Salih Fadıl 1, Burak Urazel 2. Abstract. 1. Introduction. 2. Problem Formulation

Similar documents
2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

7.0 Equality Contraints: Lagrange Multipliers

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1

Algorithms behind the Correlation Setting Window

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

G S Power Flow Solution

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

An Innovative Algorithmic Approach for Solving Profit Maximization Problems

1. Introduction. Keywords: Dynamic programming, Economic power dispatch, Optimization, Prohibited operating zones, Ramp-rate constraints.

Capacitated Plant Location Problem:

ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014

Non-degenerate Perturbation Theory

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Some Different Perspectives on Linear Least Squares

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi

Construction of Composite Indices in Presence of Outliers

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

Duality Theory for Interval Linear Programming Problems

Parallelized methods for solving polynomial equations

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

The Study on Direct Adaptive Fuzzy Controllers

A Model Reduction Technique for linear Model Predictive Control for Non-linear Large Scale Distributed Systems

Power Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1

Stationary states of atoms and molecules

Physics 114 Exam 2 Fall Name:

International Journal of Mathematical Archive-3(5), 2012, Available online through ISSN

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

Coherent Potential Approximation

Newton s Power Flow algorithm

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

Analysis of Lagrange Interpolation Formula

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

CH E 374 Computational Methods in Engineering Fall 2007

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Numerical Analysis Formulae Booklet

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

Functions of Random Variables

A tighter lower bound on the circuit size of the hardest Boolean functions

Lecture 8 IEEE DCF Performance

Multiobjective Fuzzy Optimal Power Dispatch Incorporated TCSC and Load Model 1

Solving the fuzzy shortest path problem on networks by a new algorithm

PTAS for Bin-Packing

PROJECTION PROBLEM FOR REGULAR POLYGONS

Debabrata Dey and Atanu Lahiri

Long blade vibration model for turbine-generator shafts torsional vibration analysis

44 Chapter 3. Find the 13 term and the sum of the first 9 terms of the geometric sequence 48, 24, 12, 6, 3, 3 2 Solution 2

-Pareto Optimality for Nondifferentiable Multiobjective Programming via Penalty Function

Symmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method

Some results and conjectures about recurrence relations for certain sequences of binomial sums.

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

L5 Polynomial / Spline Curves

Remote sensing image segmentation based on ant colony optimized fuzzy C-means clustering

Two Uncertain Programming Models for Inverse Minimum Spanning Tree Problem

Global Optimization for Solving Linear Non-Quadratic Optimal Control Problems

Arithmetic Mean and Geometric Mean

3.1 Introduction to Multinomial Logit and Probit

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

Rademacher Complexity. Examples

A Remark on the Uniform Convergence of Some Sequences of Functions

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions

A New Mathematical Approach for Solving the Equations of Harmonic Elimination PWM

Queueing Networks. γ 3

MULTIOBJECTIVE NONLINEAR FRACTIONAL PROGRAMMING PROBLEMS INVOLVING GENERALIZED d - TYPE-I n -SET FUNCTIONS

1 Lyapunov Stability Theory

Chapter 9 Jordan Block Matrices

Beam Warming Second-Order Upwind Method

Numerical Experiments with the Lagrange Multiplier and Conjugate Gradient Methods (ILMCGM)

The Geometric Least Squares Fitting Of Ellipses

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

General Method for Calculating Chemical Equilibrium Composition

A conic cutting surface method for linear-quadraticsemidefinite

Solution of General Dual Fuzzy Linear Systems. Using ABS Algorithm

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

9.1 Introduction to the probit and logit models

Modified Method of Computing Generator Participation Factors by Electricity Tracing with Consideration of Load Flow Changes

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

The Mathematics of Portfolio Theory

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

ONE GENERALIZED INEQUALITY FOR CONVEX FUNCTIONS ON THE TRIANGLE

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Unique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM

