Chapter - 6

CONGESTION MANAGEMENT

6.1. INTRODUCTION

Transmission system congestion in a competitive electricity market refers to the

overloading of lines or transformers due to market settlement In the deregulated

cavironmmt, the customers would like to purchase electricity from the cheapest available

sources. Hence the chanca of congestion in the deregulated market are quite high as

compared to the monopolistic marka. The congestion is undesirable in the system and

should be alleviated for the secure opaation of the systm. In deregulated environment,

them may be a large number of different buya and seller c o m b i o n s entering a

Scheduling ChrdinaIor's (SC) market through bilatnal and multilataal transactions.

Whencva electricity is traded the transmission and distribution losses occur. To keep the

balaace in the system, additional production is needed to meet the losses. But due to tbe

whaling transactions, there is a possibility of congestion occurring in the transmission

system. The transmission congestion is to be handled by a thud who is neither a

buyer nor a sella of the electric energy. To coordinate among the independent trades and

to opaple the system in a secure state, a daegulated system ha an IS0 that monitors and

opaatc~ the inkrco~ccted pow= system. It maintains tbe powa flow balance

kouglnu tbc network, includes the transmission losses and permits the feasible

In r regulated powa market, the scheduling of genaation is done by the optunal

powa flow (OPF) algorithm lbat awns the gmcntion and hansmisslon line limits [29].

With tbc and of m increasing number of wheeling transactions in the open access

market, the possibility of imutlicicnt resources leading to network congestion may be

unavoidable. Singh e( al. modelled power transactions in a deregulated market under

open transmission access as pool dispatch and bilateral dispatch [SI] to relieve the

congestion basal on nodal pricing h e w o k Real-time transmission congestion can be

d e f d as the opaating condition in which there will not be enough transmission

capability to implement all the traded transactions simultaneously due to some

unexpected contingencies. It may be alleviated by incorporating line capacity mnstraints

in the dispatch and scheduling pmess. This may involve redispatch of genaation or load

~lntailment

That arc two broad paradigms that may be employed for congestion

management. They an the cost-fnc means aad the not-cost frae means [39]. The former

includcs actions like outagmg of congestal lines or operation of transformer taps, phase

shiftas or FACTS devices. These means arc formed as cost-fne only because the

marginal co~ts involved in their usage arc nominal. Srivastava a al. [I091 have well

pr*lcnted the congestion management using an optimal power dispatch model for a

practical powa system nehvork to minimize the curtailment of the contracted powas in a

powa market having bilateral. multilataal as well as h contnrts. The net-cost fm

mums include rescheduling h e gcnantion [I261 aod curtailment of loaddtransactions

[I 131. A pamnda tamed as willingness-bpy to avoid curtailment was inlmducai in

1331 md it settles the transaction curtailment slrategies which may then be incorporated

in the ophmJ powa flow framework. This factor brought the new set of bansactions

closa ca the dcsind tmwztions within the security region. Yu ad Uic proposed

c@oa clustas m&ad to readjust the transactions in the rrstructud powa market.

But this mahod is based on DC load flow involving the acsumpions of lwrsless system

and unity voltage magnitudes at all the buses [19]. Ashwani Kumar d al. proposed a

d coogestiw managanent approach based on the real and reactive power flow

mitivity indices to reschedule the gcnaators as demomated on practical power

syst- PI.

The rrrtnrcturing of the electric power industry has involved paradigm shifts in

the d time control activities of the power grids. Managing dispatch is one of the

important control activities in a power system. OPF has perhaps the most significant

technique for obtaining minimum cost generation patiems in a power system with

existing transmiasion and operational conshakits. The mle of an IS0 in a wmpecitive

market envimnmmt would be to facilitate the wmpldc dispatch of the power tbat gas

contrackd among the market players. With the bmd of an increasing number of bilateral

contracts being signed for electricity market hadcs the possibility of insufficient

ICSOIUC~~ leading to actwork congestion may be unavoidable. In this scenario congestion

management with OPF hamwork becomes an impacant issue. Relieving congestion

process may involve rodispalch of genaation or load currailmcnt. The wntingary-

bared congestion management was explained by the m h m Alomoush a.4. [2] with

a minimum number of adjustments in p f d scheduls.

In Chis work. Evolutionary Rogramrmng algorithm is proposed to solve

cmgestion problem in a dcrrgulated mvironmmL The p r c f d schedules of the

g c a d o l l r of tbc amcqodng S O ate obtained in the CEED environrnmt. The SCs

may submit their incrnaend and decrunend bidding prices in a real-time balancing

muLa to d c v e umgcstioa. Thest can th be implemented in tbe OPF problem to yield

change in the generator outputs. EP based OPF will give a least cost fornulation to

relieve the congestion by having minimum possible adjustments to p n f d schedules.

The proposed algorithm is tested on IEEE-30 bus and Wan utility-62 bus systsms.

