8/11/2019 Application of Harvest Season Artificial Bee Colony Algorithm to Economic Load Dispatch of Power System Operati

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The 2013 Annual Conference of Power and Energy Society, Toki Messe, Niigata, Japan, August 27-29, 2013

Application of Harvest Season Artificial Bee Colony Algorithm to EconomicLoad Dispatch of Power System Operation with Pollutant Emissions

A.N. Afandi

a)

, Student Member, Hajime Miyauchi

, Senior Member

This paper presents an application of Harvest Season Artificial Bee Colony (HSABC) algorithm to solve an Economic Load

Dispatch (ELD) of power system operation throughout a Combined Economic and Emission Dispatch (CEED) problem. The

IEEE-30 bus system is adopted as a sample system for testing the problem. The simulations showed that statistical, numerical,

and convergence results of the HSABC are better than the existing tested methods. The proposed method of the HSABC seems

strongly to be a new promising approach for solving an ELD throughout the CEED problem.

Keywords: CEED, cost, HSABC, problem

1. Introduction

One of the most important problems in the power system

operation is to reduce the total technical operating cost through the

various combinations of power plants. By considering this

condition, a cost optimization is a significant case for obtaining

the best schedule of the committed generating unit outputs to meet

a certain load demand at a certain time under some operational

limitations belonged in equality and inequality constraints. An

Economic Load Dispatch (ELD) is usually applied to the power

system operation for optimizing the total operating cost. Presently,

the ELD also considers pollutant emissions into the air by burning

fossil fuels as an Emission Dispatch (EmD)(1)and it is transformed

into a Combined Economic and Emission Dispatch (CEED) (1),(2).

Many previous works have been applied to solve the ELD

throughout the CEED problems categorized into classical and

evolutionary methods. For a couple years, the evolutionary

methods are common used to solve the ELD problem and the most

popular method is a Genetic Algorithm (GA)(3). Recently, the

newest algorithm of evolutionary methods is an Artificial Bee

Colony (ABC). This algorithm is inspired by natural behaviors of

honeybees in nature. A novel generation of the ABC is a Harvest

Season Artificial Bee Colony (HSABC)(4)composed by multiple

food sources for attempting the harvest season situation.

2. ELD and HSABC

The ELD problem considered an EmD is presented inliteratures(1),(2). In this section, it is expressed by a CEED problem

as single objective function of optimization problem. To obtain the

minimum total cost of the CEED, the HSABC is demonstrated by

using two strategies, a Controlled Distance Placement (CDP) and

an Uncontrolled Distance Placement (UDP). The CDP is a

creating strategy for locating food sources within a certain

distance for every cycle and the UDP locates food sources in

various distances for each other. In these works, the ABC and GA

are selected as comparators of the HSABC and both algorithms

have been clearly presented in references(2),(3). The HSABCs

processes used a collaboration of food sources are performed in a

reference(4)

for determining the best solution. In general, the mainprocedures of the HSABC are given in equations (1) to (3) as

follows:

Minimize .. (1) ... (2)

. (3)where t is the CEED ($/hr), w is a compromised factor, h is a

penalty factor, Ftis the total fuel cost of generating units ($/hr), Et

is the total emission of generating units (kg/hr), vij is a food

position, xijis a current food, i is the i

th

solution of the food source,k {1,2,3,,SN},j{1,2,3,,D}, SN is the number of solutions,

D is the number of variables of the problem, i,j is a random

number within [-1, 1], xkj is a random neighbor of xij, xfj is a

random harvest neighbor of xkj, Hiho is a harvest season food

position, ho{2,3,,FT}, f {1,2,3,,SN}, FT is the total

number of flowers for the harvest season, Rjis a randomly chosen

real number within [0,1], MR is a modified rate.

3. Simulation Results

IEEE-30 bus system is employed as a sample system for

simulating the CEED problem. The ABC, GA, and HSABC are

adopted to solve CEED problem referred to references(2), (3),(4). The

CEED problem considered 283.4 MW of total load, 0.5 of

compromised factor, equality of generating power, generating

power limits and 5% of voltage limits.The ABC and HSABC

are run out by using colony size=100, food source=50, limit food

source=50 and foraging cycles=150. The HSABC uses three food

sources for the CDP and UDP. The GA is executed by using

several controlling parameters. Its parameters are population=50,

elite count=2, generation=150, initial population=0-1, crossover

fraction=0.4, migration fraction=0.1, migration interval=20,

forward migration direction, linear ranking fitness function,

gaussian mutation and roulette selection.