BAL-001-AB-0a Real Power Balancing Control Performance

New Algorithm for Level Set Evolution without Re-initialization and Its Application to Variational Image Segmentation

ENHANCING THE COMPUTATIONAL PERFORMANCE OF NEWTON- RAPHSON POWER FLOW CODES IN POWER SYSTEM PROBLEMS

arxiv:math/ v1 [math.gm] 8 Dec 2005

CHAPTER 4 RADICAL EXPRESSIONS

h-analogue of Fibonacci Numbers

5. Data Compression. Review of Last Lecture. Outline of the Lecture. Course Overview. Basics of Information Theory: Markku Juntti

Standard Deviation for PDG Mass Data

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables

DYNAMICS. Systems of Particles VECTOR MECHANICS FOR ENGINEERS: Seventh Edition CHAPTER. Ferdinand P. Beer E. Russell Johnston, Jr.

Transcription:

Applcato of Modfed Subgradet Algor Based o Feasble Values to Securty Costraed Ecooc Dspatch roble w rohbted Operato Zoes Salh Fadıl, Burak Urazel, Eskşehr Osagaz Uversty, Faculty of Egeerg, Departet of Electrcal Egeerg, Esksehr, Turkey sfadl@ogu.edu.tr, burazel@ogu.edu.tr Abstract A securty costraed ecooc dspatch proble w prohbted operato zoes for a lossy electrc power syste s forulated. A teratve soluto eod at s based o odfed subgradet algor operatg o feasble values s eployed to solve t. Bus voltage agtudes ad phase agles, off-oal tap settgs ad susceptace values of svar systes are take as depedet (decso) varables e soluto algor. Sce load flow equatos are added to e odel as equalty costrats, actual power syste loss s used soluto of e optzato odel. The proposed techque s tested o IEEE 30-bus test systes. The u total cost rates ad e soluto tes obtaed fro F-MSG algor ad fro e oer techques are copared, ad e outperforace of e F- MSG algor w respect to e oer eods each test syste s deostrated.. Itroducto Ecooc dspatch proble power systes s a costraed o-lear optzato proble. The soluto of t gves e u of total actve power geerato cost rate so at all equalty ad equalty costrats of e proble are satsfed. I e lterature, ay eods have bee developed ad appled to solve ecooc dspatch proble w prohbted operato zoes. Soe of ese eods use e quatuspred evolutoary algor [], e hybrd partcle swar optzato techque [], partcle swar optzato techque [3], dfferetal haroy search algor [4], e θ- SO algor [5] The odfed subgradet algor operatg o feasble values (F-MSG) s a deterstc soluto eod, whch uses deterstc equatos at oe pot to produce e ext soluto pot beg closer to e optu soluto e soluto space. I e proposed dspatch tecque based o F-MSG eod [6], e bus voltage agtudes ad phase agles, e off oal tap settgs ad e susceptace values of svar systes are take as depedet varables. Sce all e costrats ad cost fucto ca be expressed ters of ose depedet varables, e trassso le capacty costrats, bus voltage agtude costrats ad svar systes susceptace value costrats are hadled togeer e sae odel easly. The load flow equatos are serted to e odel as equalty costrats; erefore, e actual syste loss s added to e soluto process autoatcally. I e F-MSG algor, e upper boud for e cost fucto value s specfed advace ad e algor tres to fd a soluto where e cost fucto s less a or equal to e upper boud ad all costrats are satsfed. If t fds t (feasble total cost), e upper boud s decreased a certa aout, oerwse (feasble total cost) e upper boud s creased a certa aout. The aout of decrease or crease o e upper boud for e ext terato depeds o f ay feasble or feasble total cost value was obtaed e prevous teratos. Ths process cotues utl absolute value of e chage e upper boud s less a a predefed tolerace value. F-MSG algor has already appled to o-covex ecooc dspatch proble [7]. Furerore, power dspatch proble cludg lted eergy supply eral uts [8] ad ocovex puped-storage hydraulc ut schedulg proble [9] were solved va F-MSG eod. However oe of e aeatcal odels [7-9] cosder e prohbted operato zoes. To our kowledge, e proposed algor has ot bee appled to e proble cosdered s paper so far.. roble Forulato I s secto, a olear prograg odel s preseted for e ecooc power dspatch proble cosdered s paper. M = F( G) Subject to G p = 0 G Load, j j B q = 0, =,,, G Load, j j B ( ) ( ) pz { pz pz }, G G G G G G () () (3), (4) G G G a pl pl, l L (5) U U U, =,,,, ref, vc (6) a a a, tap (7) b b b, (8) svar svar svar svar ote at actve geerato of e ut,, should satsfy G oe of e equalty show equato (3). I oer words, G

