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PROJECT OUTLINE

CHAPTER ONE

INTRODUCTION

1.1 Economic Load Dispatch

1.2 Background of Study

1.3 Statement of Problem

1.4 Objective of Study

1.5 Methodology

1.6 Scope and Limitation of Study

1.7 Significance of Study

CHAPTER TWO

LITERATURE REVIEW

2.1 Economic Generation Scheduling Of Power Plants

2.2 System Constraints

2.2.1 Equality Constraints



2.2.2 Inequality Constraints

2.3 Review Of Several Methods Of Solving Eld Problems

2.3.1 The Lambda – Iteration Method

2.3.2 The Gradient Search Method

2.3.3 Classical Kirchmayer Method

2.3.4 Newton‟s Method

2.3.5 Linear Programming

2.3.6 Particle Swarm Optimization Method

2.3.7 Genetic Algorithm

2.3.8 Artificial Neural Networks

2.4 Economic Load Dispatch and the state of the Nigerian Power

System

2.5 Power World Simulator

2.6 Matlab

2.7 Summary of Reviews



CHAPTER THREE

METHODOLOGY

CHAPTER FOUR

RESULTS AND DISCUSSION

CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

REFERENCES

APPENDICES



CHAPTER ONE

INTRODUCTION

1.1 ECONOMIC LOAD DISPATCH

The problem of power supply in Nigeria is one of the greatest challenges the nation

has been facing for a long time. Electricity generation is one of the most important

sectors of the electricity industry. It is one of those problems if tackled can give way

to solving the problem of stable power supply in Nigeria leading to industrialization.

Scarcity of energy resources, increasing power generation costs and ever-growing

demand for energy necessitate the need for optimal economic dispatch in modern

power systems.

The traditional formulation of the Economic Load Dispatch (ELD) problem is a

minimization of summation of the fuel costs of the individual dispatchable generators

subject to the real power balanced with the total load demand as well as the limits on

generators outputs. The ELD problem involves two separate steps namely the unit

commitment and the online economic dispatch. The unit commitment is the selection

of unit that will supply the anticipated load of the system over a required period of

time at minimum cost. The function of the online economic dispatch is to distribute

the load among the generating units actually paralleled with the system in such a

manner as to minimize the total cost of supplying the minute to minute requirements

of the system. Thus, ELD problem is the solution of a large number of load flow

problems by choosing the one which is optimal in the sense that it needs minimum



cost of electric power generation. Also, accounting for transmission losses results in

considerable operating economy. Furthermore, ELD is equally important in system

planning particularly to the location of power stations and building of new

transmission lines.

1.2 BACKGROUND OF STUDY

Since an engineer is always concerned with the cost of products and services, the

efficient optimum economic operation and planning of electric power generation

system have always occupied an important position in the electric power industry. The

operation cost in power systems needs to be minimized at each time via ELD.

ELD is used in real-time energy management power system control by most

programs to allocate the total generation among the available power stations. In

practical power system operation conditions, many power stations with thermal

generating units supplied with multiple fuel sources like coal, natural gas and oil

require that their fuel cost functions may be segmented as quadratic cost functions for

different fuel types. The ELD problem with quadratic fuel cost functions is to

minimize fuel cost among the available fuels of each unit satisfying load demand and

generation limits. For any specified load condition, ELD determines the power output

of each plant (and each generating unit within the plant) which will minimize the

overall cost of fuel needed to serve the system load.



A wide variety of optimization techniques have been applied to solving ELD

problems. Some of these techniques are based on classical optimization methods while

others use artificial intelligence methods or heuristic algorithms.

1.3 STATEMENT OF PROBLEM

A number of power stations are connected to the national grid which supplies power

to different load centres. The load demand is totally dependent on the consumers and

it varies over a wide range. The cost of power generation is not the same for every

power station as a result of the variation in type of fuel, so to have the minimum cost

of generation for a particular load demand; we have to distribute the load among the

power stations which minimize the overall generation cost with the constraint that no

station is overloaded.

1.4 OBJECTIVE OF STUDY

The electric power industry is currently undergoing an unprecedented reform. One of

the most exciting and potentially recent developments is increasing usage of artificial

intelligence techniques.

The main objectives of this study are:

to solve the economic load dispatch problem, recognising any operational limits

of generation and transmission facilities, using artificial neural network,



To virtually demonstrate economic load dispatch using Power World Simulator

to generate real time power throughout the National grid.

To analyse the effect of fuel choice on cost of generation

1.5 METHODOLOGY

Power system control engineers are challenged by the amount of data they have to

observe in order to get an accurate result. The power engineer needs assistance to

interpret the data and extract information. In this work, the maximum-minimum

power limit and cost function of individual power stations with their transmission loss

coefficients are the data required. The following methods will be implemented in the

course of this project:

develop a functional artificial neural network program in Matlab 7.5 to solve the

economic dispatch problem,

test run the software for different values of load demands,

set up the National grid using Power world simulator,

evaluate the economic load dispatch problem in the power system network using

Power world simulator.

1.6 SCOPE AND LIMITATION OF STUDY

In this project, artificial neural network is applied to the online economic load

dispatch of fourteen power stations in the Nigeria power system. These power stations





CHAPTER TWO

LITERATURE REVIEW

2.1 ECONOMIC LOAD DISPATCH IN NIGERIAN POWER SYSTEM

In Nigeria, the generating stations are mostly in the southern part of the country with one

National Control Centre (NCC) at Oshogbo and one Supplementary National Control

Centre (SNCC) at Shiroro (A. Odubiyi, 2008). For the smooth operation of the power

system, NCC is saddled with the responsibility of system operations. System operation and

control involves scheduling and dispatching of generation to meet demand in a safe and

reliable operation on 24 hour basis all year round at minimum cost. Under current

conditions, these tasks are challenging for NCC with inadequate modern scheduling tools

and infrastructure. Collection of data, unit dispatch and load shedding instructions are

effected through Power Line Carriers (PLC) based telephony. This is slow and unable to

cope at time of system stress or emergency. Inadequate generation to meet total consumer

demand has been a constant operational challenge for engineers at NCC. More so,

appropriate scheduling procedures and manuals are non-existent for orderly operations. For

many years, approach to scheduling and dispatch were based on intuition and experience of

the respective operator. (T.S Wudil, 2008).

2.2 POWER SITUATION IN NIGERIA

The consumer‟s load demand in Nigeria is about twice the present installed generating

capacity and to say the least, “Nigeria is experiencing energy crisis”. A major problem that

fuels the Nigerian Energy Crisis is the shortage of generation capacity arising from over



aged power plants. The plants at some of the power stations are over forty years old. For

instance, the machines at Sapele power station are over twenty years old and defective. The

station presently produces about 10% of installed capacity. The plants at Delta, Kainji and

Jebba produce only 50% of installed capacity (C.C.Okoro, 2008).

