Revised Pricing in Case of Market Splitting

Revised Pricing in case of Market Splitting

based on Weighted Average Price in the Two

or More Sub Markets

Annexure I Power Exchange India Limited

Revised Pricing in case of Market Splitting based on Weighted

Average Price in the Two or More Sub Markets

1

CONTENTS

CONTENTS ..................................................................................................................................................................... 1

LIST OF TABLES .............................................................................................................................................................. 1

LIST OF FIGURES ............................................................................................................................................................ 2

1.0 Introduction ...................................................................................................................................................... 3

2.0 Fundamentals of Transmission Congestion ............................................................................................................. 4

2.1 Effects of Congestion ........................................................................................................................................... 4

2.2 Managing Congestion .......................................................................................................................................... 5

3.0 CURRENT PRACTICE IN INDIAN MARKETS ............................................................................................................... 6

3.1 Experience in the Nordic Market ........................................................................................................................ 7

3.2 Short Term Markets in India ................................................................................................................................ 8

4.0 PROPOSED MARKET SPLITTING METHOD .............................................................................................................. 14

4.1 Examples on proposed market splitting method .............................................................................................. 15

5.0 CONCLUDING REMARKS ........................................................................................................................................ 25

References: .................................................................................................................................................................. 28

LIST OF TABLES

Table 1: Share of Indian Short Term Market ................................................................................................................. 9

Table 2: Percentage of Time Instances of Congestion Occurred at the Exchanges ....................................................... 9

Table 3: monthly deficit in energy ............................................................................................................................... 13

Table 4: Price Bids for the Supply and Demand of Electricity ...................................................................................... 16

Table 5: Unconstrained inter regional Flows ............................................................................................................... 17

Table 6: Case I Corridor between Zone 2 and Zone 3 constrained to 20 MWh ........................................................... 18

Table 7: Case 1 Inter regional Flows ............................................................................................................................ 19

Table 8: MCP-MCV for Different Regions .................................................................................................................... 20

Table 9: Flow between different regions ..................................................................................................................... 21

Table 10: Congestion fund contribution ...................................................................................................................... 21

Table 11: MCP and new settlement price.................................................................................................................... 22

Table 12: Pay In and Pay out for sellers and buyers .................................................................................................... 23

Table 13: MCP & MCV values ...................................................................................................................................... 23

Table 14: Flow between different regions ................................................................................................................... 23

Table 15: Congestion fund contribution ...................................................................................................................... 24

Table 16: MCP and new settlement price.................................................................................................................... 25



2

LIST OF FIGURES

Figure 1: Percentage of time congestion occurred during July 2009 .......................................................................... 10

Figure 2: Quantum of Energy Curtailed as percentage of UMCV in July 2009 ............................................................ 10

Figure 3: Average Monthly Congestion Period, Average Difference price between two zones and Congestion

Revenue Paid ............................................................................................................................................................... 11

Figure 4: MCP for the month of july 2009 in the deficit zone ..................................................................................... 12

Figure 5: MCP in the deficit zone for August 2009 ...................................................................................................... 12

Figure 6: price signal during periods of same monthly average congestion ............................................................... 13

Figure 7: Definition of Zones and Flow Paths .............................................................................................................. 16

Figure 8: Unconstrained Market Solution ................................................................................................................... 17

Figure 9: Case 1 Sub Market 1 and 2, MCP & MCV...................................................................................................... 18

Figure 10: Flow Distribution ........................................................................................................................................ 21

Figure 11: Flow Distribution ........................................................................................................................................ 24



3

1.0 INTRODUCTION

One of the biggest challenges in efficient functioning of electricity markets is to handle transmission congestion.

Congestion arises when power flow to a region exceeds the transmission capacity available for that region. Time

differentiation is a well-known characteristic of electricity products owing to its non – storability and congestion

adds a second dimension of spatial differentiation to it.

As the transmission infrastructure does not have infinite capacity, congestion is unavoidable. However, excessive

and regular congestion may have adverse impact on the electricity market. Central Electricity Regulatory

Commission (CERC) through petition 155/2006 (Suo Motu) in the matter of ‘Development of a Common Platform

for Electricity Trading in India’ and guidelines for selling of power on Power Exchanges recognized the fact that

congestion management is a complex issue and needs to be addressed along multiple dimensions. However, there

were relatively few instances of transmission congestion. Therefore, Power Exchange India Limited (PXIL) adopted

classical market methodology for congestion management.

