Paper_FINAL.doc

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Load Profiling by a Genetic Algorithm

Ovidiu Ivanov, Mihai Gavrila

Abstract. In the electricity market context, the customerswithout digital meters must be categorized by the suppliersinto consumer categories, using load profiling techni ues!"he paper proposes a new load profiling approach thatdetermines typical load profiles #"$%s& using a geneticalgorithm approach! "$%s are determined by means of amultiob'ective optimization process based on two criteria(similarity between metered profiles and the "$%s anddissimilarities between different "$%s! "o prove theefficiency of the new approach, "$%s are determined for )consumer categories with different load behaviours!

Keywords

*lectricity markets, load profiling, genetic algorithms, typicalload profiles!

1. Introduction

In today+s electricity industry, the old verticallystructured monopolies are replaced with dynamicelectricity markets where the prices are determined bythe economic principle of supply and demand! "o

participate in the electricity market, the players must provide hourly load data for the entire trading period!

or small consumers, the digital meters costs are prohibitive and they must be categorized by theirsuppliers through load profiling techni ues, which, fora given consumer, determine its typical load profiles#"$%& based on the shape of the metered load profiles!

In -omania, the energy market liberalization started inthe year .///, when the wholesale electricity marketoperator, O%0OM, was established! "oday, the marketis 12!34 open, which means that any commercial and

5Ovidiu Ivanov is with the "echnical 6niversity of Iasi, 7lvd! 8!Mangeron, no! 39:32, Iasi, ;///3/, -omania #phone fax e:mail( ovidiuivanov?ee!tuiasi!ro&!

Mihai Gavrila is with the "echnical 6niversity of Iasi, -omania#e:mail( mgavril?ee!tuiasi!ro&!

industry consumer are eligible consumers! "he currentenergy regulations impose that, in order to participatein the electricity market, their consumption must bemonitored with electronic e uipments able to meterhourly load data @9A!

"he -omanian electricity market is programmed to become fully open in the summer of .//;, giving thesmall consumers the possibility to choose their supplier!7ecause for these consumers, installing a digital meteris a prohibitive option, they must be categorized bytheir suppliers using a method which can determine aload profile by keeping the traditional monthly meterreadings! Buch a techni ue is the load profiling!

Generally, a load profile describes how the consumeruses electricity! "hus, the load profiles are determinedusually for consumer categories, typical weekdays,seasons, or for typical values of other parameters suchas the load factor!"he load profiling techni ues emerged and have beenused widely on many electricity markets! or instance,in the summer of 9CCC, when the 7ritish retailelectricity was fully opened, more than .) millions ofsmall consumers with a peak load smaller than 9// kDentered the market @.A! "his was possible particularly

because load profiling techni ues were used!

"raditional load profiling methods classify theconsumers and generate "$%s based on the meteredload profiles and some other parameters! "his paper

presents a load profiling techni ue that identifies the

"$%s taking into account the following aspects("he "$% associated to a consumer class mustmatch as close as possible to the metered

profiles used to generate it

"he generated "$%s must be as different as possible to each other

"his method takes advantage of the known searchingand classification capabilities offered by the


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evolutionary algorithms! "he proposed method uses agenetic algorithm! -esults are presented as "$%s fordifferent consumer categories!

"he problem+s addressability is immediate, as beginning from summer .//; the -omanian electricitymarket will be 9//4 opened and the market regulationsimpose the use of "$%s for small consumers!

"he authors+ previous works in this field includeseveral papers presented on national and internationalconferences proceedings!

2. The Proposed ethod

"he basic problem can be stated as follows( or a groupof consumers represented by N hourly metered load

profiles, the goal is to determine a set of T typical load profiles #"$%s& that must comply with the followingre uirements(

"he "$% associated to a consumer class mustmatch as close as possible to the metered

profiles used to generate it

"he generated "$%s must be as different as possible to each other

%rior to be processed, the load profiles metered in kDare transformed in values expressed in percents fromthe reference period+s total consume, which has beenconsidered a month! "hus, "$%s are expressed in

percents from the energy consumption of arepresentative consumer from that category, over thereference period!

In the following, an i load profile is denoted LP i,h#i=1..N load profiles, h=1..24 hours&! 7ased on the

LP i ,h curves, a number of T "$%s are generated, wichare denoted TLP t,h #where t is the current typical profilenumber&!

"he set of "$%s is generated as a result of amultiob'ective goal function minimizing process(

.9 F b F a F += #9&

where a and b are weights, F 1 is an ob'ective functionassociated to the similarity between the metered andtypical load profiles and F 2 is an ob'ective functionassociated to the dissimilarity between distinct typicalload profiles!

"he F 1 function is defined as the overall mean s uaredifference between the "$%s and the metered profilesassociated with each "$% and it can be written as

=

=T

t

t DT

F

99

9#.&

where

=

=

==

t

t

K i h

hiht

ti

K i

it t

t

LP TLP

N

N D

.=

9,,

.=

9

9LPTLP

#2&

"he F 2 function is defined as the overall mean s uaredifference between the generated "$%s(

=

==

=

=

=

.=

9,,

9 9.

&9#.=

9

&9#9

.9

99.

.

.9

h

ht ht

T

t

T

t t t

t t

TLP TLP T T

T T F TLPTLP

#=&

"he F 1 and F 2 functions are complementary, in thesense that an optimal solution is a compromise betweentheir values! "hat+s why in the profiling process, theinverse value of F 2 has been used! "he a, b weights can

be chosen based on the importance of thecorresponding goal function! In this paper, the twovalues have been set e ual!

!. Genetic algorithms

Genetic algorithms #GE& are part of the so:called

evolutionary algorithms, which use the natural selectionmechanisms to find the optimal solution for a given

problem! "heir strength lies in a probabilistic and parallel search through the solutions+ space that offers a better chance of finding the optimal result, compared tothe traditional methods @2A,@=A!

GEs encode possible solutions of the problem intochains of numbers or symbols # genes & calledchromosomes which represent possible solutions for thestudied problem and form the search population ! "he

.


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ig! .! Metered load profiles for the 6niversity campuscategory

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ig! 2! Metered load profiles for the Bupermarket category

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ig! 9/! "ypical load profiles for the ospital category

"he results are consistent with the metered profiles+shape for each consumer category! or example,looking at the ospital or Bupermarket categories, the

profiles associated to working days and week:end areassociated to different "$%s! Dhere the metered

profiles do not differ significantly #the otel category,for instance& the algorithm generates only a single "$%for the entire week! "he generated "$%s can beconsidered as valid for their respective consumers+category!

%. &onclusion

)


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