63. PROBLEM FORMULATION

An OPF formulation for congestion management combines the following three

objativa:

(i) Minimizing the cost of generation.

(ii) Maximizing the benefit of customers.

(iii) Minimizing the deviation of generations from their prefemdlschedulcd values of generation.

6.2.1. Base car (Optimal preferred generation rbedule)

For Ihc congestion management, the buyers and sellas can submit the adjustment

bids (both incremental and dccrancntal bids). System opcrator selects their bids and

d a i d a the amount of deviations from the prcfemd schedule. In this work preferred

schcdula of the gcnaamrs arc obtained in CEED enviment .

A f a the pefemd schedule is received by the system opaator From the

exchange, it pcrfonns the contingency analysis to identify the critical contingmcis

which will result in the congestion or insure opaation of the system. For ttus purpose.

some s m d d critical contingencies arc identified and system operator prepares the

sckdule for thc wagestion management. The rnatbamhcal modelling of wheeling

tmsdom is given in h e following d o n .

The conceptual modelling of wheeling bansactions is that sellers and buy-

encourage tbe trading between them without violating the transmission constraints.

Mathematically, each bilataal transaction betwan a seller at bus-i and powa purchaser

at bus-j satisfies the following power balance relationship.

Pi-P4=0 (6.1)

In the case of multilateral trsnsaction, the summation of powa injected in diffaent b w s

(i) is equal to the summation of load powers taken out at various b w s (i).

Whm P, and Pg represent the powa injection into the seller bus-i and the power taken

out at buya bus-j, 4: is the total number of transactions.

6.23. Congation Management

The congestion management problem can be formulated as an optimization

problem with an objective to minimize the total adjustment price. This is formulated to

minimize the total cod of the adjustment bid utilized from all the SCs for the congestion

management. The ponfolios of all the SCs are optimized and kept under balance. The

objcstive function for the congestion management problem can be formulated as,

whac C', and C, an vectors of incranental and decmental bids submitted by the

gmcntors at oodc i under scheduling coonhator in for redispatch during congestion. AF'

be the change in prefarcd schedule and N, is the gcnaators of the umesponding

Scheduling Coordinator and C is the total congestion cost.

Subjected to the SC's portfolio b a l m equation

Where Phi is the power taken at node i under Scheduling Coordinator m and operating

limits on powa injection at each node is given by

P-7 2 P,, 5 P-7 (6.5) Thcsc transactions arc then brought to the notice of IS0 with a q u e s t that transmission

facilities for the relevant amount of powa transferred be provided. If there is no violation

of Sits, IS0 simply dispatches all the qucsted transactions, otherwise it carries out

twcheduling and the comsponding congestion chargcs arc levied on the customers. In

this w o k Evolutionary Programming algorithm is used to reschedule the generations to

relieve the congestion which is explained in the following section.

63 . EP-BASED CONGESTION MANAGEMENT (EPCM)

Evolutionary programming is a probabilistic sesrch technique, which generates

the initial parent vectors distributed uniformly in intavals within the limits and obtains

global optimal solution ova a number of iterations. The main stages of this technique arc

initialization. creation of off-spring vector by mutation and competition and selection of

bcst vectors to evaluate bcst fimess solution. The implementation of EP algorithm is

given below.

63.1.Idthll.lintion

The initid poplation (number of parent vectors) is g-ed after satisfying the

constmints given in (6.4) and (6.5). The elements of parent vectm ( P , ) arc the real

power outputs of genaating units distributed uniformly betwm their minimum and

maximum limits.

63.2. Mutation

An offspring P', is created from each parent vector by adding gaussian random

variable with zero mean and standard deviation q denoted as N(0, di).

P'*,= P,i + N(0, a,') fori =1 ,2 ,... p - 1 (6.6)

.-I

whae a, = f3 c (C, / C,)' (P&- - Pmi? 3.1

(6.7)

where p is the d i n g factor, Ci is the total congestion cost ad C- is the minimum

value of congestion cost in the corresponding generation.

In this work, mutation is carried out with non-linear scaling factor. The concept of

non-linear scaling factor was explained briefly in the chapter 2. The created offspring

vector must satisfy the minimum and maximum generation limits of the units and Line

flow constlaints.

633. Competition and W e d o n

The computed parent and offspring vectors are competed for the nwival ad the

best vectors are wlccted in each generation. Initialization and mutation are repeated until

there is no appreciable improvement in the obtained congestion cost. The step-by-step

cornputatid flowchart is given in Fig.6. I .