should ot be cotaed by ay of e utually dsjot prohbted zoe sets G ( pz, pz ), =,, pz. The eags of e sybols used s paper are gve e lst of sybols secto... Deterato of Le Flows ad ower Geeratos I order to express e total cost rate fucto ters of depedet varables of our optzato odel, le flows should be wrtte ters of bus voltage agtudes ad phase agles, off-oal tap settgs, susceptace values of svar systes (see equatos () ad ()). The followg equatos gve e actve ad reactve power flows over e le beg coected betwee buses ad j [0]. gj pj = U g sh a j jcos( δ δ j) b js( δ δ j) ( ) p = U g g j j j sh j j jcos( δ j δ) b js( δ j δ) bj qj = U b sh a j js( δ δ j) b jcos( δ δ j) ( ) q = U b b j j j sh j js( δ j δ) b js( δ j δ) (9) (0) () () I e equatos above, U s e voltage agtude of bus, δ s e phase agle of bus, r j jxj s e seres pedace of e le betwee buses ad j, gj jbj s e seres adttace of e le betwee buses ad j where gj jbj = /( rj jxj), gsh jbsh = gsh j( bcap bsvar ) s e su of e half le chargg adttace ad exteral shut susceptace (svar syste) f ay, ad a s e off-oal tap settg w tap settg faclty at bus. p j ad q j are e actve ad reactve power flows gog fro bus to j at bus border, respectvely. p j ad q j are e actve ad reactve power flows gog fro bus to j at bus j border, respectvely. W e help of equatos (9)-(), fro equato (), e actve ad reactve power geeratos of e ut (coected to bus ) ca be calculated by e followg expressos: = p G Load j j B = q G Load j j B The total loss of e etwork ca be calculated as follows: (3) (4) ploss j = p j pj (5) p LOSS = (6) j, j j The cost rate fucto of e ut s take as F = b c d (7) ( G) G G, G where b, c, d are costat coeffcets. The total cost rate s e detered as: = F( G) ( R/ h) G (8).. Covertg Iequalty Costrats to Equalty Costrats Sce e F-MSG algor requres at all costrats should be expressed equalty costrat for, e equalty costrats e optzato odel should be coverted to correspodg equalty costrats. The followg eod s used for s purpose sce t does ot add ay extra depedet varable (lke e slack varable approach) to e optzato odel. It s erefore e soluto te of e cosdered dspatch proble s reduced furer []. The double sded equalty x x x ca be wrtte as e followg two equaltes: h ( x ) = ( x x ) 0, h ( x ) = ( x x ) 0 (9) The we ca rewrte e above equaltes as a sgle equalty costrat for as follows: { [ { } { }]} eq h ( x) = 0, 0, ( x x ) 0, ( x x ) = 0 (0) If x x x, t s obvous at ( x x ) 0, ( x x) 0 ad { 0, ( )} 0, { 0, x ( )} 0 x = x x =. So, e equalty costrats (9) ca be represeted by e correspodg sgle equalty costrat (0). I s paper, e double sded equalty costrats gve equatos (3)-(8) are coverted to e correspodg sgle equalty costrats s aer. By usg e sae logc at s explaed e above, e uo of two sded equaltes show equato (3) ca be coverted to e correspodg sgle equalty costrat at s gve e followg equato. { G G } { G pz } { } { } { pz } { } pz G G G 0, ( ) 0, ( ), eq h ( G) = 0, ( pz G) 0, ( G pz ), = 0, 0, ( ) 0, ( ) G () It should be oted at whe G takes a feasble value, all quattes sde e square brackets equato () becoe postve ubers ad erefore e equalty costrat s ot satsfed. I e opposte case, oce G takes a feasble value,