A summary of some power plants development in Nigeria power stations is given in table

2.1 below:

Installed Source of Year of

Stations Capacity (MW) Energy Commission Age of Plant Available Capacity(MW)

Jebba Hydro 1986 24 385.6

Shiroro 540 Hydro 1990 20 450

Kainji 600 Hydro 1 32-42 480

Afam 760 Gas/Oil 196 28 -45 460

Delta IV 987 Gas/Oil 196 20-44 550

Sapele 600 Gas/Oil 197 29-32 120

Ijora 1020 Coal/Gas 1978 32

Oji 60 Coal 1956 54

Egbin 30 Gas/Oil 1986 24 1320

Geregu 1320 Gas/Oil 2007 3 276

Omotosho 300 Gas/Oil 2007 3 76

A.E.S 335 Gas/Oil 2001 9 224

Calabar 270 Diesel 1935 75 ---

Omoku 6.6 Gas 2007 3 ---

ASCO 100 Gas ---

Ibom Power Gas 187

Okpai Gas 2006 4 460

Olorunsogo Gas 114

Trans Amadi Gas 100

EPP1 Aggroko 100 Gas 2001 9 ---

EPP1 Geometric 15 Gas 2001 9 ---

EPP3 15 Gas 2001 9 ---CAI 20.4 Gas 2001 9 ---

2.4



The common problems associated with these stations are obsolete equipment which often

puts the units out of service, non-availability of spare parts and lack of requisite technology

for effective preventive maintenance. The cumulative effect of the above is the massive load

shedding, low voltage and frequency control problems, system collapse and a high level of

inefficiencies in the power. Due to the epileptic nature of the Nigerian power system, most

industrial loads rely on private standby plants. It is estimated that about 33% of suppressed

load exist in the networks. These loads are sometimes switched to the grid causing sudden

increase in load (Achugbu K.C.).

LOAD FORECASTING

The load growth of a geographical area served by a utility company is the most important

factor influencing the expansion of a power system. Therefore, the forecasting of load

increases and the system reaction to these increases is essential to the planning process

(A.O. Ibe, 2002). Load forecasting is necessary in planning the level and mix of generating

capacity that will be used to support actual demand, the sequence in which power stations

are brought into operation, the investment of generating capacity and the development of

fuel supplies.

The load curve from year 2000 to 2060 is shown in fig 2.3. From fig 2.3, the load demand at

2015 should be 14,000MW. We need to add 40% to it to provide for suppressed load and

spinning reserve. Therefore the country should be generating 19,600MW if we are to match

load demand and supply to achieve a stable supply system. For the purpose of long-term

planning, the peak load demand for the industry by 2050 could be 40,000MW. Adding 40%



for suppressed and spinning reserve would give 56,000MW which should be the peak

generation by the year 2050. It should be noted that the installed capacity should be much

higher given the contingencies that are prevalent in tropical environments.

Fig 2.3 also gives a peak demand growth per year of 1000MW. This implies that after the

power industry planners and operators have achieved a healthy power industry, i.e

generating 20,600MW by the year 2015, effort should be made to increase the generated

power by 1000MW every year and it is only at this point in time that control engineers at

various power stations will be concern with methods on how to economically generate

power to match supply with demand.

Fig. 2.1: Load Demand Curve Up To Year 2060 (C.C. Okoro)

Accurate load forecast is very important in planning as it ensures the availability of

supply of electricity, as well as providing the means of avoiding over- and under- utilization



of generating capacity. Errors in forecasting can lead to bad planning which can be

costly(A.O Ibe).

2.3 ECONOMIC GENERATION SCHEDULING OF POWER PLANTS

The operation planning of a power system is characterized by maintaining a high degree of

economy and reliability (P.S. Kannan et al, 2002). Engineers have been very successful in

increasing the efficiency of boilers, turbines and generators so continuously that each new

unit added to the generating unit plants of a system operates more efficiently than any older

unit on the system (Sarangi, 2009). In operating the system for any load condition the

contribution from each plant and from each unit within a plant must be determined so that

the cost of the delivered power is a minimum. Cost equations are obtained from the heat rate

characteristics of the generating machine which gives different generating cost at any load

(S.Pandian and K. Thanushkodi, 2010). So there should be a proper scheduling of plants for

the minimization of cost of operation.

Two major decisions must be made when scheduling the operation of a power generating

system over a short time horizon. First, the "unit commitment" decision indicates what

generating units are to be in use at each point in time over the scheduling horizon. This

decision must take into consideration system capacity requirements and the economic

implications of starting up or shutting down various steam turbines. The "economic

dispatch" decision is the allocation of the demand for power or system load among the

generating units in operation at any point in time. The optimal allocation of load among the

units depends on the relative efficiencies of the units. The nature of the scheduling problem



requires the simultaneous consideration of unit commitment and economic dispatch

decisions to achieve a least cost solution (J.A Muckstadt and S.A Koenig 1977).

The capacities, costs, and operating constraints vary greatly among the various generating

units that are found in any power system. Each unit is designed such that, when it is

committed to operation, the unit's output must be between its minimum and maximum

operating capacities.

The total cost of generation is a function of the individual generation of the sources which

can take values within certain constraint, the cost of generation will depend on the system

constraint for a particular load demand. This means that the cost of generation is not fixed

for a particular load demand but depends upon the operating constraints of the sources. In

fact, the modern power system has to operate under various system constraints.

2.4 SYSTEM CONSTRAINTS:

Broadly speaking there are two types of constraints

i) Equality constraints

ii) Inequality constraints

The inequality constraints are of two types (i) Hard type and, (ii) Soft type. The hard type

are those which are definite and specific like the tapping range of an on load tap changing

transformer whereas soft type are those which have some flexibility associated with them

like the nodal voltages and phase angles between the nodal voltages, etc. Soft inequality

constraints have been very efficiently handled by penalty function methods (Wadhwa,

1995).



2.2.1 EQUALITY CONSTRAINTS

From observation we can conclude that cost function is not affected by the reactive power

demand. So the full attention is given to the real power balance in the system. Power

balance requires that the controlled generation variables Pi obey the constraints equation.

n

i

i DPP

1 (2.1)

Where PD is load demand and N is the number of generators

2.2.2 INEQUALITY CONSTRAINTS:

Generator Constraints:

The KVA loading in a generator is given by P2 + Q2 and this should not exceed a pre-

specified value of power because of the temperature rise conditions. The maximum active

power generation of a source is limited again by thermal consideration and also minimum

power generation is limited by the flame instability of a boiler. If the power output of a

generator for optimum operation of the system is less than a pre-specified value Pmin, the

unit is not put on the bus bar because it is not possible to generate that low value of power

from the unit. Hence the generator power P cannot be outside the range stated by the

inequality

P min ≤ P ≤ P max (2.2)

Similarly the maximum and minimum reactive power generation of a source is limited. The

maximum reactive power is limited because of overheating of rotor and minimum is limited



because of the stability limit of machine. Hence the generator powers Pp cannot be outside

the range stated by inequality, i.e.

