below Vmax

6) If V >Vmax update Ps using Ps = Ps + ΔPs and go back to step 5

7) Go back to step 4, set another storage configuration, repeat step 5 and 6.

The step 7 is repeated until all possible combinations of storage locations are covered,

including the cases of multiple storages at the different nodes.

The Fitness Function for the application of Genetic Algorithm is determined as following:

PL : Active Power Loss

QL : Reactive Power Loss

CVD: Cumulative Voltage Deviation

F: Fitness Function.

13

Where:

N: The total number of nodes

Wp , Wq and Wv : The Objective Function weights (Active, Reactive power losses and

Cumulative Voltage Deviation weights), subjected to:

Wp + Wq + Wv = 1

Where; Wp = 0.35, Wq =0.1, Wv = 0.55

The main constraints in the optimization process in the proposed methodology are:

1. Active and reactive power losses constraint:

The losses after installing DG in power grid should be less than or equal losses before

installing DGs .

PL with DG ≤ PL without DG

QL with DG ≤ QL without DG

2. DG Size constraint:

To obtain a reasonable and economic solution, the size of generators should not be so small

or so high with respect to load value. The DG size is considered not more than 20% of the

load as following:

DGZ <= 20%L

where, L : Load value

DGZ : DG size

14

CHAPTER - 5

PROPOSED METHODOLOGY

1. The NRLF program is used for this load flow simulation.

V>Vmax No No V<Vmin

Yes

2. Genetic Algorithm is used to optimize the location and size of the DG

units in the next flowchart

15

Start

End

Run Load Flow Simulation for IEEE 33 bus system

Insert storage on location

Identify storage power Ps

Find critical node with voltage below Vmin

Ps = Ps + ΔPs

Vmin<V<Vmax

Ps = Ps - ΔPs

16

YesNo

CHAPTER - 6

ANALYSIS AND OBSERVATIONS

Newton Raphson Load Flow analysis was carried out on IEEE 33 bus test system, .

The load flow program was developed in Matlab.

The IEEE 33 bus test system has one slack bus, five generator buses, and twenty-four

load buses. Accordingly, we obtained the admittance matrix, Jacobian matrix,

mismatch and correction vectors.

Ybus matrix (33x33)

Yik = Negative of total admittance connected between ith&kth bus

Yii = Sum of the admittance directly connected to ith bus

Ybus=G+iB , where G and B are conductance and susceptance matrix.

Jacobian Matrix, J (64x64) given as [J1 J2; J3 J4]

J1 (32x32)

J2 (32x32)

J3 (32x32)

J4 (32x32)

J1 = ∂Pi/∂δj , where i ϵ PQ and PV buses, j ϵ PQ and PV buses

When i=j

∂Pi/∂δj = -Qi-(Vi^2)*Bii

When i≠j

∂Pi/∂δj = │ViVj│(Gijsin(δi-δj)-Bijcos(δi-δj))

J2=∂Pi/Vj, where i ϵ PQ and PV buses, j ϵ PQ buses

When i=j

∂Pi/Vj= (Vi^2)*Gii+Pi

When i≠j

∂Pi/Vj=Vi*(Gijcos(δi-δj)+Bijsin(δi-δj))

17

Load Initial system data

Run Load flow program

Register the base case

Is M<N

Generate Population

Crossover

If minimum of fitness < best value then store minimum function into

best value else proceed further

Store minimum of fitness function into

best value

Selection

Set no of iterations = N

Calculate fitness function

Mutation

Set iteration count M=0

M = M+1

Calculate fitness function

STOP

J3=∂Qi/∂δj, where i ϵ PQ buses, j ϵ PQ and PV buses

When i=j

∂Qi/∂δj = Pi-(Vi^2)*Gii

When i≠j

∂Qi/∂δj = │ViVj│(-Gijcos(δi-δj)-Bijsin(δi-δj))

J4=∂Qi/∂Vj, where i ϵ PQ buses, j ϵ PQ buses

When i=j

∂Qi/∂Vj =Qi-(Vi^2)*Bii

When i≠j

∂Qi/∂Vj = Vi*(Gijsin(δi-δj)-Bijcos(δi-δj))

Mismatch vector of dimensions 64x1 was obtained. Tolerance of the system

has been taken to be 0.0001.

After NRLF,various combinations of equal sized DG are tried out and the best

possible DG location is determined first by traditional method, then by using

optimization technique.

