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TRANSCRIPT
Minimization of Energy Loss
using Integrated Evolutionary
Approaches
Attia A. El-Fergany, Member, IEEE,
Mahdi El-Arini, Senior Member, IEEE
Paper Number: 1569614661
Presentation's Outline
� Aim of this work,
� Introduction,
� Methodology & Evolutionary Algorithms,
� Test Scenarios & Results,
� Conclusions,
� Future work.
Aim of this Proposed work
� In this work, a hybridization of Meta-heuristic’s
algorithms that attempts to minimize the real energy
losses including line security have been developed and
proposed. The control variables used in this problem
are:
– AVR operating values of generators (continuous variables),
– Transformer’s LTC tap positions (discrete variables),
– Schedules of power generation output in MW’s (Continuous
variables).
� Effects of changing number of control variables were
discussed and demonstrated
Major Factors of Power LossMajor Factors of Power Loss
Power Plant
Losses of Transformer
Consumers (Domestic,Industrial,Commercial etc.)
Losses ofTransmissionLine
Losses of Distribution LineSubstation
Electric System Loss & Energy loss
� Total electric energy losses in the electric system
consists of transmission , transformer , and
distribution losses between the supply and
receiving points.
� The difference between energy input and output as a
result of transfer of energy between two points.
(IEEE 100 - Dec. 2000)
∑∑ −=loadsourceloss
PPP
The advantages of loss reduction
� Savings in fuel costs & emissions,
� Prevention of line overloads on system
equipment,
� Improved voltage profiles over the system,
� Reduce the overall cost of power
transmission.
Simple Genetic Algorithm (GA)
{
initialize population;
evaluate population;
while Termination Criteria Not Satisfied
{
select parents for reproduction;
perform recombination and mutation;
evaluate population;
}
}
Issues for GA
� GA has some disadvantages. The population
size, the choice of the important parameters
such as the rate of mutation and crossover,
and the selection criteria of new population
should carefully be carried out. Any
inappropriate choice will make it difficult for
the algorithm to converge, or it simply
produces meaningless results and the results
different from two successive executions.
Simulated Annealing (SA)
Slow cooling(Annealing)
High temperature
System crystallizes into a state of minimal
energy
Elements move freely
Research Phases
� Data Collections & entry,
� Modeling of Objective function &
Penalties and Constraints,
� Simulations & Results.
Objective Function
� Where,
– PGi is real power generation at bus i,
– PDi is real power demand at bus i,
– Ng is number of generators,
– NL is number of loads,
– Ce is energy cost in $/kWh, and
– T is period for energy loss.
××
−∑∑
==
TCPPMinimize e
N
i
Di
N
i
Gi
Lg
11
Network Power balance
� Where,
– QGi is reactive power generation at bus i,
– QDi is reactive power demand at bus i,
– |Yij| is admittance magnitude between bus i and bus j, and
– θij is admittance angle between bus i and bus j.
( )[ ]
( )[ ]∑
∑
=
=
−=−+××=−
−=−+××=−
N
j
ijijjijiDiGi
N
j
ijijjijiDiGi
,...,N, iδδθVYVQQ
,...,N, iδδθVYVPP
1
1
11sin
11cos
The equality constraints power balances can be solved using
full NR to generate a solution of the load flow (LF) problem.
Inequality Constraints
BusesN: Set of , iVVV iii ∈≤≤maxmin
Voltage limits;
Real and Reactive power generation limits,
gGiGiGi ...N,iPPP 1maxmin
=≤≤
gGiGiGi ...N,iQQQ 1maxmin
=≤≤
ansformers:Set of TrN, kttt Tkkk ∈≤≤maxmin
Transformer tap setting;
Overload in lines are checked by
,...,nbr,i,SS Rated
lili 21=≤
Tool Used Modeling &
Simulations
� The program code was developed using
MATLAB R2011a and executed on a
LAPTOP with Processor Intel® Core i5
CPU 2.40 GHz with a 4.0 GB of RAM with
32-bit Windows 7 operating system. The
power flow equations were solved using full
N-R LF method with a tolerance of 10-4.
Overall main steps of
proposed integrated
approach
Merits of this proposed integration
� The proposed hybrid approach requires only few
parameters to be tuned for SA, which makes it
attractive from an implementation point of
viewpoint. It is worth to state that meta-
heuristic algorithms are stochastic in
nature; each run will usually produce
slightly different results. With this proposed
hybridization, with each run, the obtained
results are same.
Demerits of this approach
� The most time-consuming parts in this
method are the repeated power flow
calculations, the computational time of
this proposed algorithm is being
relatively high.
Simulation Scenarios
� Normal operating conditions,
� Different overload patterns,
� Single line outages / contingency with
different loading conditions.
One line diagram - IEEE-30 Bus System
SUMMARIES AND COMPARISONS AFTER APPLYING PROPOSED
APPROACH OF 6 AND 16 CONTROL VARIABLES WITH N-R LF (IEEE-
30 BUS SYSTEM) – LOADING CONDITIONS
N-R LF Run
load patterns - 6 Pg’s
control variables
Optimization
load patterns - 16
control variables
Optimization
Loading 100% 120% 135% 100% 120% 135% 100% 120% 135%
Total losses
(MW)6.8189 9.9434 12.9162 3.5899 6.92059 10.6677 2.95723 6.13295 9.59373
Fuel Cost ($/h) 824.1460 1046.3 1226.9 968.783 1125.71 1265.1 967.273 1123.47 1261.69
Emission
(Ton/h)0.2797 0.3334 0.3956 0.221505 0.255771 0.324327 0.221497 0.255335 0.32292
Computational
time (Sec.)0.11 0.11 0.11 24.78 19.75 27.41 180.23 190.61 226.68
Overloaded
linesNone None None None None None None None None
Reduction
after
applying
Proposed
approach
referred to N-R
LF
/ / / 47.35% 30.4% 17.41% 56.63% 38.32% 25.72%
WITH 120% LOADING AND SINGLE LINE OUTAGE SCENARIOS
(IEEE-30 BUS SYSTEM)Line’s Outage 1-2 1-3 2-5 2-6 4-6 6-7
N-R
LF
Total losses (MW) 23.2210 14.0391 18.6133 11.3923 11.7502 11.9535
Overloaded lines
1-2
3-4
4-6
1-2
2-62-6 None 2-6 None
Optimization w
ith 6
Variables
Total losses (MW) 11.7491 8.97605 13.1009 7.8605 8.03956 8.52788
Computational
time (Sec.)27.42 25.66 61.28 25.90 30.15 35.49
Overloaded lines None None None None None None
Reductions from
N-R LF run%49.40% 36.06% 29.62% 31.00% 31.58% 28.66%
Optimization w
ith 16
Variables
Total losses (MW) 10.3956 8.11593 11.7084 7.02126 7.27446 7.48797
Computational
time (Sec.)219.33 222.68 218.68 227.52 257.74 226.84
Overloaded lines None None None None None None
Reductions from
case of 6-contol
variables%
11.52% 9.58% 10.63% 10.68% 9.52% 12.19%
Conclusions
� The proposed approach was tested with single line
outage’s and being able to satisfy all constraints
including overloading condition of lines that improves
the system performance.
� Can be adapted easily to any given power network.
� Requires only few parameters to be tuned, which makes it
attractive from an implementation point of viewpoint.
� Better results obtained with increasing no. of control
variable. However, the CPU increases.
Future Work
� Extend single Objective to multi-objectives to
include security margin enhancements, fuel
cost minimization, emission minimization,
etc…
� Introduce new control variables like FACTS
device, Reactive power compensation, etc…
Thank you!
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