OPTIMAL SITING AND SIZING OF DISTRIBUTED GENERATION (DG) USING GENETIC ALGORITHM · PDF file · 2016-11-15optimal siting and sizing of distributed generation (dg) using genetic...

OPTIMAL SITING AND SIZING OF DISTRIBUTED GENERATION

(DG) USING GENETIC ALGORITHM (GA)

K. M. C. Marcelino, REE, MSEE, MBA

5/F Technical Services Building (TSB), Ortigas Ave., Ortigas Centre, Brgy. Ugong, Pasig City 1065

[email protected]

ABSTRACT

This paper presents an approach in searching for the optimal allocation (size) and location of Distributed

Generation (DG) for active power loss reduction and voltage profile improvement of the system using Genetic

Algorithm (GA) as a search method. GA will be applied in an existing 115 kV/34.5 kV distribution system of Manila Electric Company (MERALCO). DG was also introduced as an alternative to which will help in lessen

the costs of expansion of the distribution system (CAPEX projects), ease feeder and delivery point power

transformer loading, reduce technical losses, improve power system security, and introduce renewable energy as

a source of power for distribution networks, etc. GA has proven in previous studies that GA can be applied in any

type of situation and this study presents that DG has significant effect in the loss and voltage improvement of the

distribution system.

KEYWORDS: Distributed Generation (DG), Genetic Algorithm (GA), Active Power Loss, CIP2 Substation

INTRODUCTION

The development of Distributed Generation (DG) in the country is considered an essential part of the power

system. DG allows electric supply coming from the large power systems or the location of the load center to be

isolated from the rest with economic viability of building power lines. As we all know, DG can source its power

from fuel cells, diesel, and renewable energy---such as wind turbines, hydro turbines and photovoltaic (PV)

modules, among others. The distributed power generation or DG can be located anywhere as long as there is fuel

available and there is a load that consumes. Using DG has many benefits such as low technical losses and voltage

support, among others. DGs are small-scale power generation technologies used to provide an alternative to or an

improvement of traditional power systems. These technologies includes a wide range of energy sources such as

wind, solar, biomass, and storage. Using DG lessens a country’s greenhouse gases that can affect the environment.

DG will be able to reduce the project costs or capital expenditures (CAPEX) of the DU where in modifications

can be deferred at a later date.

Distribution Utilities (DU) are currently experiencing low electric power supply due to the depletion of

electric power supply generation and transmission constraints, such as low water levels for large hydro power

generation and high fuel costs for operating oil-based generators that resulted in lower available generation

capacity. DUs or ECs operates and maintains a large and sometimes complex distribution network or system or

grid, which is a part or portion of a large power system. It is in the distribution system where loads or consumers

are usually found. The distribution system is commonly operated at a voltage level of 13.2/13.8 kV for Electric

Cooperatives (EC) and/or DU located in the provinces while 34.5 kV for Metro Manila. Commonly, distribution

systems are radial networks, meaning the system is are not interconnected. The Distribution Development Plan

(DDP) made by planning engineers for a DU or an EC provides for an orderly and timely modification to the

distribution network to cope with the system. The costs of building projects includes large amount of money that will be used to provide customers more reliable, flexible and efficient power systems. DG systems are introduced

in this paper to find an alternative which will help in lessen the costs of expansion, ease feeder loading, reduce

technical losses, and introduce the use of renewable energy as a source of power for distribution networks, etc.

However, this study is focused on reducing system technical losses. Technical losses are the main part or the

major part of systems loss component. Reducing technical losses means having more power available, increasing

the energy supply of the DU or having more efficient distribution system. This means that by reducing technical

losses with the same amount of energy supply, the DU can serve more customers or consumers.

Genetic Algorithm (GA) is an optimization method that is biologically inspired. It is a stochastic global

search method that mimics the metaphor of natural evolution in order to solve problems and to model evolutionary

systems. GA uses biologically inspired techniques such as genetic inheritance, natural selection, mutation, and

sexual reproduction (recombination or crossover). Naturally, all living organisms or individuals in the world compete for resources such as food, water and shelter and most importantly for attracting mates. Those who are

able to survive and attract a mate will have relatively larger numbers of offspring and those who poorly perform

will produce fewer offspring or even no offspring at all. This means that the genes from the “fittest” will spread

from generation to generation. These are based on Charles Darwin’s “survival of the fittest” in the book of The

Origin of Species.