Prices in a competitive electricity market with price taking participants are at marginal cost. Energy pricing and

transmission congestion pricing are implicitly connected. Transmission congestion leads to increase in the price of

electricity for the congested region.

In currently mandated market splitting method at exchanges, the participants on the exchanges and the exchanges

are the ones, which are paying for the opportunity cost of congestion. The bilateral users (medium term and short

term), do not pay this opportunity cost for the transmission system. There exists a substantial bias in favor of

bilateral transactions and against the collective transactions through exchanges. It is a strong disincentive and

affects the bids put by the participants on the exchanges. The present congestion management regime is not

platform neutral, as any market mechanism ought to be and the participants on the exchange are at great

disadvantage in comparison to bilateral and OTC market. Power exchanges are superior to bilateral and OTC

markets in price discovery. The present congestion management regime is one of the reasons in hindering the shift

of trade from inefficient bilateral transactions to collective transactions through exchanges. Our proposed method

will somewhat neutralize this problem and will level the competition across all trading platforms in electricity

markets.



4

2.0 FUNDAMENTALS OF TRANSMISSION CONGESTION

Electricity is transported from suppliers (generators) to consumers through transmission network. In case of

congestion; generation, transmission, and consumption need to be adjusted. Congestion places network

constraints on dispatch and it interferes with the market’s merit dispatch objective of meeting demand at the

lowest possible cost. In the absence of congestion, generation units with the lowest cost supply electricity to meet

the demand of consumers, but when congestion arises, this may not be possible and higher cost generating units

have to be scheduled for dispatch. This introduces risk for the market, which consequently affects bidding,

dispatch pricing, and long-term investment decisions.

2.1 EFFECTS OF CONGESTION

Congestion affects everyone in the market .It affects generators by increasing their financial and physical risks. It

affects buyers by increasing their exposure to physical and financial risks. It increases level of uncertainty about

locational decisions and by increasing the price of electricity; it affects both wholesale and retail customers.

Congestion can introduce two types of risks that participants have to manage:

• The physical or volume risk

• The financial or price risk

The magnitude of these risks depends largely upon the pricing and settlement arrangement in the market and how

closely these rules are related to congestion management.

Both the above incentivize participants to engage in bidding which is not reflective of the marginal cost. The

bidders might bid as must run stations i.e. a very low bid or extremely high bid as they anticipate non – dispatch

due to congestion. Both the bidding patterns would result in disorderly bidding and therefore, skewed-pricing

leading to inefficiency.

Skewed-pricing may distort the investment decisions for both supply and demand side. This includes decisions on

technology, location and timing. In long run, this can weaken the economic signals that support efficient locational

investment decisions by generators and large industrial and commercial users. Locational signals assume greater

importance in current Indian context of ambitious capacity augmentation plans.

In addition to affecting the behavior of the market participants, congestion affects the market as a whole. First, it

increases the overall cost of electricity supply and secondly it can potentially compromise the objective to promote

efficient investment in the sector. It also leads to progressive loss of confidence of the participants at exchange.



5

2.2 MANAGING CONGESTION

Eliminating transmission congestion would be neither cost effective nor efficient. It would lead to over investment

in transmission capacity. Therefore, an optimum level of congestion would exist and it needs to be managed.

A Congestion Management Regime comprises of rules and information for the following elements of an electricity

market:

• Dispatch.

• Wholesale market pricing and settlement arrangements.

• Transmission access pricing, incentives and investment planning.

• Risk Management instruments.

A suitable congestion management regime needs to be devised for physical and operational security of the power

systems. Moreover, Congestion Management Regime has important implication for spot prices and bidding

incentives for market participants. In the long run, the manner in which a market manages congestion affects the

investment decision of new generators and consumers etc.

Some of the common methods used for handling congestion in electricity markets are:

• Redispatching.

• Coordinated auction of generation and transmission capacity (Explicit Auction).

• Nodal pricing or Locational Marginal Pricing.

• Market Splitting.

