COMPUTATIONAL FRAMEWORK FOR POWER ECONOMIC

DISPATCH USING GENETIC ALGORITHM

Tahir Nadeem Malik

Elect. Engg. Deptt. University of Engg.& Tech., Taxila (Pakistan) Student Member IEEE

Abdul Qudus Abbasi

Department of Computer Science, Quaid-i-Azam University, Islamabad (Pakistan)

Aftab Ahmad

National Power Control Centre WAPDA, Islamabad (Pakistan)

Elect. Engg. Deptt. University of Engg.& Tech., Taxila (Pakistan)

Keywords: Artificial Intelligence (AI), Software Architecture, Framework, Power Economic Dispatch (PED), Genetic

Algorithm (GA), GA Parameters, White-box Reuse.

Abstract: Economic Dispatch Problem (EDP) is the important step in Power System operation and is non-convex

optimization problem. It has been solved comprehensively with mathematical programming approaches.

However, these approaches handle non-convexity with assumption and resulting in an inaccurate dispatch.

Genetic algorithms are potential tools for Economic dispatch solution. Computational framework

“PED_Frame” has been developed which can handle economic dispatch solutions based on mathematical

programming and Genetic Algorithm based approaches independently and in hybrid form. It has been tested

on standard three machine and twenty machine test systems.

1 INTRODUCTION

Power Economic Dispatch (ED) is one of the main

functions

of modem Energy Management System

(EMS) and

vital step in power system operational

planning. It is defined as the process of allocating

generation levels to the generating units in the mix,

so that the system load may be supplied entirely and

most economically (Happ, 1977). It is on-line

function, carried out after every 15-30 minutes or on

request in Power Control Centers.

Economic dispatch is constrained non-convex

optimization problem. The cost functions of multi-

valve thermal machines are non-smooth

discontinuous and non-differentiable. However,

the

generator fuel costs are generally approximated in the

continuously differentiable quadratic form while

solving economic dispatch problem in mathematical

programming approaches.

Consequently, inaccurate

dispatch results. Genetic algorithms (GA) have been

used to solve the economic dispatch problem

independently and in conjunction with other AI tools

and optimization approaches. GA based approaches

have the ability to handle the non-convex economic

dispatch problem efficiently (

Chao-Lung Chiang,

2005) without any restriction on objective function

thus providing potential tool for real economic

dispatch problem.

The concept of power market has brought the

changes in the trend of power industry. The role and

structure of control center is being redefined. Quick

decision-making and fast answers are very

significant in market environment. Flexible and

extensible framework having the ability to

incorporate approaches for Economic Dispatch

solution may be useful tool for industry and

academia.

In this paper effort has been made to develop the

framework PED_Frame for economic Dispatch

using Genetic Algorithm and mathematical

191

Nadeem Malik T., Qudus Abbasi A. and Ahmad A. (2006).

COMPUTATIONAL FRAMEWORK FOR POWER ECONOMIC DISPATCH USING GENETIC ALGORITHM.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 191-194

DOI: 10.5220/0001203601910194

 SciTePress

programming approaches. The paper is organized as

follows: Section II gives the definition of Economic

dispatch problem. Section III covers concepts and

architecture of PED_Frame. Section IV includes the

implementation detail in PED_Frame. Lastly there is

conclusion.

2 ECONMIC DISPATCH

PROBLEM

The Economic Dispatch in its simplest form is

formulated as:

Minimize

∑

iiT

)P(CC

(1)

Subject to equality constraint:

∑

iLD

PPP (2)

Inequality constraints:

i (min)

≤ P

i (max)

(3)

Where: C

Total cost, C

generator fuel cost of the

ith generating unit, P

total load, P

i(min)

the

minimum generation power, P

i (max)

the maximum

generation power, and P

transmission loss given by

∑∑

iiL

PaPP

Where: a

: transmission loss coefficient.