This study is all about finding optimal allocation and location of DG and to prove whether DG affects the voltage profile and technical losses of the system.

This study provides information that will help engineers in implementing DG as an alternative source of

power with the benefits of reducing costs and threat of vulnerability to terrorism and improving power system

security. Furthermore, this study will give other researchers ideas on how to implement DGs in distribution

systems that will benefit both customers and the utility using the proposed algorithm. The proposed algorithm can

be an aid to other researchers in which it can also be applied to other applications, such as applications in optimal

location and size of capacitors in the distribution system and others alike.

This study will focus on the optimal location and allocation (size) of the DG technology considering the

impacts on the 115 kV sub-transmission system of Manila Electric Company (MERALCO) in Laguna using GA.

GA will be responsible in searching for the best location and size of the DG. This paper will use the existing MERALCO 115 kV sub-transmission system as a test system for simulating the proposed algorithm and will be

programmed using MATLAB.

METHODOLOGY

The operating condition of a distribution systems drastically changes upon the connection of the DG

compared to a system without DG. Planning the installation of DGs should therefore be considered such as the

maximum number of DGs that can be connected into the system and at what capacities, where these DG units

should be installed, what technology suits best, and what connection type should be used, among others.

The problem of DG allocation and location should be approached with caution. If DG units are connected

at non-optimal locations, the system losses may tend to increase while the total system loss considering the point

of view of the grid or system operator (SO) may decrease. Interconnecting a non-optimal DG size at a non-optimal

or optimal location, the system losses may also increase. Previous studies have also shown that connecting DG at

non-optimal locations increases the system’s technical loss and therefore increases the cost of electricity (Kotb,

Shebl, Khazendar, & Husseiny, 2010). In MERALCO’s case, the interconnection of Alternergy Wind One Corp.’s

(AWOC) into MERALCO’s 115 kV system, MERALCO’s system loss increased while the whole or Luzon Grid

system loss decreased.

Of all benefits and objectives of implementing DG, the idea of implementing DG for loss reduction needs

special attention.

Problem Formulation

Solving the placement and allocation of DG units requires the Fitness Function (FF) or an Objective

Function wherein this fitness function will be optimized where the presence of some constraints are needed in

order to define the optimal location and size of the DG. The FF is selected to reduce the system power losses and

increase the voltage stability of the system.

Different scenarios are used in this study to determine the effectiveness of the proposed algorithm (i.e. peak

and off-peak conditions and DG sizes). These scenarios will all be applied with the proposed GA algorithm. GA

starts by automatically proposing different DG sizes and locations within the defined DG size and location limit

then internally executes the load flow program, using Power System Analysis Toolbox (PSAT), which is properly linked with the GA toolbox or package until the minimum solution is obtained for the suggested location and size.

The simulation process will be repeatedly executed until all the solutions and limits are met. The GA algorithm

will end when at least one of the conditions is met (Nallagownden, Thin, Guan, & Mahmud, 2006)

Generations – the algorithm stops when it reaches the specified maximum number of generations.

Time – the algorithm runs for the specified amount of time in seconds.

Fitness Limit – the algorithm stops when the fittest value in the current generation is less than or equal

to the specified fitness limit.

Stall Generations – the algorithm stops when there is no improvement in the fitness function value for a

certain number of generations.

Stall Time Limit – the algorithm stops when there is no improvement in the fitness function for a certain

period of time in seconds.

The suggested algorithm is programed under MATLAB software using both Power System Analysis Toolbox (PSAT) and Genetic Algorithm Toolbox (GA) and will be applied in an existing MERALCO 115 kV-34.5 kV

distribution system in Laguna.

Objective Function

The objective function or the fitness function is the function that needs to be minimized under various

constraints. The main objective of the proposed algorithm is to determine the best locations and sizes for new DG

resources by minimizing active power losses and improving the voltage profile of the system. According to

Pisisca, Bulac, and Eremia (2009) the objective or fitness function in order to minimize transmission loss is shown

as follows:

Minimizing the active power losses:

kji

ji PP,

1 )(

n

j

jiji

ji

jiij

jiji

ji

jiijn

i

QPPQVV

RQQPP

VV

R

11

sincos

where n is the number of buses, ijR is the resistance of the line between buses “i” and “j”, iP and iQ are net

real and reactive power injections in bus “i” and iiV , are the voltage magnitude and angle at bus “i”.

