Chapter - 3 ASSESSMENT OF AVAILABLE

TRANSFER CAPABILITY

3.1. INTRODUCTION

Available Transfa Capability (ATC) is a measure of unutilized capability of

bansmission system subjected to wheeling transactions without violating transmission

constraints at a given time and load. Capacity Benefit Margin (CBM) is incorporated in

the lest systems by reserving powers in h;mrmission lines. The Transmission Reliability

Margin (TRM) is implemented either by making any one of the transmission lines or a

generator out of order. CEED problem is obtained by considering both the economy and

emission objectives. In the proposed approach, Newton-Raphson method and

evolutionary programming algorithm have been used for solving the power flow and

combined economic emission dispatch rrspectively. This bi-objective CEED problem is

convded into single objective function using price penalty factor approach. A novel

modified price penalty factor is proposed to solve the CEED problem. A non-linear

scaling factor is also included in EP algorithm to improve the convergence performance.

Assessmat of ATC in combined economic emission environment has been tested on

IEEE-30 and Indian utility-62 bus systems with line flow constraints. Simultaneous

bilataal and multilalaal wheeling transactions have becn canied out in the tst systems

for tbe assessment of ATC. The solutions obtained arc quite encouraging and useful in

the prcmt dcrrgulatcd environment

Opm transmission accss in electric powa industry is now a legal

rcquiranent [121]. kcgula t ion and market competition w a c introduced in most of the

counttics like Noway, Chilc Pnu, Bolivia, England the U.S. and now in

India [54,99,111,116]. Operation of power system under deregulated environment

presents many technical [97] and economical problems [98]. It seeks to identify

deregulation scenarios and technical solutions such as standards and algorithms.

Transmission open access to all supplim and consumers has been mandated in the U.S

[I 161. Various mathematical models have been developed [35,38,49,70,115] to find

Available Transfer Capacity of transmission system.

The central feature dominating the power market in the restructured system is

called as power pool. In addition, a range of other trading arrangements such as bilateral

and multilateral contracts, forward and future markets and the hour-ahead spot market

have made their appearance. Many buyers of powa such as industries, housing estates

and otha complna may not wish to buy their electricity through the pool and may

prefer to make their own power purcha.% agnements with selected suppliers. This

provides motivation for bansaction contracts between Independent Power Producer (IPP)

and consumers. These bansactions could be of two tqpes: either involving just one buyer-

sella pair (even if their physical injection and utilization points are multiple) and it is

known as b i l a t d transaction, or bringing together multiple buyers and sellers who

gmup thrmsclva together to enter into a multilatd bansaction.

Aa approach to Ihe optimal power dispatch problem in a structure dominated by

bilateral Md multilataal bansactions have becn pmposed by Fang and David [32]. Yog

Raj Sood et d. describe Evolutionary Programming based Optimal Power Flow (EP-

OPF) algorithm for the optimal selection of whaling option from various feasible

options of daegulated powa system considering s@em

clstraints 11341. The selection of the best possible wheeling hansaction has been

dctcmined based on transfer capability and short-term marginal cost in a deregulated

power system [132,133].

Since many utilitica provide transaction services for wholesale customers, they

must know about the post information on ATC of their transmission networks. Such

information will help power marketers, sellers and buyers in reserving transmission

services. ATC must be rapidly updated for new capacity reservations, schedules or

transactions. There is a strong demand to improve the uncertainty of the ATC limits for a

number of varying factors, including uncertainties in the location of the delivery and

receiving points. The camputation of ATC with TRM and CBM bas been carried out by

Yan-Ou and Chanan Singh in IEEE-24 bus reliability 1 s t system [43,129].

In this chapter, ATC with margins is computed for practical power systems in

combined economic emission environment. Ln the proposed approach, Newton-Raphson

mahod and EP algorithm have been used for power flow and combined economic

emission w a t c h rcspcctively. A modified price penalty factor approach is proposed in

chis papa to solve the CEED problem. A non-linear scaling factor is also included in EP

algorithm IO improve the convergence performance. CBM is incorporated by r e s e ~ n g

powa in W s s i o n l ina and TRM is implemented by making either any one of the

bansmission l ina or gcnaator out of order at a time. The proposed EP based CEED

alg&chm for Ihe computation of ATC with simultaneous bilateral and multilateral

transactions is dmnstnted on the IEEE-30 and Lndian utility42 bus systems.

3.2. AVAILABLE TRANSFER CAPABILITY

T m f a capability of a transmission system is a measure of unutilized capability

of the system et a given time and depends on a number of factors such as the system

generation dispatch, system load level, load distribution in network, power transfer

W e e n areas and the limit imposed on the transmission network due lo thermal, voltage

and stability considnations. In the contingency, it is computed after the operation of any

automatic devices to secure the system constraints, but before any postcontingency

operator initiated the system adjustments.