The optimization technique used is genetic algorithm where the total sum of

DG sizes is 20% and hence the voltage profile is improved in addition to

reducing the losses.

18

CHAPTER - 7

RESULTS

7.1 Base Case (When DG is not used)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus No

Per Un

it Volta

ge

Per Unit Voltage Variation when DG is not used

Fig.7.1: Bus Voltage Variation in per unit when DG is not used.

There are 21 buses with voltage less than .95 pu. The real power loss is 202.7 kW and reactive power loss is 135.2 kW. The minimum voltage is .9131 per unit and it is obtained at bus no. 18

The fitness function in this case is 0.9200

7.2 Traditional Method

We assume certain combinations of DG to be placed at the points where voltage is the least. Performance of the system in terms of fitness function, voltage profile, real and reactive power loss is studied. An approximate idea about the optimal location of DG is obtained.

The total DG penetration in the system is kept at 20% of system load. IEEE 33 bus system has total system load of 3715 kW. Hence sum of all individual DG units should not exceed 20% of 3715 kW i.e. 743 kW. In traditional method, we assume the size of all DG sources to be equal..

A. When two equal DG sources are placed in the system.

19

We do not place one single DG as in that case its size would be too large to be realistic. Hence we directly study the cases of putting 2 equal sized DG in the system.

The best most critical buses are identified as 18,17,16,33,32. Out of these the best result is obtained when two DG of 371.5 kW are put at buses 18 and 33.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus No.

Per Un

it Volta

ge

Per unit voltage variation when two equal DG are used at two most critical locations at 18th and 33rd bus (Traditional Method)

B. When three equal DG sources are placed in the system

The best most critical buses are identified as 18,17,16,33,32. Out of these the best result is obtained when three equal DG of 247.6 kW are put at buses 18,17 and 16.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus No.

Per U

nit V

oltage

Per Unit Voltage variation when three equal DG are used at 18,17,16th bus (Traditional Case)

20

C. When four equal DG sources are placed in the system

The best most critical buses are identified as 18,17,16,33,32. Out of these the best result is obtained when three equal DG of 185.7 kW are put at buses 18,17 ,16 and 33.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus No.

Per U

nit Vo

ltage

Per Unit Voltage variation when four equaL DG are used at four most critical points (Traditional Case)

D. When four equal DG sources are placed in the system

Here equally sized DG of 148.6 kW are placed at buses 32,33,18,17,16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330.93

0.94

0.95

0.96

0.97

0.98

0.99

1Per Unit Voltage variation when five equal DG are used at buses 18,17,16,32,33 (Traditional Method)

Bus No.

Per Un

it Volta

ge

21

Comparision of all traditional methods is done in table below. It shows that the most appropriate case is to apply is that of four DG of equal size at bus numbers 18,17,16,33.

No. of DG

placed

Bus No. DG size

(kW)

Fitness function

value

Ploss(kW)

Qloss(kVAr)

Min Voltage

(pu)0 - - .9200 202.7 135.2 .9131

2 18,33 371.5 0.5114 128.2 84.7 .9391

3 18,17,16 247.6 0.2780 140.7 94.1 .9281

4 18,17,16,33 185.8 0.2600 130.8 86.3 .9339

5 17,18,16,32,33 148.6 0.5049 127.8 84 .9371

7.3 Using Genetic Algorithm

The traditional method gave best results when DG was used at four positions. Hence we apply genetic algorithm technique to find the best DG sizing and location when at most four DG sources are used and sum of their capacity should not exceed 20% of load i.e. 743 kW.

For a population size of 500 individuals, and for 500 iterations or number of generations, the least fitness function value is obtained as 0.2560 when DG of size .209kW, 61.6 kW, 230.8 kW and 240.3 kW are placed at buses 33,18,14 and 17 respectively.

In this case the real power loss is 128.4 kW and reactive power loss is 84.5 kW. There are six buses with voltage below 0.95 per unit and the least bus per unit voltage is 0.9348. This is a considerable improvement wrt the voltage profile in base case when 21 buses had voltage lesser than 1 per unit. The curve below shows the comparison between voltage in base case and the voltage profile obtained in most optimal case.

22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Bus No.

Per un

it voltag

e

Per unit volatge variation in base case and with DG used at optimal locations as per genetic algorithm results

When optimal size and location of DG isdecided by GA

When DG is absent

Table 1: Bus Voltage Comparison in base case and optimal DG placement case

Bus No.