The active power losses are obtained from a load flow program. Further, the main function is subject to

operation constraints, such as the maximum and minimum power the DG can produce, the location of the DG,

etc. These limits are shown below:

DiiDGi PPP ,

n

k

kiikkiikki BGUU1

)sin()cos(

n

k

kiikkiikki BGUU1

)sin()cos(

maxii SS

maxmin iinomi UUU

maxDGDG PP

In addition to the capacity limits of the DG, the following constraints shall be considered in this study to

derive an optimal condition with consideration of system reliability. According to Abou El Ela, Zein El-Din and

Spea in 2006, these constraints or limits that must be followed are as follows:

Reliability Constraint

lossD

N

i

N

j

DGinj PPPPg DG

j

1 1

where injP is the injected power coming from the conventional power generation or delivery point substation at

bus i, jDGP is the power being generated by the DG at bus j, GN is the total number of delivery point buses,

and DGN is the total number of DG buses.

Upper and lower voltage limits

VVV ni

Figure No. 1 Genetic Algorithm Method

In order to assess the performance of the proposed algorithm, this paper used MATLAB as the computing

software to solve for the best location and allocation of the DG. GA Toolbox was used to determine the best

location and allocation of DG within the 115 kV sub-transmission of the MERALCO in Laguna. The power flow

analysis was implemented using PSAT (Milano, 2005). It is an open-source MATLAB Toolbox developed by Dr. Federico Milano; it has the capability to perform power flow analysis and other power system studies like optimal

power flow.

System Description

Manila Electric Company (MERALCO) was established in 1903 as Manila Electric Railroad and Light

Company to provide electric light and power and an electric street railway system to Manila and its suburbs

(MERALCO, 2016). It is a private distribution utility (DU) which holds a congressional franchise under Republic

Act or RA No. 9209 effective June 28, 2003, which grants MERALCO a 25-year franchise valid through June 28,

2028 to construct, operate, and maintain the electric distribution systems in the municipalities and cities of

Bulacan, Cavite, Metro Manila, and Rizal and certain cities, municipalities and barangays in the provinces of

Batangas, Laguna, Pampanga, and Quezon. MERALCO’s head office is located at Lopez Building, Ortigas Avenue, Pasig City.

As of Dec. 31, 2015, MERALCO has a total of ten (10) network sector offices, thrity (30) office branches

and seven (7) auxilliary offices, fourteen (14) extension offices, one (1) payment office and one (1) customer

assistance office aside from the head office in Ortigas Centre. Also, the company’s network facilities consists of

110 substations with a total capacity of 16,642 MVA (delivery point substations and distribution substations), 855

circuits with a total of 21,825 circuit-kilometers (sub-transmission and distribution) and 172,579 distribution

transformers wherein MERALCO’s distribution system in Laguna consists of three (3) delivery point substations,

thirteen (13) distribution susbtations, seven (7) customer-owned substations (6-load and 1-power plant) in looped

configuration. Figure No. 2 shows MERALCO’s franchise are and distribution facilities.

Figure No. 2 MERALCO Franchise Map and Distribution Facilities

RESULTS

In order to assess the proposed algorithm in solving the DG allocation and location problem, the method is applied to an actual system of MERALCO with 24 bus, 115 kV with 13.8 kV radial distribution system connected

wherein three (3) cases were shown. The initial system consists of 24 loads with a total peak load of 900 MW and

an off-peak load of 246 MW with a total active power loss of 5.45 MW and 0.75 MW, respectively, and total

reactive power loss of 130.24 MVAR (peak) and 20.97 MVAR (off-peak) using the actual loading of substations

obtained from SCADA. The slack bus is set at Biñan, Sta. Rosa and Calauan delivery point substations. Lastly,

MVA base is set at 100 MVA and maximum DG output is set at 100 MW for Case A, 200 MW for Case B and