Q p min ≤ Q P ≤ Q p max (2.3)

Voltage Constraints:

It is essential that the voltage magnitudes and phase angles at various nodes should vary

within certain limits. The normal operating angle of transmission, lies between 30 to 45

degrees for transient stability reasons. A lower limit of delta assures proper utilization of

transmission capacity.

Running Spare Capacity Constraints:

These constraints are required to meet:

a) The forced outages of one or more alternators on the system and

b) The unexpected load on the system

The total generation should be such that in addition to meeting load demand and losses a

minimum spare capacity should be available i.e.

G ≥ Pp + PSO (2.4)

Where G is the total generation and PSO is some pre-specified power. A well planned system

is one in which this spare capacity PSO is minimum.

Transmission Line Constraints:

The flow of active and reactive power through the transmission line circuit is limited by the

thermal capability of the circuit and is expressed as.

Cp ≤ Cp max (2.5)



Where Cp max is the maximum loading capacity of the pth

line.

Transformer taps settings:

If an auto-transformer is used, the minimum tap setting could be zero and the maximum tap

setting could be one, i.e.

0 ≤ t ≤ 1.0 (2.6)

Similarly for a two winding transformer if tapping are provided on the secondary side,

0 ≤ t ≤ n (2.7)

Where n is the ratio of transformation.

Network security constraints:

If initially a system is operating satisfactorily and there is an outage, may be scheduled or

forced one, it is natural that some of the constraints of the system will be violated. The

complexity of these constraints (in terms of number of constraints) is increased when a large

system is under study. In this, a study is to be made with outage of one branch at a time and

then more than one branch at a time. The natures of constraints are same as voltage and

transmission line constraints.

2.5 REVIEW OF SEVERAL METHODS OF SOLVING ELD PROBLEMS

A bibliographical survey on ELD methods reveals that various numerical optimization

techniques have been employed to approach the ELD problem.



ELD is solved traditionally using mathematical programming based on optimization

techniques such as lambda iteration, gradient method and so on. Complex constrained ELD

is addressed by intelligent methods. Among these methods, some of them are genetic

algorithm (GA), evolutionary programming (EP), dynamic programming (DP), tabu search,

hybrid EP, neural network (NN), adaptive Hopfield neural network (AHNN), particle

swarm optimization (PSO) etc. Some of the various approaches used to solve ELD problems

are as summarized as follows:

2.3.1 THE LAMBDA – ITERATION METHOD:

In Lambda iteration method lambda is the variable introduced in solving constraint

optimization problem and is called Lagrange multiplier. It is important to note that lambda

can be solved at hand by solving systems of equation. Since all the inequality constraints to

be satisfied in each trial the equations are solved by the iterative method

i) Assume a suitable value of λ(0)

this value should be more than the largest intercept of the

incremental cost characteristic of the various generators.

ii) Compute the individual generations

iii) Check the equality

N

i

i D PP1

is satisfied. (2.8)

iv) If not, make the second guess λ and repeat above steps

Demerits of the Lambda – Iteration Method

Under some initial starting points, the lambda-iteration approach exhibits an oscillatory

behavior, resulting in a non converging solution



2.3.2 THE GRADIENT SEARCH METHOD:

This method works on the principle that the minimum of a function, f(x), can be found by a

series of steps that always take us in a downward direction. From any starting point, x0, we

may find the direction of “steepest descent” by noting that the gradient f,

)9.2(

1

n x

f

x f

f

always points in the direction of maximum ascent. Therefore, if we want to move in the

direction of maximum descent, we negate the gradient. Then we should go from x0

to x1

using:

f X X 01

(2.10)

Where α is a scalar to allow us to guarantee a process of convergence. The best value of α

must be determined by experiment

In case of power system economic load dispatch f becomes

)11.2(1

N

i

ii PF f

The object is to drive the function to its minimum. However we have to be concerned with

the constraints function



N

i

iload PP1

)(

(2.12)

To solve the economic load dispatch problem which involves minimizing the objective

function and keeping the equality constraints, we must apply the gradient technique directly

to the

Lagrange function:

)13.2()()(11

N

i

iload

N

i

ii PPPF

And the gradient of this function is )14.2(

1

Pn

P

The economic dispatch algorithm requires a starting value and starting values for P1,P2,

and P3 .The gradient for ℑ is calculated as above and the new values of ,P1, and P2 etc,

are found from

X1

= X0 – ( ℑ) α (2.15)

Where X is a vector

2

1

P

P

X





The equation (2.19) is a set of n equations with (n+1) unknowns. Here n generations are

unknown and λ is also unknown. These equations are known as coordination equations

because they coordinate the incremental transmission losses with the incremental cost of

production.

To solve these equations, the loss formula is expressed in terms of generations and is

approximately expressed as;

00

1 1 1

0 BP BP BPP i

n

i

n

j

n

i

i jiji L (2.20)

Where Pi and P j are the source loadings, Bij the transmission loss coefficients. The

Algorithm of the classical kirchmayer method is as follows:

1. Start

2. Read the constants ai, bi, loss coefficient matrices Bij, B0i, and B00, Power demand

PD, maximum P

imax, minimum P

imingenerators real power limits.

3. Assume a suitable value of λ. This value should be greater than the largest

intercept of the incremental cost of the various units. Calculate P1, P2,…..,P6

based on equal incremental cost.

4. Calculate the generation at all buses using:

iii

n

ji

jiji

i

i

Ba

P Bb B

P

22

210



Keeping in mind that the values of power to be substituted on the RHS in the

above equation during the zeroth iteration correspond to the values calculated in

step 3.For subsequent iterations, the values of power to be substituted corresponds

to the power of the previous iteration. However if any generator violates the limit

of generation, that generator is fixed at the limit violated.

5. Check if the difference in the power at all generator buses between two

consecutive iterations is less than the specified value, otherwise go back to step 4.

6. Calculate the losses using the relation;

00

1 1 1

0BP BP BPP i

n

i

n

j

n

i

i jiji L

7. Calculate,

)( D LGPPPP

8. If ΔP is less than a specified value ε, stop calculation and calculate the cost of

generation with values of powers. If ΔP < ε is not satisfied go to step 7

9. Update λ as λn+1

= λ(n)

-Δ λ(n)

where Δ λ is the step size.