oe of e quattes cotaed by e square brackets becoes zero, so e equalty costrat s satsfed s case. 3. The Modfed Subgradet Algor Based o Feasble Values The olear optzato proble descrbed by equatos ()-(8) ca be represeted e stadard for gve below: M F T ( x) hx ( ) = 0 Subject to x K () where x = U, U,, U, δ, δ,, δ, a, a, a, bsvar, bsvar,, b tap svarsvar s e depedet varable vector cosstg of e voltage agtudes ad phase agles of e buses (except e referece bus), tap settgs of e off-oal tap rato trasforers ad susceptace values of e svar systes e etwork. ( x ) s e objectve fucto at s gve equato (8), ad hx ( ) = h( ), h( ),, h ( ) x x x () s e equalty costrat vector. It cludes all e orgal equalty costrats, whch are gve (), ad e equalty costrats whch are obtaed fro covertg all e equalty costrats gve (3)-(8) to e correspodg equalty costrats va e eod gve Secto.. K s a suffcetly large copact set cotag e potetal values of x. Rego K s bouded by e upper ad e lower lts of e voltage agtudes of e buses ad e upper ad e lower lts of e tap settgs of e off oal tap rato trasforers, ad e upper ad e lower lts of e susceptace values of svar systes whch are gve equatos (6)-(8). ote at e voltage agtude ad phase agle of e referece bus, ( U ref, δ ref ), ad voltage agtudes of e voltage cotrolled buses are ot cluded to x sce ey are ot depedet varables ad rea costat durg e soluto process. I solvg e costraed optzato proble gve by equato (), e frst step s to covert t to ucostraed oe by costructg e dual proble. Ths ca be doe usg varous LaGrage fuctos []. LaGrage fucto ust guaratee at e optal soluto of e dual proble be equal to at of e pral costraed proble. Oerwse, ere wll be a dfferece betwee e optal values of ese probles, oer words, a dualty gap wll occur. Classcal LaGrage fucto guaratees e zero dualty gaps for e covex probles. However, f e objectve fucto or soe of e costrats are ot covex, e e classcal LaGrage fucto caot guaratee s. Therefore, for e o-covex probles, sutably selected augeted LaGrage fuctos should be used. Cosderg e o-covex ature of our proble, we for e dual proble usg e followg sharp augeted LaGrage fucto: L( xu,, c) = ( x) c hx ( ) uhx, ( ) = ( x) c [ h( )] [ h( )] h ( ) x x x ( uh ( x) uh ( x) u h ( )) x / (3) where u, u,, u R ad c 0 are LaGrage ultplers (dual varables). The dual fucto assocated w e costraed proble s defed as H ( u, c) = M L( x, u, c). x K The, e dual proble s gve by (4) Max H ( u, c) (5) ( u, c) R R For e gve dual proble, e codtos of ( guarateeg zero dualty gaps are prove [3]. 3.. The F-MSG Algor Italzato Step: Select arbtrary actve ad reactve power geeratos, tap settgs ad susceptace values of e svar systes for all subtervals. The, perfor AC power flow calculatos w e correspodg selected actve ad reactve power geerato values all subtervals to obta e tal values for e voltage agtudes ad phase agles of e buses all subtervals. Calculate e tal total cost F T. Step ) Choose postve ubers ε, ε, Δ ad M (upper boud for ). Set =, p = 0, q = 0, ad H =. Step ) Choose ( u, c ) R R ad () > 0 ad set =, u = u, c = c, Step 3) Gve ( u, c ) satsfacto proble (CS) Fd a soluto x K such at F( x ) c h( x ) u, h( x ) H, solve e followg costrat (6) If a soluto to (6) does ot exst or ( ) > M, e go to Step 6; oerwse, f a soluto x exsts e check wheer hx ( ) = 0. If hx ( ) = 0 (or f hx ( ) ε ) e go to step5, oerwse go to step 4. Step 4). Update dual varables as u = u α s h ( x ) (7) c = c ( α) s hx ( ) (8) where s s a postve step sze paraeter defed as ( H L( x, u, c )) λα 0 < s = (9) α ( α) hx ( ) where α ad λ are costat paraeters w α > 0 ad 0< λ<. Step sze s correspodg to e dual varables ( u, c) should also satsfy e followg property: ( hx u ) s ( ) c > ( ). (30) Set =, update ( ) such a way at ( ) as, ad go to step 3. Step 5) If p = 0, t eas at ay feasble total cost rate value has ot bee chose yet, e set Δ = Δ, oerwse set

