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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
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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
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CHAPTER THREE
METHODOLOGY
CHAPTER FOUR
RESULTS AND DISCUSSION
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
REFERENCES
APPENDICES
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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
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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.
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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,
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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
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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
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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
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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%
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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
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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
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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).
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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
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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)
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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.
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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
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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
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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
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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
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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.
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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
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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
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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
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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.
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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
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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
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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.
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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.
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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
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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.
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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:
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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
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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.
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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
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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
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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
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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
5 3 JEBBA 4 OSHOGBO 2 Closed 0.0020 0.0154 1.8384
6 3 JEBBA 4 OSHOGBO 3 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
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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.
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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
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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.
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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.
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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.
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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.
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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
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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.
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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
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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)
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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)
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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)
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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.
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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
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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
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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
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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.
Recommended