3 PROPOSED

FRAMEWORK - “PED_Frame”

Framework is a set of cooperating classes that

makeup reusable design for a specific class of

software and defines overall structure of application,

it’s partitioning in classes and objects (Gamma,

1995). A framework can be customized to a

particular application by creating application

specific subclasses of abstract classes targeting a

specific group of applications, which have similar

architecture design. A fairly large number of useful

frameworks are available for application builders.

One of these frameworks is Microsoft’s Document-

View architecture.

The proposed framework “PED_Frame” targets

power economic dispatch software application

development. It provides white-box reuse

mechanism for incorporating new algorithms. All

inputs and outputs are handled through a grid.

PED_Frame provides extremely efficient component

for status visualization.

Standard Template Library (STL) is used as base

for the development of the vector-matrix library.

This library gives efficient algorithm

implementation for vectors and matrices. Document-

View Architecture acts as container for PED_Frame

and provides base services. These are primarily in

the form of messaging mechanisms, and threading

support. The class diagram of PED_Frame is shown

in figure 1.

3.1 Input/Output Using Grid

CPEDGrid class acts as interface for grid, which

provides functionality like input, formatted output,

print and data validation checks. It extends grid

functionality to link its services with other parts of

the PED_Frame. These are:

a. Data present in Grid is transferred to a matrix,

which can act as data communicator.

b. Data present in matrix is formatted in grid for

display and printing purposes.

c. Data editing and data validation checks.

PED_Frame creates three instances for grid class -

-- Machine data, B-Coefficients and output.

3.2 Analysis Status Visualization

Analysis status visualization is implemented in class

CPEDAnalysisStatus derived from CDialog class of

MFC. This dialog window contains progress

indicators showing current state of convergence,

iteration count, and time elapsed.

3.3 Analysis

This part of the framework is responsible for actual

implementation of algorithm. The analysis is

accomplished through following classes:

a. CPEDAnalysisBase class is derived from

CWinThread so acting as independent thread of

execution. It contains references to matrices for

input, B-Coefficient and output, Machines

count, maximum iterations count, power

demand and error tolerance. It also provides

virtual functions for start analysis,

communicating status information and

formatting output.

b. CPEDGABased is derived from

CPEDAnalysisBase and works as base for

implementation of genetic algorithm. This class

contains information for Genetic Algorithm

(GA) parameters. A new class can be derived

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from this class to implement any GA based

solution for Economic dispatch.

c. CPEDCABase is also derived from

CPEDAnalysisBase and works as base for

conventional algorithm implementation. It

contains information like initial start and step

length. A new class can also be derived from

this class to implement any mathematical

programming based solution for Economic

dispatch.

3.4 Vector-Matrix Library

This library includes template based CPEDVector

and CPEDMatrix classes. CPEDVector is derived

from vector<class T> and it implements

functionality for initialization, resizing, addition,

multiplication, searching, sorting and debugging

routines.

CPEDMatrix class is derived from CPEDVector

< CPEDVector <calss T> > and it implements

functionality for initialization, resizing, and matrix

algebra operations.

3.5 MFC Document-View

Architecture

PEDFramework is primarily based upon doc-view

architecture of MFC. CPEDApp is derived from

CWinApp, CMainFrame is derived from

CFrameWnd, CPEDView is derived from

CScrollView and CPEDDoc is derived from

CDocument class. CPEDView contains grid objects

and manages their functionality. CPEDDoc contains

instances of matrix class for input matrix, B-

Coefficient matrix and output matrix. The reference

of these matrices is passed to respective

CPEDAnaslysisBase derived class, which performs

analysis by using these matrices and updates output

matrix.

4 IMPLEMENTATION USING

PED_FRAME

4.1 Economic Dispatch using

Mathematical Programming

based Approach

It can be implemented as follows:

a. Derive a class from “CPEDCABase”and

declare algorithm specific parameters in it.

b. Write code for new algorithm in StartAnalyse

function, which is in overridden form.

c. Call UpdateStatus with error value and current

iteration.

d. Override “FormatResults” to transfer results

from analysis structure to output matrix.

e. Create a new menu item and write its message

handler in CPEDDoc. Create an object of

Derived class and set parameters.

f. Create an object of CPEDAnalysisStatus and

pass this object to it; calling its DoModal().