10. Stop.



Yes

No Yes

No

Yes

Start

Read in ai, bi, PD, Pimin, Pimax, Bij, B0i, B00

Assume a suitable value of λ

Determine Pi corresponding to incremental cost of

production

Set K = 0

Set n=1

Solve for Pi

ii

i

n

ji

jij

i

i

i

Ba

P Bb

B

P

22

21 0

If Pi > Pimax

If Pi < Pimin

n=n+1

Check if all buses

have been accounted

1n

i

n

i PP

Pi = Pimax

Pi = Pimin

K=K+1



Yes

` Yes

No

No

Yes

Fig. 2.2: Flow chart for the classical Kirchmayer method.

Demerits of Classical Kirchmayer Method

Solving economic load dispatch problem using the classical kirchmayer method could be

very time consuming in a large interconnected system.

2.3.4 NEWTON’S METHOD:

Newton‟s method goes a step beyond the simple gradient method and tries to solve the

economic dispatch by observing that the aim is to always drive

0 x (2.25)

Calculate

)(

1 1

000

D LG

n

i

n n

i

ii jiji L

PPPPand

BP BP BPP

If ΔP≤ε

)( D LG PPPP

Print generation and

Calculate cost of generation

Is ΔP >0

= λ- Δλ

=λ+Δλ



Since this is a vector function, we can formulate the problem as one of finding the

correction that exactly drives the gradient to zero (i.e. to a vector, all of whose elements are

zero).Suppose we wish to drive the function g(x) to zero. The function g is a vector and the

unknown, x are also vectors. Then to use Newton‟s method, we observe

g(x+Δx)=g(x)+[g‟(x)] Δx=0 (2.26)

Where g‟(x) is the familiar Jacobian matrix. The adjustment at each step is then

Δ X = −[g ' ( x)]−1 g( x) (2.27)

Now, if we let the g function be the gradient vector x we get

)28.2(

1

x x

X

For the economic load dispatch problem this takes the form:

N

i

N

i

iload ii PPPF 1 1

)()(

(2.29)

The x ∇ψ is a Jacobean matrix which has now second order derivatives is called Hessian

matrix. Generally, Newton‟s method will solve for the correction that is much closer to the

minimum generation cost in one cost in one step than would the gradient method.

2.3.5 LINEAR PROGRAMMING:

Linear programming (LP) is a technique for optimization of a linear objective function

subject to linear equality and linear in-equality constraints. Informally, linear programming

determines the way to achieve the best outcome (such as maximum profit or lowest cost) in

a given mathematical model and given some list of requirements represented as linear

equations. For example if f is function defined as follows



d xc xc xc x x x f nnn

....),....,(221121 (2.30)

A linear programming method will find a point in the optimization surface where this

function has the smallest (or largest) value. Such points may not exist, but if they do,

searching through the optimization surface vertices is guaranteed to find at least one of

them. Linear programs are problems that can be expressed in canonical form

Maximize C T

X

Subject to AX ≤ b

X represents the vector of variables (to be determined), while C and b are vectors of

(known) coefficients and A is a (known) matrix of coefficients. The expression to be

maximized or minimized is called the objective function (cT

in this case). The equations AX

≤ b are the constraints which specify a convex polyhedron over which the objective function

is to be optimized.

2.3.6 PARTICLE SWARM OPTIMIZATION METHOD

The Particle Swarm Optimization (PSO) method is a member of wide category of swarm

intelligence methods for solving the optimization problems. The origin of PSO is described

as sociologically inspired, since it is based on the sociological behavior associated with bird

flocking. It is a population based search algorithm where each individual is referred to as

particle and represents a candidate solution. Each particle in PSO flies through the search

space with an adaptable velocity that is dynamically modified according to its own flying

experience and also to the flying experience of the other particles. In PSO each particles

strive to improve themselves by imitating traits from their successful peers.



Further, each particle has a memory and hence it is capable of remembering the best

position in the search space ever visited by it. The position corresponding to the best fitness

is known as pbest and the overall best out of all the particles in the population is called gbest

(J. Kennedy, R. Eberhart, 2001).

2.3.8 GENETIC ALGORITHM

Genetic Algorithm (GA) can be viewed as a general purpose search method, an optimization

method, or a learning mechanism, based loosely on Darwinian principles of biological

evolution, reproduction and “the survival of the fittest.” GA maintains a set of candidate

solutions called population and repeatedly modifies them. At each step, the GA selects

individuals at random from the current population to be parents and uses them to produce

the children for the next generation. Candidate solutions are usually represented as strings of

fixed length, called chromosomes (D. E. Goldberg, 1989).

GA can be applied to solve a variety of optimization problems that are not well suited for

standard optimization algorithms, including problems in which the objective function is

discontinuous, non-differentiable, stochastic, or highly nonlinear. GA has been used to solve

difficult engineering problems that are complex and difficult to solve by conventional

optimization methods (Danraj& Gajendran, 2004).

2.3.9 ARTIFICIAL NEURAL NETWORKS

Neural networks are composed of simple elements operating in parallel. These elements are

inspired by biological nervous systems. As in nature, the connections between elements



largely determine the network function. You can train a neural network to perform a

particular function by adjusting the values of the connections (weights) between elements.

Typically, neural networks are adjusted, or trained, so that a particular input leads to a

specific target output. The network is adjusted, based on a comparison of the output and the

target, until the network output matches the target. Typically, many such input/target pairs

are needed to train a network

Figure 2.1 Artificial Neural Networks

A feed-forward neural network based on the supervised back propagation learning algorithm

is used to implement the economic scheduling of power plants. The Feed-forward neural

network consists of an input layer representing the input data to the network, some hidden

layers and an output layer representing the response of the network. Each layer consists of a

certain number of neurons, each neuron is connected to other neurons of the previous layer

NEURAL NETWORK

Including Connections

(Called weights)

COMPARE

Target

Input

AdjustWeights

OUTPUT



through adaptable synaptic weights w and biases. If the inputs of neuron are the variables

P1, P2. . . PR, then the output of the neuron is obtained as follows:

a=f (wp+b) (2.31)

Where; „w‟ represents the weight of the connection between the neuron and the input „p‟, „b‟

represents the bias of neuron and „ f’ is the transfer function (activation function) of the

neuron. Multilayer networks often use the log sigmoid transfer function (logsig).

A feed-forward neural network of three layers is considered, input, hidden and output

layers, respectively. The input patterns of the neural network is represented by a vector of

variables (P1, P2. . . PR) submitted to the ANN by the input layer are transferred to the

hidden layer. Using the weight of the connection between the input and the hidden layer,

and the bias of the hidden layer, the output vector is then determined. Training is the process

of adjusting connection weights w and biases b. In the first step, the network outputs and the

difference between the actual (obtained) output and the desired (target) output (i.e., the

error) is calculated for the initialized weights and biases (arbitrary values). During the

second stage, the initialized weights in all links and biases in all neurons are adjusted to

minimize the error by propagating the error backwards (the back-propagation algorithm).