293 MW for Case C (343 MVA x 0.95 p.f. x 90%).

Prior to the load flow analysis of the system, the number of populations and generations must be initially

set to determine whether these populations and generations can have an effect in finding the optimal size and

location. Tests were conducted to verify whether the statements are true or false. As described and in the example

by R. L. Haupt and S. E. Haupt in 2004, an initial of eight (8) population size and recommended generations of twenty (20) were used. Based on these settings, finding the optimal location and size is difficult due to the low

population size which the GA searches for the best population available and creates a new set of generation or

children. Increasing the population size to fourteen (14) and generation still set at twenty (20), finding the best

result increases as compared to having an initial population size of only eight (8). Further, increasing the

population size to twenty (20), as suggested and described by R. L. Haupt and S. E. Haupt in 2004, and generations

unchanged, the probability of finding the best result drastically increases because every iteration or every

simulation of the proposed algorithm shows only the result of the fittest. Further increasing the population size to

thirty (30) and generation still unchanged, the results of finding the fittest child suddenly decreases just like in

having an initial population of eight (8). Increasing or decreasing the population size does not mean finding the

best result or the optimal result. On the other hand, when trying to decrease or increase the number of generations

from the recommended generations of twenty (20) generations, the result is the same as increasing or decreasing the number of populations. Meaning, finding the best solution or optimal result decreases when both population

and generation were decreased. Finding the initial population size is very important due to it having an effect on

the evaluation of the cost function. Having a small initial population means it improves initial performance while

having a large initial population size improves long term performance. As described by R. L. Haupt and S. E.

Haupt in 2004, the best initial population size ranges from twenty (20) to thirty (30). Further, population size is a

parameter setting that will have an effect to the evaluation of the cost function or objective function and it also

indicates the performance of GA. The entire algorithm is based on randomness. GA is an algorithm of random

search; repeatability of results is a concern. To address this issue, ten (10) independent runs/tests/iterations were made to verify the high probability of the results.

Selection function is what chooses parents that will be needed in the creation of the next generation.

Roulette-wheel selection was used because the chromosomes with the bigger fitness will likely be selected many

times to produce the next set of generation. Meaning roulette-wheel based selection will produce fitter results

compared to other selection methods such as ordinal selection wherein the chromosomes are chosen randomly.

In this study the following parameters were set and the rest are default by the GA:

Generations – 20

Initial Population – 20

Selection Function – Roulette Wheel

Base Case – No DG

This case discusses the existing condition of the system during peak and off-peak condition. The chosen

distribution system is located at Laguna where large demand customers are located such as various manufacturing

companies such as Toyota Motors, Inc. Samsung Electronics, and other alike. Also, it is in Laguna where various

industrial parks are located making Laguna as the industrial hub of MERALCO’s franchise area. Further, these

customers requires better system voltages compared to other customers like commercial and residential. As these

customers utilizes power quality or voltage sensitive equipment. If the system voltage is poor, these industrial

customers will opt to find other distribution utilities that can provide better system voltages in order for them to

manufacture better. Having poor system voltage will mean bad product or waste product output for the manufactures as they will not be able to sell their products. MERALCO’s distribution system in Laguna has a

peak demand of 900 MW and off-peak demand of 246 MW.

It can be seen in Table No. 1 that the exisitng system loss during peak is 0.0545 p.u. or 5.45 MW and 0.75

MW during of peak condition. Also shown in Figure No. 3 the system voltages at 115 kV bus during peak and

off-peak condition without DG. The figure shows that during peak condition, the system voltages sags due to the

simultaneous operations of the industrial, commercial and residential customers while there is an increase of

system voltages during off-peak condition due to limited operations of the various consumers whithin the area.

The figure also indicates that there is a room to further improve the system voltage of the distribution utility

especially the bus voltages between Sta. Rosa and Calauan delivery point substations even though the system

voltage is still within the prescribed 0.95 p.u. to 1.05 p.u. limit of the Philippine Grid Code (PGC).

Table No. 1 System Loss without DG

Case

Peak Off-peak

Optimal System Loss Optimal System Loss

Location (Substation)

Output (p.u.)

Active (p.u.)

%

Improvement Location

Output (p.u.)

Active (p.u.)