ATC is defined as the Total Transfer Capability (TTC) less the Tnlnsmission

Reliability Margin less the sum of existing transmission commitments (which includes

retail customer service) and the Capacity Benefit Margin (CBM).

ATC can be expressed as:

ATC = TTC - TRM - Existing Transmission Commitments (including CBM)

Transmission Reliability Mmgm is defined as (hat amount oftransmission transfer

capability n e c a s q to cnsure that the interconnected transmission network is secured

unda a reasonable range of uncntaintics.

Capacity k c f i t Margin is defined as (hat amount of eanrmission transfer

capability resaved by load w i n g entities to ensure access to generation from

intaco~ected sys(ans to meet generation reliability requirements.

Utilities would have to dctamine adequately their ATC's to inswe that system

reliability is maintained while saving a wide range of ~ i s s i o n transactions. ATC

betwm and within arras of the intau,~ected power system and ATC for critical

transmission paths behvao these areas would be continuously updated and posted

changes in scheduled power transfers between the areas.

The ATC principles an stated as follows:

ATC calculations recognize time-variant power flow conditions and

simultaneous transfers and parallel path flow throughout the transmission

network.

ATC calculations must recognize the dependency of it on the points of

power injection, the direction of power transfers and the points of power

cxtnrtion.

ATC calculations must produce commercially viable results and the

computed values must give a reasonably accurate and dependable

indication of transfer capabilities available to the electric power market.

ATC calculation is subjected to the following constraints

Power limit on transmission line

MVAfp, S MVAfp,q- (3.1)

Where M V A ~ , , ~ is the maximum rating of transmission line connecting bus p and q.

The inequality constraint on voltage of each PQ bus

V," 5 v, s v,- (3.2)

W h a V,' Pnd V,- are minimum and maximum voltage at bus i.

The inequality constraint on 4 and reactive power generation (P,i,Q,) of

each generation i

PpU s P,, s P',- (3.3)

Q" S Qg 5 QatU (3.4)

when pd',pgiPU, ~~i~~ and Qgim are minimum and maximum value of real and

reactive power allowed a1 generator i.

In this work, simultaneous bilateral and multilateral transactions were carried out in the

practical test system to determine their ATC.

33. CEED PROBLEM FORMULATION

hrring the computation of ATC, all the generators of the utilities must be held at

optimal smings. In this chapter, optimal settings of generators were obtained from CEED

environment. The problem formulation of Evolutionary programming based CEED

environment was briefly explained in the chapter 2. The mathematical modelling of

wheeling transactions is given below:

3.4. MODELLING OF WHEELING TRANSACTIONS

A simultaneous wheeling transaction has been included in an n bus system, the

r l l a at the bus i and the buyer with a load at bus j. The corresponding wheeling

transaction can be represented as WT (i-j), where i and j may be varied from I to n and

i is not equal to j. Let us assume that an Independent Power Producer (IPP) is willing to

supply the additional load demand at bus j through the utility transmission system by a

whaling bansaction WT (i-j). Then run the EPCEED with all the generators of the utility

being held at fued optimal setling of base case under these conditions. The amount of

whaled powa in the network must be withii the limits of IPP and satisfy the

transmission constraints. In general, the algebraic sum of power delivered by the non-

utility gcncntordlndcpcndcnt Power Producers is equal to the sum of powm taken at

diffarmt l d points. Each b i l a t d transaction between a seller at bus i and power

p & a a at bus j satisfies the following power balance equation.

Multilateral transaction involving more than one seller and / or one buyer can be

expmsed as:

Where Pd and P4, npresrnt the power injections into the seller bus i and the power taken

at the buyer bus j and k is the total number of wheeling transactions.

35. STEP-BY-STEP ALGORITHM

The procedure for calculating maximum power transfer capability of a wheeling

W o n is as follows

I. The optimal cost of generation is determined by the EPCEED algorithm (base case) for the given system load.

2. Select the bus number(s) in which the IPP is connected and the load(s) is taka out

3. Set the load bus counter j. on which additional load to be added.

4. Note the value of real power delivered by the IPP and taken out at the load bus. Thus wheeling transactions are incorporated in the deregulated power system. At a time more than one transaction is also permitted.

5. CBM is incorporated by w i n g powers in the selected transmission lines. TRM is implemented by either making the comsponding transmission line or gen"BLOr BS out of order in the network.

6. Find the maximum amount of power that can be bansferred without violating mwuk consaaints for the corrcpndmg wheeling transaction.