The network outputs and the error are calculated again with the adapted weights and biases,

and the process (the training of the Artificial Neural Network) is repeated at each epoch

until a satisfied output (corresponding to the values of the input variables is obtained and the

error is acceptably small.



Once the network is trained with the algorithm and appropriate weights and biases are

selected, they can be used in the test to identify the output pattern given an appropriate input

pattern. The training is performed off line resulting in reduced on-line computations. The

design process of the ANN economic load dispatch goes through the following steps:

Preparation of a suitable training data set that represents cases the Neural Network

needs to learn

Selection of a suitable Neural Network structure for a given application.

Training the Neural Network.

Evaluation of the trained Neural Network using test patterns until its performance is

satisfactory.

Advantages of artificial neural network

A neural network can perform tasks that a linear program can not.

When an element of the neural network fails, it can continue without any problem by

their parallel nature.

A neural network learns and does not need to be reprogrammed.

It can be implemented in any application.

It can be implemented without any problem.

Disadvantages of artificial neural network

The neural network needs training to operate.

The architecture of a neural network is different from the architecture of

microprocessors therefore needs to be emulated.



Requires high processing time for large neural networks.

2.6 POWER WORLD SIMULATOR

Power World Simulator is a power system simulation package designed from the ground up

to be user-friendly and highly interactive. It has the power for serious engineering analysis,

but it is also so interactive and graphical that it can be used to explain power system

operations to non-technical audiences. It has a comprehensive, robust Power Flow Solution

engine capable of efficiently solving systems of up to 60,000 buses. It allows users to design

and simulate a power system network using one-line diagrams via the interconnection of

buses, transmission lines, transformers, generators and so on.

2.7 MATLAB

Matlab is an interactive software package which was developed to perform numerical

calculations on vectors and matrices. It can do quite sophisticated graphics in two and three

dimensions, it contains a high-level programming language which makes it quite easy to

code complicated algorithms involving vectors and matrices, it can numerically solve

nonlinear initial-value ordinary differential equations and above all, it contains a wide

variety of toolboxes including the neural network toolbox which allow it to perform a wide

range of applications from science and engineering.

Mathematics is the basic building block of science and engineering, and MATLAB makes it

easy to handle many of the computations involved. It was designed to group large amounts

of data in arrays and to perform mathematical operations on this data as individual arrays



rather than as groups of data. This makes it very easy to apply complicated operations to the

data, and it makes it very difficult to do it wrong.

2.8 SUMMARY OF REVIEW

The economic generation scheduling of thermal power plants in power system and the

Nigeria‟s energy crisis were discussed in this chapter. When the problem is to be solved,

few constraints have to be kept in mind. Different types of constraints were discussed in this

chapter. Also, various methods applied to solve the economic load dispatch problem were

discussed.



CHAPTER THREE

METHODOLOGY

3.1 ECONOMIC DISPATCH INCLUDING LOSSES

The Economic Load Dispatch (ELD) involves generating adequate electricity to meet

the continuously varying consumer load demand at the least possible cost under a

number of constraints. Practically, while the scheduled combination of units at each

specific period of operation are listed, the ELD planning must perform the optimal

generation dispatch among the operating units to satisfy the load demand, spinning

reserve capacity, and practical operation constraints of generators. When transmission

distances are very small and load density is very high, transmission losses may be

neglected and the optimal dispatch of generation is achieved with all plants operating

at equal incremental production cost. However in a large interconnected network

where power is transmitted over long distances with low load density areas,

transmission losses are a major factor and affect the optimum dispatch of generation.

Mathematically, ELD can be represented as;

N

i

ii PF F Min

1

)(

(3.1)

2)( iiiiiii PcPbaPF

(3.2)

Subject to:



N

i

i L D PPP1

0

With P i, min ≤ P i ≤ P i, max

Where

)3.3(00

1 1 1

0BP BP BPP i

n

i

n

j

n

i

i jiji L

F is the system overall cost function

N = the number of generators in the system

ci is a measure of losses in the system, bi is the fuel cost and ai is the salary and wages,

interest and depreciation.

P D=the total power system demand

P L= the total system transmission losses

Pi= the active power generation of generator number i

Bij, B0i, B00= Transmission loss coefficients



The coefficients Bij in eqn. (3.3) are the loss coefficients called B-coefficients and for

an n generator system, the coefficient is an n×n symmetric matrix

jj j j

j

j

ij

B B B

B B B

B B B

B

21

22221

11211

The diagonal elements are all positive and strong as compared with the off diagonal

elements which mostly are negative and are relatively weaker. These coefficients are

determined for a large system by an elaborate computer programme starting from the

assembly of the open circuit impedance matrix of the transmission network which is

quite lengthy and time consuming and is beyond the scope of this project. Besides, the

formulations of the B-coefficients are based on several assumptions and do not take

into account the actual conditions of the system.

B-coefficients have been developed by applying tensors to power system wherein the

interconnected system is reduced to one with sources equal to the actual number of

sources but loads equal to one hypothetical load. These are considered constants and

reasonable accuracy can be expected provided the actual operating conditions are

close to the base case where the B constants were computed.



3.2 DEMONSTRATION OF THE ECONOMIC LOAD DISPATCH PROBLEM

The network of Nigeria power system shown in fig 3.1is considered to demonstrate

the Economic Load Dispatch problem.

figure 3. 1 Map of the Nigeria national grid

This power system is designed and analysed with the aid of Power World Simulator

(PWS) as shown in fig. 3.2.

Each power station in the power system shown in fig. 3.2 has its individual generating

characteristics which are different from those of other stations. The generating



characteristics of the various power stations (comprising of the unit cost function,

generator real power limit) are the functions for determining the optimum operation of

the power stations. Unfortunately, cost functions for determining the optimum loading

of power plants in Nigeria‟s power networks are not available (C.C. Okoro). In the

course of this project, arbitrary values were used to formulate the cost functions of the

various power plants in the networks. These values are given in table 3.1. The

installed generating capacity of the power stations and the transmission line

parameters used in designing the power system is shown in table 3.2 and 3.3

respectively.

Figure 3. 2 One line diagram of the National Grid



Table 3.1: Unit cost function

S/N Power Stations Cost Functions(Fi)

a b c

1 Jebba PS 250 4.80 0.00752 Egbin 600 6.00 0.0050

3 Kainji 240 5.00 0.0070

4 Shiroro 140 5.60 0.0065

5 Sapele 450 6.30 0.0050

6 Delta IV 300 6.00 0.0057

7 A.E.S 250 7.00 0.0090

8 Calabar 190 8.50 0.0125

9 Afam 561 6.92 0.0036

10 Oji 200 7.70 0.0200

11 Ijora 220 8.00 0.0098

12 Omotosho 280 6.50 0.007913 Geregu 270 7.00 0.0078

14 Omoku 300 6.80 0.0180

Recall from equation 3.2 that the unit cost function is given by;

2

iiiiii PcPbaF

Table 3.2: Installed Generating Capacity.