Captured within 10 cycles from all period of running out the

designed program for the HSABC, Figure 1 and Figure 2 show thepositions of food sources. These figures illustrate food sources

positions of the placement strategies. Specifically, these figures

a) Correspondence to: A.N. Afandi. E-mail: an.afandi@ieee.org Computer Science and Electrical Engineering, Graduate School of

Science and Technology, Kumamoto University, 2-39-1 Kurokami,Chuo-ku, Kumamoto 860-8555, Japan

8/11/2019 Application of Harvest Season Artificial Bee Colony Algorithm to Economic Load Dispatch of Power System Operati

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The 2013 Annual Conference of Power and Energy Society, Toki Messe, Niigata, Japan, August 27-29, 2013

describe the collaboration of three food sources in the UDP and

CDP to show the involvement of the first food source and other

food sources for determining solutions.

Fig. 1. Collaboration of food sources using CDP.

Fig. 2. Collaboration of food sources using UDP.

Fig. 3. Convergence speeds of the tested algorithms.

Table 1. Comparison of the CEEDs statistical results.

Subjects ABC GAHSABC

(UDP)

HSABC

(CDP)

Population 50 50 50 50

Max. Iteration 150 150 150 150

Min. Iteration 34 51 24 17

Start Cost CEED ($/hr) 727.63 729.42 727.11 726.45

Min.Cost CEED ($/hr) 725.04 723.68 725.04 724.60

Min.Cost ELD ($/hr) 415.13 413.97 415.13 414.80

Min.Cost EmD ($/hr) 309.91 309.71 309.91 309.80

Mean ($/hr) 725.15 725.34 725.09 725.10

Median ($/hr) 725.04 725.04 725.04 725.04Mode ($/hr) 725.04 725.04 725.04 725.04

Std. deviation 0.42 0.69 0.24 0.22

The convergences of three tested methods are given in Figure 3

for the ABC, GA and HSABC. These characteristics illustrate a

speed of each computation during searching the best value of the

final solution for the CEED problem. From this figure, it is known

that the fastest computation is obtained by using both types of the

HSABC. Specifically, HSABC used CDP has better performances

contrasted to the UDP and other tested methods. In detail, the

obtained iteration are 34 of the ABC, 51 of the GA, 24 of the

HSABC using UDP and 17 of the HSABC using CDP.

Table 2. Final results of the simulations.

Gen. UnitPower

(MW)

Fuel Cost

($/hr)

Emis. Cost

($/hr)

Tot. Cost

($/hr)

G1 126.07 311.74 151.55 463.29

G2 49.74 130.34 125.12 255.46

G3 28.40 78.81 84.22 163.03

G4 31.80 111.78 97.07 208.85

G5 26.63 97.62 80.93 178.55

G6 27.17 99.97 80.93 180.9Total 289.81 830.26 619.82 1,450.08

Statistical results for the ABC, GA and HSABC are given in

Table 1. This table compares the computing abilities of each

algorithm for determining the solution of the ELD throughout the

CEED problem in term of populations, obtained iterations, costs,

means, medians, modes, standard deviations. By considering

283.4 MW of load demand, six generating units produce 289.81

MW of total power output with 6.41 MW of total power loss. The

minimum total cost is obtained in 1,450.08 $/hr contributed by

830.26 $/hr of fuel cost and 619.82 $/hr of emission cost.

4. Conclusions

This paper presents the HSABC for solving an ELD throughout

the CEED problem using IEEE-30 bus system. The simulations

showed that the HSABC is better than the existing tested methods.

Convergence speeds of HSABC are smooth and quick to select

solutions and HSABC used CDP has better performances. The

proposed method of the HSABC algorithm seems strongly to be a

new promising approach for solving an ELD throughout the

CEED problem based on the solution quality and the

computational efficiency under several constraints. From these

works, real system applications are devoted to the future

investigations.

eferences

(1) Yunzhi Cheng, Weiping Xiao, Wei-Jen Lee and Ming Yang , A New Approach

for Missions and Security Constrained Economic Dispatch,Proc. NAPS, IEEE

Conference Publication, Starkville USA, 4-6 Oct 2009, pp. 1-5.

(2) R.Gopalakrishnan, A.Krishnan, A novel combined economic and emission

dispatch problem solving technique using non-dominated ranked genetic

algorithm, European Journal of Scientific Research, vol.64, pp. 141-151, Nov.

2011.

(3) Karaboga D, Basturk B, A Powerful and Efficient Algorithm for Numerical

Function Optimization: ABC Algorithm, J. of Global Optimization, vol. 39, no.

0925-5001, pp. 459-471, Apr. 2007.

(4) A.N. Afandi, Hajime Miyauchi, Multiple Food Sources for Composing Harvest

Season Artificial Bee Colony Algorithm on Economic Dispatch Problem, Proc.

The 2013 Annual Meeting of the IEEJ, No. 6-008, pp. 11-12, Nagoya, 20-22

March 2013.

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CostoftheCEED(

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Iterations

ABC

GA

HSABC (UDP)

HSABC (CDP)