7, lncrancnt the load bus count j by I and if j is less than or equal to number of bcucs.rhengolostep5.

8. Similarly increment the sella bus i by I until it is less than or qua1 to n number of buscs.

9. Find the maximum amount of real power (ATC) supplied by the IPP and corresponding cost of gmaxdion of utilities for the given load demand including wheeling ~ t i m without violating tbe transmission constraints.

3.6. SIMULATION RESULTS

The study has ban conducted on IEEE-30 bus (31 and Indian utility-62 bus

systems [I 141, slightly modified to represent simultaneous wheeling transaction in a

dacgulated market. For the present study, reactive power h a n d at load buses has been

taken as constant. In the proposed approach the minimum generation and cost of the

genuating units were obtained in wmbined cconomic emission environment using

evolutionary progmmming with transmission wnsmkts. The proposed algorithm is

applied lo IEEE-30 bus and Indian utility42 bus systems whow data have ban given

in Appendix A & B. Table 3.1 summarites the best solution obtained by evolutionary

b d optimal power flow (EPOPF), combined economic emission dispatch (EPCEED)

and rnodilied wmbined mnomic emission dispatch (EPMCEED) with line flow

constraints for IEEE-30 bus and Indian utility42 bus systans. The execution time for the

proposed method of IEEE-30 and Indian utility-62 bus system is obtained as 52.01 and

285.77 Sec. rcspcctively.

To compute static ATC, best g-tor sating5 obtained from the EPMCEED

algorithm for the given system load IS taken as base ease values. C-ity Bcnefit Margin

is imapomed by nsaving powers in bansmission lines of both the t a t sydans given in

Table 3.2. Transmipsion Reliability Margin is implanented by making third lie as out of

orda in FEE-30 bus system. It is incorporated in Indian utility42 bus systan by making

a genaabr connected at 37. bus as out of da. The simulation studies were canied out

oa PlIl7OMhz systan in MATLAB environment.

Table 3.1, Bat ldutlou of differtot methods

Table 33.Implewot.tiw of CBM

IEEE-30 bus system Lndh. utility42 bur system

N ~ f 1. 2. 3 4. 5. 6.

Lincs 1. 2. 3. 4. 5.

-

- 1 2 9 10 28

2 5 10 20 27

1 11 23 37 47 14

hwygA) 10 40 30 I2 I5

To

10 16 24 46 48 19

PaeygA, 05 05 10 05 10 10

3.6.1. IEEE-30 bur system

The EPMCEED algorithm was applied to IEEE-30 bus system with 6 genemhg

units and 41 bansmission lines with four tap changing transformers. The total system

load demand is 283.4 MW. For the base case, best g e n d o n of IEEE-30 bus generating

units obtained h m EPMCEED algorithm is prrscnted in the Fig.3.1. The total operating

cost of wmbmed amnomic dispatch is obtained as 2 15 1.570 S h .

G l n e n b r Number

Fig 3.1. Bat perator rat[ags of EPMCEED method - IEECM bus system

Rcoul~r of Tblc 3.1 reveal that the price penalty factor (b) approach give only

the rppmxhak value of total oy#PLiag cost for their corrrspoading load demaod. Hence

in this wok. a modified price penalty factor OL) apprwh is proposed to give exact total

opntine oost.

The e o m ~ o n a l procedure of proposed modified price penalty factor for

lEEE 30 bus systan is explained as follows. The ratio bdwm the maximum fuel and

emission costs of six gcnaating units wne found and arranged in a d i g order.

ha = [ h2 hl hq hs h3 h]

h, = [ 1.7342 3.5258 5.7554 5.9324 5.9834 6.30001

The comspoading maximum limits of generating units are givcn by

P,-=[8020035 305040]

For a load of Pd MW starting from the lowest h, value unit, the maximum

capacity (m) of the units is added om by one and when this total equals or exceeds the

load h, associated with the last unit in the procss is price penalty factor 1631.

m=[80280315345395435]

For Pd = 283.4 MW, (80+200+35) MW > 283.4 MW.

Hmcc price penalty factor (h) is dctamined as 5.7554 for IEEE 30 bus system. Eva-

though the pricc penalty factor was computed for 283.4 MW but it gives the value up to a

load dcmand of 315 MW. So the modified price pcnalty factor is computed by

intapoldng the values of h, for second and third generating units by satisfying the

corresponding load danand.

By incorporating modified price pcnalty factor approech, the operating cost savings of

approximately 1137 Sihr was obtaimd for IEEE-30 bus systan givm in Table 3.1.

The following case studis an camed out for computing static ATC with margins

subje~ted to various whaling transactions in a c o m b i economic emission

environment

Case I : La us assume that IPP of 50 MW maximum capacity is connected at bus no.30.