S/N Power Stations Installed Capacity(MW)

1 Jebba 540.0

2 Egbin 1320.0

3 Kainji 760.0

4 Shiroro 600.0

5 Sapele 1020.0

6 Delta IV 600.0

7 A.E.S 270.08 Calabar 6.6

9 Afam 987.0

10 Oji 30.0

11 Ijora 60.0

12 Omotosho 335.0

13 Geregu 300.0

14 Omoku 100.0



Table 3.3: Transmission Line Parameters

S/N From Number From Name To Number To Name Circuit Status R(Ω) X(H) C(F)

1 10 KAINJI 1 B. KEBBI 1 Closed 0.0122 0.0916 1.2178

2 2 JEBBA PS 3 JEBBA 1 Closed 0.0001 0.0007 0.0416

3 2 JEBBA PS 3 JEBBA 2 Closed 0.0001 0.0007 0.0416

4 3 JEBBA 4 OSHOGBO 1 Closed 0.0020 0.0154 1.8384



7 10 KAINJI 3 JEBBA 1 Closed 0.0015 0.0113 0.6726

8 10 KAINJI 3 JEBBA 2 Closed 0.0015 0.0113 0.6726

9 15 SHIRORO 3 JEBBA 1 Closed 0.0045 0.0342 2.0424

10 15 SHIRORO 3 JEBBA 2 Closed 0.0045 0.0342 2.0424

11 4 OSHOGBO 5 AIYEDE 1 Closed 0.0045 0.0345 0.4518

12 4 OSHOGBO 6 IKJ WEST 1 Closed 0.0099 0.0745 0.9900

13 4 OSHOGBO 18 BENIN 1 Closed 0.0099 0.0742 0.9864

14 4 OSHOGBO 30 IBADAN N 1 Closed 0.0089 0.0162 0.8967

15 5 AIYEDE 6 IKJ WEST 1 Closed 0.0054 0.0405 0.5382

16 5 AIYEDE 29 IJORA 1 Closed 0.0013 0.0137 0.8235

17 5 AIYEDE 30 IBADAN N 1 Closed 0.0041 0.0818 1.8712

18 6 IKJ WEST 7 AKANGBA 1 Closed 0.0004 0.0027 0.1414

19 6 IKJ WEST 8 EGBIN PS 1 Closed 0.0011 0.0086 0.5148

20 8 EGBIN PS 6 IKJ WEST 2 Closed 0.0011 0.0086 0.514821 6 IKJ WEST 18 BENIN 1 Closed 0.0051 0.0039 2.3248

22 6 IKJ WEST 18 BENIN 2 Closed 0.0051 0.0039 2.3248

23 6 IKJ WEST 29 IJORA 1 Closed 0.0026 0.0419 2.0567

24 6 IKJ WEST 35 OMOTOSHO 1 Closed 0.0221 0.0331 1.0976

25 8 EGBIN PS 9 AJA 1 Closed 0.0003 0.0019 0.1162

26 8 EGBIN PS 9 AJA 2 Closed 0.0003 0.0019 0.1162

27 18 BENIN 8 EGBIN PS 1 Closed 0.0068 0.1095 1.9820

28 8 EGBIN PS 22 A.E.S 1 Closed 0.0200 0.1000 0.5000

29 12 KADUNA 11 KANO 1 Closed 0.0090 0.0680 0.9036

30 12 KADUNA 13 JOS 1 Closed 0.0081 0.0609 0.8092

31 15 SHIRORO 12 KADUNA 1 Closed 0.0017 0.0132 0.7380

32 15 SHIRORO 12 KADUNA 2 Closed 0.0017 0.0132 0.7380

33 13 JOS 14 GOMBE 1 Closed 0.0118 0.0887 1.1786

34 38 MAKURDI 13 JOS 1 Open 0.0021 0.1976 1.0934

35 14 GOMBE 37 DAMATURU 1 Open 0.0228 0.1231 1.4630

36 15 SHIRORO 16 KATAMPE 1 Closed 0.0025 0.0190 0.6442

37 15 SHIRORO 16 KATAMPE 2 Closed 0.0025 0.0195 0.6442

38 18 BENIN 17 AJAOKUTA 1 Closed 0.0035 0.0271 1.6190

39 17 AJAOKUTA 18 BENIN 2 Closed 0.0035 0.0271 1.6190

40 17 AJAOKUTA 36 GEREGU 1 Closed 0.0056 0.1638 2.0975

41 17 AJAOKUTA 41 LOKOJA 1 Open 0.0019 0.1417 0.1147

42 18 BENIN 19 SAPELE 1 Closed 0.0009 0.0070 0.4156

43 18 BENIN 19 SAPELE 2 Closed 0.0009 0.0070 0.4156

44 21 DELTA IV 18 BENIN 1 Closed 0.0042 0.0316 0.4204

45 18 BENIN 23 ONITSHA 1 Closed 0.0054 0.0405 0.5382

46 18 BENIN 35 OMOTOSHO 1 Closed 0.6430 0.1564 1.7840

47 19 SAPELE 20 ALADJA 1 Closed 0.0025 0.0186 0.2474

48 21 DELTA IV 20 ALADJA 1 Closed 0.0009 0.0072 0.2158

49 23 ONITSHA 24 NEW H. 1 Closed 0.0038 0.0284 0.3772

50 25 ALAOJI 23 ONITSHA 1 Closed 0.0129 0.1963 2.7960

51 40 OWERI 23 ONITSHA 1 Closed 0.0045 0.0651 1.8070

52 24 NEW H. 25 ALAOJI 1 Closed 0.0054 0.0408 0.5422

53 28 OJI 24 NEW H. 1 Closed 0.0005 0.0042 0.249054 24 NEW H. 39 ALIADE 1 Open 0.0073 0.1284 1.0967

55 26 CALABAR 25 ALAOJI 1 Closed 0.0200 0.1000 0.4000

56 27 AFAM GS 25 ALAOJI 1 Closed 0.0006 0.0043 0.2574

57 25 ALAOJI 32 IKOT EKP 1 Open 0.0670 0.1089 2.0340

58 25 ALAOJI 40 OWERI 1 Open 0.0920 0.1007 1.0981

59 32 IKOT EKP 26 CALABAR 1 Open 0.0480 0.1086 2.0789

60 27 AFAM GS 31 P.H 1 Open 0.0710 0.1679 1.0863

61 27 AFAM GS 32 IKOT EKP 1 Open 0.0020 0.1783 1.0956

62 39 ALIADE 38 MAKURDI 1 Open 0.0760 0.1200 0.9883

63 43 EGBEMA 40 OWERI 1 Closed 0.0099 0.0742 0.9864

64 42 OMOKU 43 EGBEMA 1 Closed 0.0011 0.0086 0.5148



3.3 DESIGN PROCEDURE OF THE POWER SYSTEM NETWORK USING

PWS.