C l r p L . (b r M d Q pr.a naa rhch r l m crqrrm d K d m Ilrmrucmplmam-4b

' Flg. 6.1. Flowchart for EP b a d Congalioa, Mumgemat

6.4. SIMULATION RESULTS

When the system is insecure and there arc power flow violations in the system,

the objcctive of IS0 is to eliminate the system overload and come up with the corrective

rescheduling to eliminate the violations as f& as possible. Mnimum operating cost,

minimum number of controls, or minimum shift from the optimum opaation may be

uacd as the objective function. Ench Schedule Coordinator may trade hansaction with

ohax before submitting prefmed schedules to the ISO. Thse parties may trade powa

again when preferred schedules arc rrturncd to them for revision. in this wo& the

prcfemd schedules of generation arc obtained in CEED environment. In this procts$

adjustment bids (incmental and darcmental) reprcpmt the economic information on

which the IS0 will base its decisions to relieve congestion. Adjustment of bids include

suggested deviations from preferred loads and genaation schedules provided by SCs. At

each bus, ranges of powa deviations along with deviations in price are submined to the

ISO. An OPF will be solved for the test systems to detcrmiae the preferred schedules of

generation that satisfy the objective of minimizing deviations from the desired

bansaction. In this w o k Evolutionary Programming algorithm is used to reschedule the

generations and to minimirt the congestion cost. The developed EP-based congestion

management is demonstrated on IEEE-30 bus and Indian utility-62 bus systems.

6.4.1. IEEE-30 bus system

It consists of six gcnaating units, 41 traasmission lines, 4 tap changing

tramformas md two VAR SOUIUS. The generator, bus, line, cost and emission data of

the IEEE-30 bus syltan arc given in Appcadix A. To incorporare the resbucturcd marlrel

gcnaaton arc grouped unda two echeduling coodinatom. Two garaabrs GI., and (31.2

refm to SCI and the total load is about 96.7 MW. Four genrmtors Gz.l, G22, G2.3 & 02.4

refer to gemration of SC2 and the total load is about 186.7 MW. The prefarcd schedules

obtained 6um conventional OPF in CEED environrnart an givm in Table 6.1. It also

gives the ~ e n W d c c r a n e n t a l bids submitted by the gcncrntors of cmrcsponding

scheduling coordinator. Four bilatanl transactions and a rnultilatcral hansaction an

carried out to meet out the i n c h demand. The buyer snd seller buss involving in the

wire business and their magnindes clrc given in Table 6.2 & 6.3. Afta the permitted

wheeling bansactions, it was found that lines 2 and 5 were congested

Table 6.1. Pnfemd Sebedula and InJDce Bids - IEEE30 Bus System

Table 63. Dctalls of Bilateral Tmnuctious - IEEE-30 Bus System

Table 63. Details of MdtiLteral Trauudol~ - lEEE -30 Bus System

T n u u d o n

TI TI f 3

7.4

"'Iue O&M*dOn

17 10 15 20

From BU No.

14 16 2s IS

To Bus No.

22 08 0s 24

To relieve the congestion, the gcnetations of SCs are adjusted from the pnfemd

schedules. In the same time, SCs must satisfy the total generation and load profils at all

the times. In this pspa, evolutionary programming algorithm is used to obtain the new

schedules of genetations at each SC by satisfying the power balance equation. The

proposed algorithm uses the incmentaVdccrcrnental bids to relieve the congestion at

minimum cost The rescheduling of genmtom must satisfy the minimum and maximum

limits of genmtors. The obLained minimum congestion cost to reschedule the genmtors

to permit the above whetling tmsrctions is S 279.450. The prefnred schedules and

reschedules of generators of comsponding SCs an given in Fig. 6.2.

6.43. lndira Utility-62 bas System

To validate the performance of the proposed algorithm, Indian utility42

bus system consisting of 19 gennators, 89 (220 kV) lines with I1 tap changing

transformers has been considaed. The total system demand is 2909 MW. The total

system load is md by h e Scheduling Coordinators and their comsponding load

demaads an 906.423 MW, 531.210 MW & 1471.351 MW qmtively. The bus, line,

generator, load, cost and emission data of the tcst system are given in Appcnd~x.B. The

genaator p n f d schedules in CEED environment and incmental and decmental

b i b to relieve the congestion are given in Table 6.4. Two multilataal bansactions arc

carried out in the above test system and the details ~IX given in Table 6.5. It was obscrved

that lina 3 and 6 got c o n g d when the whaling transactions arc pnmined.

Evolutionary ppmming algorithm is applied to relieve the congestion by altaing the

schadula of g d o n and obtllincd congestion cost is Rs. 21 12.462.

Fig. 6.2. Preferred and Rescheduled Genemtor Powen - IEEE - 30 Bus System

Table 6.4. Preferred Sebcduln and lndDec Bids - Indim Utility - 62 Bus System

98

Table 6.5. DeWh of Multilnternl Tnnuetion8 - Indian Utility - 62 Bus System

65. CONCLUSION

The work reported in this chapta deals with an o p W power flow basad

congestion management carried out with bilateral and multilateral hansactions. It was

found lhat the kansmission system was overloaded when the wheeling transactions were

carried out. This congestion was relieved by adjusting the generator setlings with rrspect

to their bids. The proposed congestion management algorithm was demonstrated on IEEE

and Indian utility systems with evolutionary programming algorithm.