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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