Voltage in absence of DG

Voltage in optimal DG placement case

1 1 12 0.997032244 0.9975164273 0.982936497 0.9860113814 0.975453378 0.9804455685 0.968056148 0.9750450116 0.949654948 0.9610166417 0.946169312 0.9582837748 0.941324503 0.9560479799 0.935055797 0.95356610510 0.929240751 0.95157996311 0.928380175 0.95143385812 0.926870722 0.95128162513 0.920757681 0.95051061214 0.918490892 0.95021822615 0.917078742 0.95002842116 0.915711 0.95018980917 0.913683858 0.95079415418 0.913076809 0.95050700319 0.99650388 0.99698832120 0.992926207 0.99341240421 0.992221704 0.99270824522 0.991584285 0.992071139

23

23 0.979350457 0.98243678924 0.97267925 0.97578691225 0.969346763 0.97246511326 0.947725048 0.95940004427 0.945161291 0.95727336128 0.933721593 0.94750327829 0.925502951 0.94054955430 0.921945936 0.93776435731 0.917784604 0.93504396332 0.916869178 0.93457974933 0.916585492 0.934778128

Over the 500 generations, the variation of minimum value fitness function for every generation is as shown :

0 50 100 150 200 250 300 350 400 450 5000.255

0.256

0.257

0.258

0.259

0.26

0.261

0.262

0.263

0.264

0.265

0.266

0.267

0.268

0.269

0.27

0.271

0.272

0.273

0.274

0.275

0.276

0.277

0.278

0.279

0.28

Generation No.

Fitnes

s Func

tion Va

lue

Fitness Function variation over the generations

24

The real and reactive power loss in base case and optimal DG placement case is analysed as under :

Real Power Loss (kW)

Reactive Power Loss

(kVAr)

0

50

100

150

200

250

Without DG PlacementWith DG Placement

Comparison between Power Loss

Pow

er L

oss

Table 2 : Comparison of losses between base case and optimal DG placement case

Real Power Loss Reactive Power Loss

Power Loss without DG (kW) 202.7 135.2

Power Loss with optimal DG Placement (kVar)

128.9 84.8

Percentage Reduction 36.4% 37.55%

25

CHAPTER – 8

Progress made since last Evaluation:

1. We have continued with our literature survey.

2. After applying Newton Raphson load flow analysis on IEEE 33 bus system we have devised a program to find out the optimum positions to install 4 distributed generators in the bus system subject to various constraints.

3. The optimization method used for the above analysis is genetic optimization algorithm(GA). This is done using programming in MATLAB.

26

REFERENCES

[1] Cody A. Hill ,Matthew Clayton Such ,Dongmei Chen ,Juan Gonzalez and W.MackGrady,”Battery Energy Storage for Enabling Integration ofDistributed Solar Power Generation”,IEEE transactions on smart grid, VOL. 3, NO. 2, JUNE 2012. (Base Paper).

[2] Paulo D. F. Ferreira, Pedro M. S. Carvalho, Luis A. . F. M. Ferreira, and Marija D. Ilic,’’ Distributed Energy Resources Integration Challenges in Low-Voltage Networks: Voltage Control Limitations and Risk of Cascading’’, IEEE transactions on sustainable energy, vol. 4, No.1,January 2013.

[3] P. S. Georgilakis and N. D. Hatziargyriou ,” Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research”, IEEE Transactions on power systems, Vol. 28,No. 3, August 2013.

[4] N. W. Miller, R. S. Zrebiec, R. W. Delmerico, and G. Hunt, “Design and commissioning of a 5 MVA, 2.5 MWh battery energy storage,” in Proc. 1996 IEEE Power Eng. Soc. Transm. Distrib. Conf., pp. 339–345.

[5] Md. Anisa Shereen,“ Optimal Allocation of DG Units for Radial Distribution Systems using Genetic Algorithm ”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-6, August 2012

[6] B. Venkatesh, S. Chandramohan, N. Kayalvizhi and R.P. Kumudini Devi, “Optimal Configuration of Radial Distribution System Using Artificial Intelligence Methods”, TIC-STH 2009

[7] A. K. Singh and S. K. Parida, “Selection of Load Buses for DG placement Based on Loss Reduction and Voltage Improvement Sensitivity” ,Proceedings of the 2011 International Conference on Power Engineering, Energy and Electrical Drives, Torremolinos (Málaga), Spain. May 2011.