Δ = (/ ) Δ. If Δ < ε, e stop, x s a approxate optal pral soluto, ad ( u, c) s a approxate dual H = F( x ), H Δ, soluto; oerwse set { } q = q, =, ad go to step. Step 6) If q = 0, t eas at ay feasble cost rate value has ot bee chose yet, e set Δ = Δ ; oerwse, set Δ = (/ ) Δ. If Δ < ε e stop, ad s case, e last calculated feasble x s a approxate optal pral soluto, ad ( u, c) s a approxate dual soluto; oerwse, set H = H Δ, p = p, = ad go to step-. I s algor, steps 3 ad 4 ca be cosdered as e er loop, ad steps, 5 ad 6 ca be cosdered as e outer loop. We call ay outer loop, whch a feasble cost rate value s geerated by e algor, as a feasble state, f. The followg proble s solved by usg GAMS solver: Mze f = 0 L( xu,, c) H 0 (3) Subject to x K where f s a fcttous objectve fucto whch s detcally zero, or ca be take as ay costat value [6]. The way of updatg e dual varables ( u, c) step 4 wll force e soluto Step 3 to coverge to e feasble soluto (see Theores [6]). 4. uercal Exaple The proposed dspatch techque was tested o IEEE 30-bus test syste. lease refer to referece [] for ecessary data for e test syste such as actve ad reactve geerato lts of e geerators, prohbted operato zoes for all geerators, actve power trassso capacty lt for all trassso les, actve ad reactve load schedules for e test syste, coeffcets of fuel cost rate fuctos. The paraeters, 5 explaed secto 3., ad 3., are chose as ε = 5 0, = 0.05, Δ = 00, M = 500, u = [0,0,...0,0], c = 5000, ε ( 3) ( ) =. Bus s take as e referece bus ad ts coplex voltage s take as.05 0 pu. The upper ad lower lts of e bus voltage agtudes for all buses are take as U = 0.95, U =.05 pu,. Ital bus voltage agtudes ad phase agles each subterval are calculated by carryg out a load-flow soluto w e selected tal geerato values, whch are gve Table. o ore load flow calculato s carred out e subsequet stages of e soluto process. The sulato progra was coded MATLAB ad GAMS was used to solve CS gve by equato (6). I e followg two cases, we solved e dspatch proble by usg e F-MSG algor ad copared e foud results w e oes obtaed va dfferet eods such as sulated aealg (SA), shuffle frog leapg (SFLA), partcal swar optzato (SO) ad hybrd shuffle frog leapg ad sulated aealg (Hybrd SFLA-SA) eods (please see referece []). Table. Selected tal geerato values IITIAL GEERATIOS (MW) G G G 5 G G3 LOSS 03.40 60.00 45.00 5.00 30.00 35.00 5.00 885.5 4.. Case : rohbted Operato Zoes are ot Cosdered To show at e proposed eod satsfes e prohbted operato zoe costrats, frst we solved e dspatch proble w e assupto at e prohbted operato zoes do ot exst. Therefore, we dd ot cosder e prohbted operato zoe lts equato (3) ad we appled e F-MSG algor to e dspatch proble w e calculated tal bus voltage agtudes ad phase agles. The soluto-pot actve ad reactve power geeratos for e curret case are gve Table. 4.. Case : rohbted Operato Zoes are Cosdered I s case, e prohbted operato zoe lts equato (3) are added to e dspatch proble ad t s solved by eas of e F-MSG algor by usg e sae tal bus voltage agtudes ad phase agles. The soluto-pot actve ad reactve power geeratos for e curret case are gve Table 3. 5. Dscusso ad Cocluso I s paper, we propose a soluto to securty costraed power dspatch proble w prohbted operato zoes by usg e F-MSG algor for a lossy power syste area. The dspatch techque s tested o e IEEE 30 bus test syste. As see fro Table ad Table 3, e proposed techque provdes e lowest total cost ad e shortest soluto te values, aog e results obtaed fro e techques gve referece []. We are curretly perforg research o applcato of e F-MSG algor to ecooc power dspatch probles cludg prohbted operato zoes w o-covex total cost curve. Table. Coparso of e results at are obtaed by e oer eods w oes foud va e F-MSG for case. METHODS F-MSG Hybrd SFLA-SA SFLA SA G MW 76.0 7.78 8.6 9.5 G MW 48.84 48.0 5. 48.40 G 5 MW.54 3.94.8 9.55 MW.70 3.85 5.59.6 G MW.3.60 0.00 0.00 G3 MW.00.00.8.00 LOSS ( MW ) 9.8 9.79 0.46 0.68 ( R/ h ) 80.79 a 80.97 a 803.67 (805.6) b (804.9) b 804.0 ST* (sec).73 8.93 9. 98.74 * Soluto Te a The values take fro ref []. b The values, whch are calculated by usg e respectve geerato values show Table ad cost rate fuctos gve [].