%

Improvement

Base - - 0.0545 - - - 0.0075 -

Figure No. 3 System Voltage without DG

Case A – DG output set to 100 MW

As discussed above, the setting for the initial population for this study is set to twenty (20) with twenty

(20) generations. During peak load system, the optimal location to interconnect the DG produced two (2) locations (Calamba and CIP2 substations) as these locations yielded the highest improvement in active system’s loss or an

improvement of 9.37%. However, when the system load is decreased to off-peak loading condition, the optimal

location yielded only one (1) location which is the CIP2 substation with an 18.89% system loss improvement. It

can be seen in Table No. 2, the optimal locations to interconnect the DG under peak and off-peak system loading

condition. As previously mentioned, the optimal locations to interconnect the DG are at Calamba and CIP2

substations during peak condition due to high similar results indicating that these substations yielded the highest

system loss improvement, one can accept that these results are accurate. However, during off-peak condition, only

one (1) substation location was determined. The equivalent optimal output powers of DG ranges from 0.9984 to

0.9999 p.u. or from 99.84 MW to 99.99 MW during peak condition while from 0.9784 to 0.9994 or from 97.84

MW to 99.94 MW during off-peak condition, which is almost equal to the maximum output power limit of the

DG. The table also shows how the system’s total active power losses decreased from the initial loss of 5.45 MW

down to 4.94 MW, or a 9.37% improvement during peak load system while an 18.89% improvement or from 0.75 MW to 0.61 MW. The chosen locations are considered the load centers of the distribution system in Laguna as

various industrial estates and customers are located within the area covered by these substations and is in between

the Sta. Rosa and Calauan delivery point substations where there is still an opportunity for improvement as

discussed in the Base Case.

Table No. 2 Result of iterations for Case A (peak and off-peak conditions)

Iteration

No.

Peak Off-peak

Optimal System Loss Optimal System Loss

Location (Substation)

Output (p.u.)

Active (p.u.)

%

Improvement Location

Output (p.u.)

Active (p.u.)

%

Improvement

1 CIP2 0.9940 0.0495 9.19% Calamba 1.0000 0.0062 17.56% 2 Calamba 0.9765 0.0496 9.00% Calamba 0.9994 0.0062 17.56% 3 Calamba 0.9988 0.0494 9.37% CIP2 0.9887 0.0061 18.89% 4 Calamba 0.9803 0.0495 9.19% CIP2 0.9980 0.0061 18.89% 5 Calamba 0.9999 0.0494 9.37% CIP2 0.9994 0.0061 18.89% 6 CIP2 0.9995 0.0494 9.37% Calamba 0.9816 0.0062 17.56% 7 CIP2 0.9984 0.0494 9.37% CIP2 0.9994 0.0061 18.89% 8 CIP2 0.9984 0.0494 9.37% Calamba 0.9835 0.0062 17.56% 9 FPIP 0.9999 0.0495 9.19% CIP2 0.9784 0.0061 18.89%

10 Calamba 0.9794 0.0496 8.95% CIP2 0.9330 0.0062 17.56%

0.9600

0.9700

0.9800

0.9900

1.0000

1.0100

1.0200

Bus Voltages at 115 kV Bus (p.u.)

Peak Condition Off-peak Condition

Figure No. 4 shows the comparative system voltage profile during peak condition between the Base Case

and the two (2) identified optimal substations to interconnect the DG while Figure No. 5 shows voltage profile

during the off-peak condition and the DG connected at CIP2 substation as this is only the identified optimal

location to interconnect the DG. It can be seen in both figures that the voltage profile of the system improved and

is now nearer the unity value or the 1.0 p.u. voltage.

Figure No. 4 System Voltage during Peak Condition

Figure No. 5 System Voltage during Off-peak Condition

Case B – DG maximum output set to 200 MW

In this case, the maximum output of the DG was increased from 100 MW to 200 MW to determine whether

this setting can have an effect in the performance of the GA, the DG optimal location and size, system’s active

power loss and system’s voltage profile. Based on the results found in Table No. 3, increasing the DG power

output improved the system’s loss by more than double during peak condition, from 9.37% improvement in Case

A to 19.46% improvement (5.45 MW to 4.41 MW) and by almost 70% during off-peak condition or from 18.89%

in Case A to 32.19% (0.75 MW to 0.51 MW). During peak condition, the same two (2) substation locations that were identified in Case A were also identified in this case during peak condition. However, it was identified that

Calamba substation is more appropriate to interconnect the DG as this occurred four (4) out of the ten (10)

iterations while CIP2 substation only occurred once. The size of the DG ranges from 1.9965 to 2.0000 p.u. or

from 199.65 MW to 200 MW.