Redispatching

In case of re-dispatch the system operator, issues suitable dispatch instructions to costly electricity suppliers

located in the area downstream of the congested corridor, to meet the demand in the area. This method has an

underlying assumption that on instructions surplus generation capacity is ready to be dispatched. In Indian context

is not always true, as generating stations and load distribution is skewed. Further, distribution licensees may prefer

load shedding to buying costly power in view of the financial constraints and non-obligated supply agreements

with customers. Therefore, re-dispatch is not suitable in Indian context.

Explicit Auction

Coordinated auction of transmission capacity with generation also involves fundamental changes in market design

and complex policy issues on whether transmission capacity (rights) need to be auctioned or allocated on basis

needs to be addressed. Hence, this method is also not appropriate in the current context of power market

operation.



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Market Splitting

In market splitting, the market is divided into two or more submarkets with congested links acting as the

boundary. Clearing price for each sub market is determined separately taking into consideration the Aggregate

Supply and Aggregate Demand for each sub market accounting for limitations of flow over the congested corridor.

Nodal pricing or Locational Marginal Pricing

Nodal or LMP is a limiting case of market splitting where each node in itself becomes a separate market. Each

node of the power system therefore has a separate price depending upon the cost of energy, cost of transmission,

which includes congestion as well as losses.

3.0 CURRENT PRACTICE IN INDIAN MARKETS

Central Electricity Regulatory Commission (CERC), guided by the National Electricity Policy is working towards one

of its mandate to introduce competition in the electricity market. It approved the setting up of two power

exchanges in India namely the Indian Energy Exchange (IEX) and Power Exchange India Limited (PXIL). The trading

at these two exchanges at present occurs on a Day-ahead Spot (DAS) and Term-ahead products including weekly

and Day Ahead Contingency. The trading volume has picked up in day ahead spot segment but the market is yet to

generate enthusiasm for term ahead products.

The power market in India follows a distributed, portfolio-based concept. The market participants transact energy

on long-term or day-ahead basis on their total energy portfolio. The market structure requires that all market

participants have a balanced portfolio. This implies that for each participant own generation and procurement

must balance the sum of consumption and sales. The markets (Power Exchanges, bilateral contracts and

brokers/OTC services) provide the means for trading to achieve this balance. The Market Operator has no interest

or influence in the actual unit commitment and scheduling of individual assets – it has been left to the market

participants to handle. Any real-time deviation or system operational issues are handled by the independent

system operator(s), who uses unscheduled interchange for real time load-generation balance in the absence of

separate markets for procuring balancing power, capacity reserves and other ancillary services.

Currently Congestion Management for DAS, is handled by market splitting mechanism in accordance with the

directions of National Load Dispatch Center (NLDC). Congestion occurs whenever the state of the transmission grid

is characterized by one or more violations of the physical, operational, or policy constraints against its normal state

or from one of the contingency from a set of specified contingencies. In an initial iteration, bids of market

participants from various bid zones are and an unconstrained market-clearing price (UMCP) is discovered. The

algorithm furthermore calculates the cleared schedules for all market participants based on the UMCP. In the next



7

iteration, the supply/demand balance within each zone is aggregated, and any zonal “imbalance” then represents

the amount of electricity from/to that zone. If this calculated transmission amount exceeds the permitted transfer

capability as decided by NLDC, it amounts to transmission congestion and accordingly, “market splitting” algorithm

is initiated. The congested zone is separated from the rest of the system, and the price is discovered for both the

zones as separate markets. The discovered price is adjusted to drive the supply/demand “imbalance” to a level to

match the permitted transfer capability.

As the market is shallow and the liquidity is low, the present market splitting results in aberrations in price

discovery in the region downstream of the congested corridor. The price of power in that region goes up

significantly compared to the region upstream of the congested corridor. This results in vast difference in area

prices between the regions downstream and upstream of the congested corridor leading to a Congestion Surplus.

3.1 EXPERIENCE IN THE NORDIC MARKET

The Nordic market, consisting of Norway, Sweden, Finland and Denmark uses the classical market-splitting model

for congestion management. The underlying philosophy of congestion management regime is summarized as

under.

System operators understand the physical realties of the network whereas traders / market participants

understand the financial realities of their trades and the underlying physical realties of generation and demand.

Economically efficient congestion management is achieved only by combining this information.