Table 3.. Coparso of e results at are obtaed by e oer eods w oes foud va e F-MSG for case. SOLUTIO METHODS F-MSG Hybrd SFLA-SA SO SA G MW 78.3 8.45 74.6 8.70 G MW 45.00 45.00 57.0 57.9 G 5 MW.8.53 3.54 7. MW 3.6.08 4.6 6.5 G MW 3.7.98.00 0.00 G3 MW.00.00 4.5.00 LOSS ( MW ) 0.05 0.64.09.85 ( R/ h ) 806.43 c 808.7 c 804.74 805.8 (809.75) d (80.74) d ST(sec) 5..9 4.94 3.86 c The values take fro ref []. d The values, whch are calculated by usg e respectve geerato values show Table 3 ad cost rate fuctos gve []. 6. Lst of Sybols R : a fcttous oetary ut : uber of buses e etwork. G,: set cotas all buses to whch a geerator s coected.,: set cotas all buses to whch a reactve power source s coected. : set at cotas all buses drectly coected to bus. B tap, L : sets at cotas all tap chagg trasforers ad les e etwork, respectvely. p : actve power flow o le l, (pu or MW). l G, G : actve/reactve power geeratos of e ut, respectvely, (pu or MW, MVar). Load, Load : actve/reactve loads of e bus, respectvely, (pu or MW, MVar)., ;, G G G G : lower/upper actve/reactve geerato lts of e geerato ut, respectvely, (pu or MW, MVar). pz, pz : lower ad upper lts of e prohbted zoe for e ut s actve power geerato, respectvely. p l : u actve trassso capacty of trassso le l, (pu or MW). pz : uber of prohbted zoes for geeratg ut. rohbted zoes are ubered such a way at pz( ) < pz, =,3,..., pz., VAR : uber of equalty costrats ad depedet varables, respectvely svar : uber of statc var systes e etwork. tap : ubers of off oal tap rato trasforers e etwork. x : depedet varable vector obtaed at e terato of e er loop of e u, c of e outer loop terato. : dual varables calculated at e terato of e outer loop. terato of e er loop s : postve step sze paraeter calculated at e er loop. F : total cost value whch wll be checked e T Δ : decreet/creet o F value, at e ed of terato, accordg to wheer F s feasble or ot, (R). ε, ε : tolerace values for h( x ) ad Δ, respectvely. 