0.9600

0.9700

0.9800

0.9900

1.0000

1.0100

Bus Voltages at 115 kV Bus (p.u.) during Peak Condition

Without DG With 100 MW DG at Calamba With 100 MW DG at CIP2

0.9800

0.9900

1.0000

1.0100

1.0200

1.0300

Bus Voltages at 115 kV Bus (p.u.) during Off-peak Condition

Without DG With 100 MW DG

During off-peak condition, the same substation in Case A was also identified as optimal location to

interconnect the 200 MW DG which is CIP2 substation. However, it is more evident in this case that CIP2 is the

optimal location as seven (7) out of the ten (10) simulation results indicates the CIP2 is the appropriate location

to interconnect the DG. The DG’s power output ranges from 1.9778 to 1.9996 p.u. to from 197.78 MW to 199.96

MW.

Table No. 3 Result of Iterations for Case B (peak and off-peak conditions)

Iteration

No.

Peak Off-peak

Optimal System Loss Optimal System Loss

Location (Substation)

Output (p.u.)

Active (p.u.)

%

Improvement Location

Output (p.u.)

Active (p.u.)

%

Improvement

1 CIP2 1.9775 0.0440 19.28% CPIP 1.9791 0.0052 30.86% 2 CIP2 1.9972 0.0439 19.46% CPIP 1.9907 0.0052 30.86% 3 Calamba 1.9965 0.0439 19.46% CIP2 1.9995 0.0051 32.19% 4 CIP2 1.9538 0.0442 18.91% CIP2 1.9970 0.0051 32.19% 5 Calamba 1.9997 0.0439 19.46% CPIP 1.9988 0.0052 30.86%

6 Air Liquid 1.9888 0.0443 18.73% CIP2 1.9996 0.0051 32.19% 7 Calamba 2.0000 0.0439 19.46% CIP2 1.9805 0.0051 32.19% 8 CIP2 1.9754 0.0441 19.09% CIP2 1.9976 0.0051 32.19% 9 Calamba 1.9996 0.0439 19.46% CIP2 1.9991 0.0051 32.19% 10 CIP2 1.9723 0.0441 19.09% CIP2 1.9778 0.0051 32.19%

Shown in Figure No. 6 the effect of increasing the DG’s maximum output power during peak condition to

the system’s voltage profile among Base Case, Case A and Case B. Figure No. 7 shows the system’s voltage

profile during off-peak condition with CIP2 as the only identified optimal location to interconnect the DG for

Cases A and B. Both the figure shows that increasing the maximum output power of the DG has a little effect in

the system voltage profile improvement compared with Case A or with DG’s maximum output set at 100 MW

because the lines are now almost or overlapping with each other indicating that there is a very little improvement.

It can be noted that increasing the DG’s output does not have an effect the performance of the GA as the GA still obtained the same results got from Case A during peak and off-peak condition of the system.

Figure No. 6 System Voltage during Peak Condition

0.9600

0.9700

0.9800

0.9900

1.0000

1.0100

Bus Voltages at 115 kV Bus (p.u.) during Peak Condition

Without DG With 100 MW DG at Calamba With 100 MW DG at CIP2

With 200 MW DG at Calamba With 200 MW DG at CIP2

Figure No. 7 System Voltage during Off-peak Condition

Case C – DG maximum output set to 293 MW

In this case, the maximum power output was further increased to 293 MW, again to determine the effect

in the performance of the GA, DG optimal location and size, system’s active power loss and system’s voltage

profile. The maximum output of 293 MW DG was derived using the maximum capacity of the wire or 115 kV

line, which is 2-795 MCM ACSR with a capacity of up to 343 MVA, multiplied by the assumed 95% power factor

and the 90% critical loading criteria of the line or the wire (343 MVA x 90% p.f. x 90% critical loading criteria).

It can be seen in Table No. 4 that during the system peak condition, system loss improved 27.53% or by

8.07% basis points from Case B and 18.16% basis points from Case A. Further, the system loss improvement were lower due to the incremental percentage increase compared from Cases A and B where the incremental

percentage increases were 9.37% and 10.09%, respectively. This means that there is a certain point wherein the

system loss improvement will reach its peak and will now start to improve at a slower pace. This can also be the

cause of the load of the system, where the load of the system is now insufficient to cause a higher system loss

improvement which means that the DG’s maximum effect in the system can also be determined how large or how

big the load or the system is. Further, it is safe to say that the DG’s size is relatively proportional to the system

loss improvement. Meaning, as the DG’s output increases the system loss improvement also increases. Further in

this Case, only Calamba substation was identified as a suitable location to interconnect the DG because of having

the highest system loss improvement compared with the system without the DG or the Base Case and the DG’s

output ranges from 292.60 MW to 292.77 MW.