3.3.1 Creating a New Case

From the File menu select New Case. At any point of the development of this case,

you can save your work by selecting Save Case (or Save Case as …) from the File

menu.

3.3.2 Inserting a Bus

From the Insert menu select Bus or click on the button in the “Insert” toolbar.

Click anywhere in the drawing and the following dialog box should appear:

Fig. 3.2: Bus option dialog box.



Use the bus dialog box to specify the name, size, orientation, area, zone and

nominal voltage of the bus.

Click OK on the bus option dialog to finish creating the bus and to close the

dialog.

3.3.3 Inserting a Generator

From the Insert menu select Generator or click on the button in the “Insert”

toolbar.

Left click the bus on the one line diagram to which you want to attach the

generator. This brings up the Generator option dialog.

Fig. 3.3: Generation option dialog box



3.3.4 Inserting a Transmission line

From the Insert menu select Transmission Line or click on the button in the

“Insert” toolbar.

Click on the bus you want the transmission line to originate from and double click

on the bus where the line should terminate. The following dialog bus will then

appear:

Fig. 3.4: Transmission Line option dialog box.

Insert the parameters R, X , and C in p.u. These values for the various bus

connections are given in table 3.2.



Click OK. A transmission line with two circuit breakers on each side and a line

flow pie chart in the middle should appear in the one line diagram.

3.3.5 Inserting a load

From the Insert menu select Load or click on the button in the “Insert” toolbar.

Click on the bus where the load should exist. The following dialog should appear:

Fig 3.5: Load option dialog box.

Click ok to close the dialog box.



3.3.6 Running a Case

In order to simulate the case that we have designed, we select the Run Mode from

the toolbar below the menu.

Right click on the one line diagram and select the area information dialog option.

The following dialog box will appear:

Fig. 3.6: Area Information dialog box

Select the Economic Dispatch control option and then click OK.

When you run the simulator, you will observe that the sum of the MW output of the

various plants is equal to the sum of the total area load and the total transmission loss.



3.4 TRAINING THE ARTIFICIAL NEURAL NETWORK

Click on Matlab‟s icon to open a blank command window

Type „edit‟ on the command window to open an editor window as shown below;

Fig. 3.7: User interface showing the editor window

The algorithm to train the neural network is typed on the editor window as shown

in figure 3.8. This program calls up the neural network tool and analyses the total

area load demand as the input to the neural network. The electric power generation

of the six thermal power plants, the total system transmission losses and the total

hourly cost are taken as the output of the neural network.



Fig. 3.8: User interface showing the training algorithm.

For the purpose of training the neural network, data were obtained from the

simulated results from Power World Simulator at fourteen different total area load

demand to ensure a fast learning rate and ability to produce correct output when

fed with a different input. These data are shown in appendix…..

After preparing adequate data for training and test of neural networks, now the

important key is selecting the number of neurons in the hidden layer of the

networks such that the exactness of network is maximum. For this reason, the

neural network is trained with five neurons in the hidden layer and a neuron in the

output layer. A number of 200 epochs is considered. The Tan-Sigmoid Transfer



Function was used in the hidden layer while the Linear Transfer Function was used

in the output layer. Also, the default Levenberg-Marquardt algorithm (trainlm) was

adopted to achieve a better training speed.

The figures of the untrained system, the system performance and the output of the

trained system are obtained after the program has been run for analysis by clicking

on the run icon.

The figures of the untrained system, the system performance and the output of the

trained system are obtained after the program has been run for analysis by clicking

on the run icon.

Fig. 3.9: User interface showing the system performance, the untrained output and

the trained output.





CHAPTER FOUR

SIMULATION AND DISCUSSION OF RESULTS

In order to assess the effectiveness and robustness of the proposed Artificial Neural

Network method, the Nigeria power system network with limitations to 14 generating

units and 29 load centres have been considered. The simulated results from Power

World Simulator (PWS) and artificial neural network‟s response to these data are

tabulated in appendix I and II respective. A comparism of PWS and ANN trained

generation output of Jebba hydro plant and Sapele thermal plants are as shown in table

4.1and 4.2 and fig.4.1 and 4.2 gives their respective graphical relationships.

Table 4.1: ANN‟s Response to PWS Generation Output of Jebba Plant

S/N Total Area Load(MW) PWS(MW) ANN(MW)

1 1750.00 228.29 228.81

2 2000.00 247.89 246.98

3 2250.00 266.97 267.05

4 2500.00 287.61 287.92

5 2700.00 304.00 304.39

6 3000.00 327.52 327.76

7 4000.00 407.07 407.88

8 4250.00 431.46 431.14

9 5500.00 540.00 539.00

10 6000.00 540.00 540.85



Fig. 4.1: A graphical relationship between PWS and ANN Gen. Output of Jebba Plant.

Table 4.1: ANN‟s Response to PWS Generation Output of Sapele Plant

S/N Total Area Load(MW) PWS(MW) ANN(MW)

1 1750.00 192.14 190.70

2 2000.00 215.47 216.55

3 2250.00 243.81 243.74

4 2500.00 272.40 272.08

5 2700.00 295.08 295.45

6 3000.00 332.48 331.40

7 4000.00 455.67 454.56

8 4250.00 484.15 485.51

9 5500.00 663.83 665.21

10 6000.00 791.80 791.99

0.00

100.00

200.00

300.00

400.00

500.00

600.00

1 2 3 4 5 6 7 8 9 10

PWS(MW)

ANN(MW)



Fig.4.2:A graphical relationship between PWS and ANN Gen. Output of Sapele Plant.

It can be inferred from the plots above that the Artificial Neural Network program

possesses a learning ability and can adapt to recognize learned patterns of behavior in

the electric power system, where exact functional relationships are neither well

defined nor easily computable.

EFFECTS OF FUEL CHOICE TO GENERATION COST

The effect of fuel choice on cost of generation can be evaluated from the cost function

equation. Considering the generations at different load demands of an hydro (Shiroro)

and thermal (DeltaIV) plant with equal installed capacity of 600MW and cost function

equation given in equations 4.1 and 4.2 respectively, the results obtained are given in

table 4.3 and a graphical comparism is as shown in fig 4.3.