7. Refereces terato of e outer loop, (R). outer loop [] eto, JXV., Berert, DLdA., Coelho, LdS., "Iproved quatuspred evolutoary algor w dversty forato appled to ecooc dspatch proble w prohbted operatg zoes", Eergy Coverso ad Maageet, vol.5, pp.8 4, 0. [] ka, T., ara, MR., Azzpaah-Abarghooee, R., "A ew hybrd algor for optal power flow cosderg prohbted zoes ad valve pot effect", Eergy Coverso Ad Maageet, vol.58, pp.97 06, 0. [3] Mohaad-Ivatloo, B., Rabee, A., Soroud, A., Ehsa, M., "Iterato SO w te varyg accelerato coeffcets for solvg o-covex ecooc dspatch probles", Electrcal ower ad Eergy Systes, vol.4, pp.508 56, 0. [4] Lg Wag., Lg-po L., "A effectve dfferetal haroy search algor for e solvg o-covex ecooc load dspatch probles", Electrcal ower ad Eergy Systes, vol.44, pp.83 843, 03. [5] Hosseezhad, V., Babae, E., "Ecooc load dspatch usg θ- SO", Electrcal ower ad Eergy Systes, vol.49, pp.60 69, 03. [6] Kasbeyl, R., Ustu O., Rubov, AM., The odfed subgradet algor based o feasble values, Optzato, vol. 58, (5), pp. 535-56, 009. [7] Fadıl, S., Yazıcı, A., Urazel, B., A soluto to securty costraed o-covex ecooc dspatch proble by odfed subgradet algor based o feasble values, Iteratoal Joural of Electrcal ower ad Eergy Systes, vol.43, pp.849 858, 0. [8] Fadıl, S., Yazıcı, A., Urazel, B., A Securty-costraed Ecooc ower Dspatch Techque Usg Modfed Subgradet Algor Based o Feasble Values ad seudo Scalg Factor for a ower Syste Area Icludg Lted Eergy Supply Theral Uts, Electrc ower Copoets ad Systes, vol.39, pp.748 768, 0. [9] Fadıl, S., Urazel, B., Soluto to Securty-costraed ocovex uped-storage Hydraulc Ut Schedulg roble by Modfed Subgradet Algor Based o Feasble Values ad seudo Water rce, Electrc ower Copoets ad Systes, vol.4(), pp. 35, 03. [0] Jegaeesa R, or M, Role MF. ewto-raphso power flow soluto eployg systeatcally costructed Jacoba atrx. d IEEE Iteratoal Coferece o ower ad Eergy;008:80-6. [] Burachk, RS., Gasov., Isaylova, A., Kaya CY., O a odfed subgradet algor for dual probles va sharp augeted lagraga J. Of Global Optzato, vol. 34, pp. 55-78, 006. []Rubov, AM., Gasov, R., The olear ad augeted lagragas for o-covex optzato probles w a sgle costrat Appled ad Coputatoal Maeatcs, vol, p.4-58, 00. [3] Gasov, R., Augeted lagraga dualty ad odfferetable optzato eods o-covex prograg, Joural of Global Optzato, vol. 4, pp. 87 04, 00.