[8] Miller, N., GE Energy's Energy Applic. & Syst. Eng. Group, Schenectady, NY, USA ; Manz, D. ; Roedel, J. ; Marken, P. Kronbeck, E.''Utility scale Battery Energy Storage Systems'',GE Energy's Energy Applications & System Engineering Group, Schenectady, NY, USA,IEEE 2010.

[9] W. S. Tan, M. Y. Hassan and M. S. Majid, “ Multi Population Genetic Algorithm for Allocation and Sizing of Distributed Generation ”, Optimization Conference (PEOCO2012), Melaka, Malaysia: 6-7 June 2012.

[10] XingbinYu,ChananSingh,”Apracticalapproachforintegratedpowersystem vulnerability analysis with protection failures”, IEEE Transactions on Power Systems, Vol. 19, No. 4,November 2004.

[11] K. Wang,F.Ciucu, C. Lin and S.H. Low, “A Stochastic Power Network Calculus for Integrating Renewable Energy Sources into the Power Grid”, IEEE 2012.

27

[12] Francesco Marra, Y. TarekFawzy, Thorsten Bülo and BoštjanBlažič,” of Photovoltaic Energy Storage Options for Voltage Support in Low-Voltage Grids with High Penetration ” IEEE Trans. Vol.3 PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin.

[13] U. Eminoglu, T. Gozel and M.H. Hocaoglu, “DSPFAP: Distribution Systems Power FlowAnalysis Package UsingMatlab Graphical User Interface (GUI), ”Received 17 June 2007; accepted 5 October 2007.

[14] SumitPaudyal, Claudio A. Cañizares, and Kankar Bhattacharya, “Optimal Operation of Distribution Feeders in Smart Grids”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 58, NO. 10, OCTOBER 2011.

[15] Wenping Qin, Wei zhang, Peng Wang, Xiaoqing Han, “Power System Reliability Based on Voltage Weakest Bus Identification”, Power and Energy Society General Meeting, 2011 IEEE.

[16] Ali O Al-Mathnani, M A Hannan, Majid Al-Dabbagh,MohdAlauddinMohd Ali and Azah Mohamed, “Development of New Control Strategy for Voltage sag Mitigation,” 2nd IEEE International Conference on Power and Energy (PECon 08), December 1-3, 2008.

[17] ErhanDemirok, Dezso Sera, Pedro Rodriguez, Remus Teodorescu,“Enhanced Local Grid Voltage Support Method for High Penetration of Distributed Generators”, IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society.

[18] John P. Barton and David G. Infield,’’Energy Storage and Its Use With Intermittent Renewable Energy , ” IEEE Transaction on energy conversion, vol19, no.2, june12.

[19] Mohamed M. Aly ,Mamdouh Abdel-Akher , ZakariaZiadi and TomonoboSenjyu “Voltage Stability Assessment of Photovoltaic Energy Systems with Voltage Control Capabilities”,Electrical Engineering Department Faculty of Engineering, Aswan UniversityAswan 815452, Egypt.

[20] Qiuye Sun, Zhongxu Li, Huaguang Zhang, “Impact ofDistributed Generation on Voltage Profile in Distribution System”, 2009 InternationalJoint Conference onComputational Sciences and Optimization.

[21.] Kyung-Hee Jung, Hoyong Kim, Daeseok Rho, “Determination of the installation site and optimal capacity of the battery energy storage system for load leveling”, IEEE Transactions on Energy Conversion, Vol. 11, No. 1, March 1996

[22] N.Z. Mohd Ali, I. Musirin, Z. Khalidin, M.R. Ahmad, “Distributed Generation Sizing and Placement using Computational Intelligence”,2012 IEEE International Power Engineering and Optimization Conference (PEOCO2012), Melaka, Malaysia: 6-7 June 2012.

[23] Gopiya Naik., D. K. Khatod , M. P. Sharma, “Sizing and Siting of Distributed Generation in Distribution Networks for Real Power Loss Minimization using Analytical Approach”, International Conference on Power, Energy and Control (ICPEC), 2013.

28

APPENDIX (A)

I. LINE DIAGRAM OF IEEE 33 BUS SYSTEM

29

II. SYSTEM DATA FOR IEEE 30 BUS SYSTEM

Bus Data

30

Branch Data

31