CS = 140 + 5.6PS + 0.0065PS2

(4.1)

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

900.00

1 2 3 4 5 6 7 8 9 10

PWS(MW)

ANN(MW)



CD = 300 + 6PD + 0.0057PD2

(4.2)

Table 4.3 Hourly cost for Shiroro and Delta IV power plants

Total Area Load(MW) Shiroro(MW)Shiroro Hourly Cost ($/hr) Delta IV (MW) Delta IV Hourly Cost ($/hr)

1500.00 176.04 1327.26 170.69 1490.21

1750.00 202.18 1537.91 193.70 1676.06

2000.00 224.75 1726.93 212.16 1829.53

2250.00 246.17 1912.45 236.89 2041.21

2500.00 270.16 2127.31 260.94 2253.75

2700.00 293.23 2340.98 283.90 2462.82

3000.00 317.02 2568.57 315.75 2762.78

3700.00 383.50 3243.57 387.92 3485.27

4000.00 406.44 3489.82 420.61 3832.06

Fig.4.3: A graphical comparism of generation cost of a thermal and hydro plant

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

3500.00

4000.00

4500.00

1 2 3 4 5 6 7 8 9

H o u r l y c o s t ( $ / h r )

Comparism of Hourly cost of generation of

Hydro and thermal plants

Shiroro Hourly Cost ($/hr)

Delta IV Hourly Cost ($/hr)



From fig.4.3, it can be seen that the hourly cost of generation of a thermal plant is

higher than that of a hydro plant hence it is more economical to operate a hydro plant

than a thermal plant.

Hydro electric power is the cheapest way to generate electricity, no other energy

source, renewable or non renewable can match it. Once a dam has been built and the

equipment installed, the energy source (water) is free. From PHCN‟s most recent

estimate, the country‟s outstanding total exploitable hydro potential, listed in Table

4.5, currently stands at 12,220 MW. Added to the 1930 MW (Kainji, Jebba and

Shiroro), already developed, the gross hydro potential for the country would be

approximately 14,750 MW. Current hydropower generation is about 14% of the

nation‟s hydropower potential and represents some 30% of total installed grid

connected electricity generation capacity of the country. Power utilities in Nigeria are

predominantly made of thermal plants with only 3 hydro plants. This choice of

thermal plants with gas as their primary source of energy has hindered the realization

of a reliable and stable power system in Nigeria. Apart from the high cost of operation

and short life of gas/thermal stations, Nigeria is known to have a volatile Niger Delta

region militant struggle. Presently, the existing generating stations dependent on gas

are forced to be shut down at one time or the other as a result of vandalisation of gas

pipelines supplying these stations. A comparism of thermal plants and hydro plants

given in table 4.4 will further highlight the factors exacerbating the nation‟s energy

crisis.



Table 4.4 Comparism Thermal and Hydro power plants

THERMAL PLANTS HYDRO PLANTS

1. Less capital intensive to construct.

2. Can be located near the load centre

reducing transmission capital cost and

transmission losses.

3. Maintenance cost high relative to

hydro.

4. Reliability depends on the frequency

of electrical and mechanical failures and

the availability of gas as a Nigerian

factor. Also, integrity of condenser water

intake is another factor.

5. have shorter life span

1. Highly capital intensive to construct.

2. Located where the energy source is

available with need for transmission facility

to evacuate the power.

3. Maintenance cost less relative thermal

plants.

4. Reliability depends on hydraulogical

factors: River flow and storage available.

5.Have longer life span



Table 4.5: PHCN Estimate of Current Exploitable Hydro power plant (Iceed,2006)

S/N LOCATION RIVER

POTENTIAL CAPACITY

(MW)

1 Donka Niger 2252 Zungeru II Kaduna 450

3 Zungeru I Kaduna 500

4 Zurubu Kaduna 20

5 Gwarram Jamaare 30

6 Izom Gurara 10

7 Gudi Mada 40

8 Kafanchan Kongum 5

9 Kurra II Sanga 25

10 Kurra I Sanga 15

11 Richa II Daffo 2512 Richa I Mosari 35

13 Mistakuku Kurra 20

14 Korubo Gongola 35

15 Kiri Gongola 40

16 Yola Benue 360

17 Karamti Kam 115

18 Beli Taraba 240

19 Garin Dali Taraba 135

20 Sarkin Danko Suntai 45

21 Gembu Dongu 13022 Kasimbila Katsina Ala 30

23 Katsina Ala Katsina Ala 260

24 Makurdi Benue 1060

25 Lokoja Niger 1950

26 Onitsha Niger 1050

27 Ifon Osse 30

28 Ikom Cross 730

29 Afikpo Cross 180

30 Atan Cross 180

31 Gurara Gurara 300

32 Mambilla Danga 3960

Total 12,220



CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

Economic load dispatch in electric power sector is an important task, as it is required

to distribute the load among the generating units actually paralleled with the system in

such a manner as to minimise the cost of supplying the minute to minute requirement

of the system which aids in profit-making. In a large interconnected system, it is

humanly impossible to calculate and adjust each generation and hence the help of

digital computer system is being used and the whole process is carried out

automatically. In the course of this work, artificial neural network has been proposed

to determine economic generation scheduling considering transmission losses of

power plants very efficiently and accurately. A trained ANN can be applied to find out

the economical load dispatch pattern for a particular load demand in a fraction of

second. However, ANN algorithms still need further research and development to

improve its performance to obtain the robustness needed to incorporate several other

practical constraints as input-output information of the training sets. Also, methods

can be thought of which reduced the training time. The effect of complexity of the

neural network on the performance of system may also be studied.

Also in the course of this work, it was highlighted that the available energy generated

in Nigeria is not enough to meet the demand of the populace leading to constant load

shedding and blackouts. National economies with secure electricity supply industry



have generating capacities that not only match national load demand but also allow for

spinning reserve and suppressed load. This is only achieved by having robust

generating facilities that involve a very wide energy mix (coal, gas (thermal), hydro,

and nuclear) built on the most advantageous sites. The power industry is highly capital

intensive and so designers and planners must aim to provide the electricity at as cheap

rates as possible and operate the stations in an optimum manner. However, for us to

build a reliable power system with an available generating capacity that can meet up

load demand, losses and spinning reserves, maintainability and sustainability must be

the goal. A good ratio of 60/40 for non-renewable and renewable energy sources is

hereby recommended, which tally with modern energy management and development

worldwide. Moreover, in view of the numerous research inputs needed for optimum

operation of the Nigeria power system, it is recommended that the Power Holding

Company of Nigeria (PHCN) and the Nigerian Electricity Regulatory Commission

(NERC) fund a power systems research centre that would provide the professional

inputs needed to take decisions for regulating and operating a reliable and fast

growing power industry.

Documents

Project Thesis Repaired)