Nordic market therefore use market splitting in the day ahead market combined with coordinated re-dispatching

in the operating phase.

The power exchange splits the bid with geographical bid areas with limited capacities of exchange (as the entire

transmission corridor is available to exchanges); a power pool price is set according to amounts of demand and

generation offered in the whole market area. The TSO then computes a load flow and identifies constrained lines.

It should be pointed out that the same later is used for evaluation of Net Transfer Capability (NTC). Geographical

bid areas are defined across both sides of the bottleneck a new price is defined for each area with flows limited to

the capacity of inter connected lines. Thus each area has its own pool price; area upstream of congestion corridor

having a lower pool price whereas area downstream of congestion corridor having a higher pool price. This price

demand effect results in releasing of congestion by decreased demands in high priced areas and price increases in

low priced areas. The generation side has opposite effect.

Thus, a spot market price is settled for the whole market and there are different price areas according to actual

congestion. Consumers downstream of the congestion will pay higher price, and generators upstream of the



8

congestion will be paid the lower price. The congestion charge is the difference between the prices in downstream

and upstream area. It is collected by the system operator and is used to take measures to progressively reduce

congestion.

In this method, congestion relief relies partly on the market forces, being based on the sale price and purchase

price curves. Trade will be maintained if the price of the area ensures profitability for the market actors. The

market splitting concept thus encourages trading as far as market participants receive ex-ante information about

the profitability of congestion in some areas. Apart from providing, a market signal to facilitate investments, this

method also ensures efficient utilization of transmission network in the longer run. However, this system does not

provide for any incentive to transmission system operator to reduce congestion.

The internal congestions are handled through counter trade or by reducing interconnector capacity at the bidding

area borders. Counter trade is mainly used on intra-day operation and to maintain firm capacity notified in day

ahead markets.

All the trading capacity over the inter connectors between bidding areas is left at the disposal of Nord Pool for the

day ahead trade (Initially, some transmission capacity was reserved for Long Term Contracts but was discontinued

from early 2000). There is no other capacity nomination like yearly or monthly contracts. Therefore, the trade has

to be done through Nord Pool. The intra-day market & with continuous trade, uses the balance on first come first

serve. The unused capacity is then used for the regulation market.

As is evident from above, Nordpool uses market splitting in conjunction with counter trade and balancing power

methods. Moreover, the system also causes no market participant to be assigned privileges on any bottleneck

(which is one of the important features of a liberalized market), as it could be abused by a commercial participant.

In the absence of any possibility to exchange KWh across the bottleneck in order to facilitate trading, market

participants, make a financial contract. The underlying principle is energy is always procurable and by entering into

a financial contract only price is procured (which is fixed). The financial contracts provide the necessary hedge to

price risk. Moreover, the bidding areas are consistent with the geographical areas of the various Transmission

System Operators. It is pertinent to mention here that Nordic market enjoys 72% of the total consumption.

However, this system needs to be used in conjunction with other methods and could potentially pose problems

when used at a larger scale

3.2 SHORT TERM MARKETS IN INDIA

The Indian Short term power market, which includes all the contracts of less than one year period stands about 8%

of total generation. The balance 92% of generation is tied up in long-term contracts. In terms of volumes, the total



9

market in 2009 was about 30.6 billion units (1units =1kWh). The share of exchanges in the total short-term trade

was about 18.92%. Out of the total generation of 750 billion units, the share of total short-term market (excluding

UI volume) is 4.08% and the share of exchanges is 0.77%.

Total Short Term Electricity Transacted (BU)

Total Electricity

Generated (BU)

Traded over exchanges as

percentage of

Bilateral Exchange Total

Short term

market

(Excluding UI)

Total

Electricity

Generated

24.81 5.79 30.6 750 18.92% 0.77%

TABLE 1: SHARE OF INDIAN SHORT TERM MARKET

It is evident from the above that the total quantum of power traded through exchange forms a very small

percentage of the overall generation. In addition, the instances of congestion are tabulated below, for the period

from July ’09 to April’10.