During off-peak system, the system loss improved to 34.85% compared to Base Case or the system loss

only improved by a mere 2.66% basis points compared with Case B and 15.96% basis points compared with Case

A which is lower than the increase compared with Case B from Case A. On one hand, the optimal location to

interconnect the DG is still the same which is CIP2 and is consistent with the simulation results in the previous

cases during off-peak condition as this resulted in the highest system loss improvement. Also, during off-peak

condition, only CIP2 susbtation was identified because the the substation occurred eight (8) out of the ten (10)

iteration or simulation results. Finally, the DG’s output ranges from 282.95 MW to 286.21 MW. It can be noted

that the DG’s maximum output range is almost not equal to the set output of 293 MW, which means that beyond

the DG output range, the system loss improvement will be lesser than the obtained 34.85% system loss

improvement.

0.9800

0.9900

1.0000

1.0100

1.0200

1.0300

Bus Voltages at 115 kV Bus (p.u.) during Off-peak Condition

Without DG With 100 MW DG With 200 MW DG

Table No. 4 Result of Iterations for Case C (peak and off-peak conditions)

Iteration

No.

Peak Off-peak

Optimal System Loss Optimal System Loss

Location (Substation)

Output (p.u.)

Active (p.u.)

%

Improvement Location

Output (p.u.)

Active (p.u.)

%

Improvement

1 CIP2 2.8587 0.0398 26.98% CPIP 2.8087 0.0050 33.52% 2 Calamba 2.9277 0.0395 27.53% CIP2 2.8295 0.0049 34.85% 3 Calamba 2.9260 0.0395 27.53% CIP2 2.4671 0.0049 34.85% 4 CIP2 2.9147 0.0396 27.35% CIP2 2.8462 0.0049 34.85% 5 CIP2 2.9039 0.0396 27.35% CIP2 2.6684 0.0050 33.52% 6 Calamba 2.5936 0.0410 24.78% CIP2 2.8621 0.0049 34.85% 7 Calamba 2.9106 0.0396 27.35% CPIP 2.6672 0.0050 33.52% 8 Calamba 2.9056 0.0396 27.35% CIP2 2.8363 0.0049 34.85% 9 FPIP 2.8911 0.0400 26.61% CPIP 2.8157 0.0050 33.52%

10 Canlubang 2.9283 0.0436 20.01% CIP2 2.8353 0.0049 34.85%

Figure No. 8 shows the system voltages among the cases. The figure again shows that there is a very little

room for improvement in terms of the system voltage even as the DG’s output is increased as discussed earlier in

Case B. However, as seen in Figure No. 9 during off-peak condition, Case C has the best system voltage compared with the other Cases as this has the best system voltage.

It can generally be noted that increasing the DG’s output does not have an effect the performance of the

GA as the GA still obtained the same results (optimal location and size of the DG) got from all the case with DG

during peak and off-peak condition of the system.

Figure No. 8 System Voltage during Peak Condition

0.9600

0.9700

0.9800

0.9900

1.0000

1.0100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Bus Voltages at 115 kV Bus (p.u.) during Peak Condition

Without DG With 100MW DG (Calamba) With 100MW DG (CIP2)

With 200MW DG (Calamba) With 200MW DG (CIP2) With 293MW DG (Calamba)

Figure No. 9 System Voltage during Off-peak Condition

Summary of all the Cases

It can be seen in the tables below the optimal locations, DG’s output power range and system loss

improvement during peak and off-peak condition of all the cases.

Table No. 5 Summary of Results of System Loss Improvement during Peak Condition

Case

Optimal System Loss

Location (Substation)

Output (p.u.) Active (p.u.)

%

Improvement

Base - - 0.0545 -

A Calamba 0.9988 to 0.9999

0.0494 9.37% CIP2 0.9984 to 0.9995

B Calamba 1.9965 to 2.0000

0.0439 19.46% CIP2 1.9972

C Calamba 2.9260 to 2.9277 0.0395 27.53%

Table No. 6 Summary of Results of System Loss Improvement during Off-peak Condition

Case

Optimal System Loss

Location (Substation)

Output (p.u.) Active (p.u.)