This will start analysis.

g. On completion of analysis just call

“UpdateAllViews”, this will update grid

control with results.

4.2 Economic Dispatch using GA

a. Derive a class from “CPEDGABase”.

b. Remaining steps are same as those in

Conventional Approach.

4.3 Economic Dispatch in

PED_Frame

In PED_frame following algorithms have been

implemented:

Lamda iteration algorithm with and without loss,

Simple Genetic Algorithm with typical reproduction,

crossover and mutation operators, and Sequential

hybrid approach, which after fixed number of

generation, passes control to Lambda iteration

algorithm.

The transmission loss has been calculated using

B-Coefficients. The inputs are standard machine cost

curves, B-coefficients, power demand. Genetic

algorithm solution involves GA parameter settings.

The software has been tested on 3 and 20 machine

systems (

Ching-Tzong Su, 2000) and several other

standard test systems. However, in the paper three

machine system results have been given in Table 1.

Table 1: Computational Results Using PED_Frame.

Unit Out

put (MW)

Lambda

Iteration

Genetic

Algorithm

Hybrid

73.6600 50.0000 73.6600

69.9850 75.7173 69.9850

75.1803 93.0189 75.1803

total

218.8253 218.7362 218.8253

(MW) 8.8254 8.7362 8.8254

210.0 210 210.0

Generation

Cost ($/h)

3164.5664 3155.0181 3164.5664

COMPUTATIONAL FRAMEWORK FOR POWER ECONOMIC DISPATCH USING GENETIC ALGORITHM

193

5 CONCLUSIONS

Economic dispatch is online function and vital step

in power system operational planning. It forms an

important part of energy management system. It has

been solved comprehensively in mathematical

programming environment by approximating cost

curves. However, genetic algorithm based

approaches ca handle actual non-convex cost curves

effectively. In this paper a framework has been

developed which can handle economic dispatch

using classical optimization and Genetic Algorithm

based approaches independently and in hybrid form.

It has been tested for on 3 machine, 20 machine

systems and several other standard test systems. It is

the platform, which is flexible, extensible and useful

for many individuals. Step by step transition of

PED_Frame to the computational framework

“Power System Master (PSM)” would be the future

activities to achieve the goal of developing the

software package for Power System Operational

Planning.

REFERENCES

Happ H.H, 1997. Optimal power dispatch -- A

comprehensive survey. In IEEE Transactions on PAS,

vol. PAS-96, no.3, pp.841-854.

Chiang Chao-Lung, 2005. Improved genetic algorithm for

power economic dispatch of units with valve-point

effects and multiple fuels. In IEEE Transactions on

Power System, vol. 20, no. 4, pp. 1690 –1699.

Gamma, Helm, Johnson, Vlissides, 1995. Design Patterns

Catalog of Design Patterns, Addison-Wesley.

Desktop Application with Visual C++ 6.0, MCSD Series

Microsoft Press.

Chowdhury B.H. & Rahman Salfur 1990. A review of

recent advances in economic dispatch. In IEEE

Transactions on Power Systems,vol.5, no.4, pp.148-

1259.

Su Ching-Tzong & Lin Chien-Tung, 2000. New approach

with a Hopfield modeling framework to economic

dispatch. In IEEE Transactions on Power Systems,

vol.15, no.2, pp. 541 -545.

CPEDApp CPEDView CPEDDoc CFrameWnd

CWinThread CDialog

CPEDGrid CPEDAnalysisisBase CPEDAnalysisStatus

CPEDGABase CPEDCABase CPEDMatrix CPEDVector

Derived From

Contained in

Figure 1: Class Diagram for PED_Frame

Figure 1: Class Diagram for PED_Frame.

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