All the generators of the IEEE test system must be held at best setting values obtained

from EPMCEED method. The buyer is intensted to have a bilateral tansaaion in all the

buscs of 30-bus system. Fig. 3.2 presents the maximum allowed load (ITC) that can be

supplied by IPP through various wheeling bansactions at different load points. The real

power (ATC) to be whaled with CBM and TRM when IPP is connected at the 30' bus is

shown in Fig.32.

Fig. 3.2. Mulmum dowed load supplied by LPP through whcding tmnrrcdona W (30-1)

This information helps the pool controller to encatrage feasible wheeling

tnaw3ioas without violating system reliability and cconomy in combined economic

h e 2: Let us consider tha a seller and buyer pair is i n t d to transfer real powa

betwear 14' bus to 2zd bus, 16' bus to 8' bus, 25' bus to 5' bus and IS' bus to 24'

bus simultaneously. It is found that ATC obtained is about 17 MW, 10 MW, 30 M W and

IS MW rrspcctively for the above wheeling transactions without violating the

transmission constraints.

Case 3: Let us consider that a sella and buym are intercad in hansfming real powa

along with four simultanwus bilateral in case 2 and a multilataal transaction between

buses 18 and 2 1 to load buses 6,12,27 and 30 respectively. The sunmation of wheeling

d powm taken out at the load buses is equal to the powa supplied by IPP. ATC with

margins obtained for the above transactions is 10 MW, 7 MW, 4 MW and S MW in the

wmspondmg load buses. The lines got congested after traasfcning 26 MW of real

powa along with four simultaneous bilakd transactions in case 2.

3.63. Indian Utility - 62bw system

To validate the performance of the proposcd algorithm, Indian utility-62 bus

system consisting of 19 gmcrators. 89 (220irV) lines with I I lapchaaging transfomm

has ban considad. The (ocal load demand is 2909 MW. For the comsponding load

danand, kd generation of Indian utility42 bus generating uaits obtained from h e

proposed evolutionary programming algwithm is givm in Fig.3.3. Bat solution obtained

by different mcchods for Wan Utility 62 bus system is given in Tablc 3.1. The total

opwating cost of the above test system with EPMCEED algorithm is obtained as

54060.270 S h . From the m l t s of Table 3.1, it is justified that modified price penalty

factor approach gives the exact minimum dispatch solution. By incorporating modified

price pmalty factor approach, the opemting cost savings of approximately 769 S h was

obtained.

300

Fig 33. B a l generator Ictbingr of EPMCEED method - Indian utility 62 bur system

rtK following case studies src carried out for computing static ATC with margins

wbcn subjected to simultaneous bilatcrsl and multilatsal transactions.

Case I : Lct us consider a sella and buyer pair is intasted to have four bilataal real

powa transdons betwrm 28' bus to 44. b w 35' bus to 16' bus 50' bus to 10' bus

and 30' bus to 48' bus s i m u l m l y . It is o k w c d that ATC obtained is about

35 MW. 5 MW, 4 MW and 36 MW rrspectively f a the above whaling hansactims

Case 2: La us consider that a seller and buym are interested in transferring nal power

along with four simultanwus bilateral in case 2 and a multilataal transaction betweem

buses 36 and 42 to load buses 12,2454 and 60 mptively. The summation of wheeling

real powers taken out at the load buses is equal to the power supplied by IPP. ATC with

margins obtained for the above bmsactions is 20 MW, 10 MW, 5 1 MW and 68 MW in

the comesponding load buses. The lines got congested &a hansferring 149 MW of real

power along with four simultsnwus bilateral transactions in case 2.

Care 3: La us consider that sellers and buym are i n t e d in transfening red power

betwear them by two multilateral transactions. For the fvst multilateral transaction, the

seller buses an 9,18, 30 and buyer buses are 10,15.36,60 respectively. The static ATC

dctennined for the first multilateral bansection is 63 MW using the proposed algorithm

with line flow constraints. For the second multilateral transaction, the seller buses are

33 &I 5 and buyer b w s are 19.22 & 12 respectively. The static ATC value obtained with

the proposed algorithm is 54 MW for the second multilateral transaction.

3.7. CONCLUSION

In this work, the transfer capabilitia of the test systems with and without margins

were calculated with wheeling transactions. It represents the mount of additional power

to k wheeled in the transmission system without affecting the system security. During

the computation of the transfa capability. the base case genaator settings of the test

syolrm~ w a c obtained using cvolutionsry programming algorithm. The d t s obtained

are found to k wful in de(ermining the magnitude of f d b l e whaling transactions in a

m b i n a l economic emission environment.