%

Improvement

Base - - 0.0075 -

A CIP2 0.9784 to 0.9994 0.0061 18.89% B CIP2 1.9778 to 1.9996 0.0051 32.19%

C CIP2 2.4671 to 2.8621 0.0049 34.85%

CONCLUSIONS

Genetic algorithm (GA) can be used as a better tool in finding the optimal solution(s) compared to manually

searching for the optimal location(s) to interconnect and the size(s) of the DG(s) in the distribution system.

Moreover, GA is faster, easier and more accurate to use compared to other search methods. Adding DG in to the

system reduces both active and reactive power losses, improves power system security by sourcing power within

the distribution system instead of sourcing from the bulk power generators thereby reducing the threat of

vulnerability to terrorism and it improves the system’s voltage profile as seen in the various tables and figures

presented in all the cases, respectively. Further, the amount of effect of the DG to the system greatly depends to the optimal location and size of the DG installed in the system and the load of the system itself or the distribution

system. It can also be seen in all the results and cases that the output limit of the DG is somewhat directly

proportional to the system’s active power losses improvement during peak and off-peak condition. However, there

0.9900

1.0000

1.0100

Bus Voltages at 115 kV Bus (p.u.) during Off-peak Condition

Without DG With 100MW DG With 200MW DG With 293MW DG

will come a point that the improvement will be lesser compared with the previous size or output of the DG as

DG’s, as previously mentioned, effect on the system also depends on the load of the system itself. On one hand,

increasing the size or power output of the DG did not have an effect on the performance of the GA as shown in

Case B and C.

Based on the various cases shown above, the optimal substation location to interconnect the DG must be at CIP2 substation during peak and off-peak conditions. This is because most of the large industrial customers are

located near the CIP2 substation; it produces the least system active power loss or has the best active power loss

improvement and resulted in best system voltage. Further, CIP2 substation is the only substation location that has

been identified during off-peak condition while it is one (1) of the two (2) substations indicated in Cases A and

B. Lastly, CIP2 substation is almost equidistant between two (2) delivery point substations, Sta. Rosa and Calauan

DPs.

Another key point in this study is the optimal size of the DG. Based on the various cases, the optimal size

of the DG is 100 MW as this resulted in better system voltage and there is only very little or minimal improvement

in system voltages when the DG’s size is increased. Furthermore, it did not violate any system condition such as

having a power backflow at the 230kV side which will need coordination with the grid owner/operator if ever

backflow is allowed and various re-setting of protection and control relays at the 230kV side.

RECOMMENDATIONS

Objectives in this study have been properly addressed by proving that GA is a better search method

compared to other known methodologies, it minimized the system’s total active losses, found the optimal location

to interconnect and size of the DG and voltage profile improvement of the system. However, further studies must

be conducted on how to minimize both the system’s total active and reactive power losses. Reactive power losses

have also great effect in the system’s capability to transfer power from point to point because reactive power

losses limit the transfer of real power in the system. In this study, the DG must both inject real and reactive power

to the system to reduce both the active and reactive power losses in the system.

Another study must be added is the economic analysis of what type of DG will be best suited for the system.

In the objective function, both the economic and technical analysis must be carefully taken into consideration

because this will determine whether the study will be both beneficial to the people and the investors. This will

easily determine the type, size and location of DG.

Identically with the other studies, the maximum number of DGs must also be studied as this have an effect

in the planning of the whole system. Also, the number of DGs in the system also affect the system’s reduction in

losses and to determine the relationship between the numbers of DGs in the system the voltage profile of the

system because the system may get worse if the number of DGs in the system is too much or the system cannot

afford additional power source.

Lastly, the seasons of the year must also be taken into consideration because every season there are various

characteristics of the system due to the nature of the loads on how they react with the season and the system. For

example, the summer season in the Philippines registers the peak load of the system or the highest load of the

system for the whole year. Moreover, it is the time of the season where the surroundings are very hot and dry.

Taking this into consideration will make the DG suit better. With this known characteristics of the system, adding

the type of DG and knowing the optimal output of both the real and reactive power will make the study better.

There are lots of studies that can be taken into consideration like the impacts of the DG to the system (i.e. finding

the correct size of protective relays in the system, harmonics, etc.).

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