S. No. Month

Average

Congestion (as

percentage of

time during the

month) Zone

Average

Downstream

Price

(Rs./MWh)

Average

Upstream Price

(Rs./MWh)

Difference

(Rs./MWh)

1 Jul-09 54% NR 5,143 4,399 744

2 Aug-09 58% NR 7,736 5,513 2,223

3 Sep-09 48% NR 4,246 3,726 520

4 Oct-09 19% NR 4,246 3,726 520

5 Nov-09 15% NR 4,670 3,976 694

6 Dec-09 27% NR 4,519 2,984 1,535

7 Jan-10 24% NR, S2 4,870 3,349 1,520

8 Feb-10 50% SR 4,389 3,165 1,224

9 Mar-10 60% SR 6,800 4,623 2,178

10 Apr-10 26% SR 7,325 6,062 1,263 TABLE 2: PERCENTAGE OF TIME INSTANCES OF CONGESTION OCCURRED AT THE EXCHANGES

It can be seen from the table that the congestion is transitory in nature and moves from NR to SR. In addition, it is

also dynamic with quantum of congestion varying over the year. The occurrence of congestion not only leads to

curtailment on the volumes of electricity transacted through exchanges (in the year 2009, the curtailment led to

about 17% loss or about 0.99 Billion Units) reducing the liquidity and leads to price rise.

The periods of congestion and quantum of curtailment during the month of July 2009 at PXIL is shown in figure 3

and figure 4 below.



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FIGURE 1: PERCENTAGE OF TIME CONGESTION OCCURRED DURING JULY 2009

FIGURE 2: QUANTUM OF ENERGY CURTAILED AS PERCENTAGE OF UMCV IN JULY 2009

The periods of higher congestion coincided with periods of higher prices owing to higher market demand, which

further worsened the situation. This result in the quantum of congestion revenues increasing very rapidly based on

both the price difference as well as the number of instances. Figure 5 depicts the monthly average price difference

in the two zones after market splitting, average monthly percentage of time congestion occurred and congestion

revenue paid for the month.

0%

20%

40%

60%

80%

100%

0 10 20 30Pe

rio

d (

pe

rce

nta

ge

of

tim

e

du

rin

g 2

4 h

ou

r p

eri

od

)

Day of the Month

Congestion July 2009

Congestion July 2009

0%

20%

40%

60%

80%

100%

0 5 10 15 20 25 30

Pe

rne

tag

e o

f V

olu

me

Cu

rta

ile

d

Day of the Month

Curtailed Volumes as %age of UMCV

Curtailed Volumes

as %age of UMCV



11

FIGURE 3: AVERAGE MONTHLY CONGESTION PERIOD, AVERAGE DIFFERENCE PRICE BETWEEN TWO ZONES AND CONGESTION REVENUE PAID

From the above figures and table, following can be concluded:

• Owing to continuous congestion during the first half of the month, the total liquidity in the market

decreased, indicating the lack of confidence amongst participants about their ability to buy power

through exchanges. This is also evident from the reduced percentage curtailment during the latter half of

the month.

• The average price difference decreased during initial period. Participants take it into their stride by

increasing the prices, which manifested as a subsequent increase in the price difference during the

balance period of the month.

• The congestion revenue follows not only the duration of congestion but also the price difference between

the two areas. Once the bidders resort to disorderly bidding, the congestion revenue increases

irrespective of the market liquidity. Moreover, the congestion rents become higher even when the

congestion decreases.

In order to analyze the price signal emanating from the market, the intraday market cleared price for the month of

July 2009 & August 2009 is shown in figure below.

0%

10%

20%

30%

40%

50%

60%

70%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

May-09 Jul-09 Aug-09 Oct-09 Dec-09 Jan-10 Mar-10 May-10

Pri

ce

Month of the Year

Congestion Revnue

Average difference in two zones

Congestion Revenue

Average Monthly Congestion



12

FIGURE 4: MCP FOR THE MONTH OF JULY 2009 IN THE DEFICIT ZONE

FIGURE 5: MCP IN THE DEFICIT ZONE FOR AUGUST 2009

From the above chart, it can be concluded that:

• There exists no particular pattern in the emanating price signal and the behavior has a large random

component

• The only reason in day-to-day variation in price could be disorderly bidding by participants in

anticipation of expected congestion & priority in allocation.

0

2000

4000

6000

8000

10000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Pri

ce R

s./M

Wh

Hour of the Day

Market Clearing Price in July 2009 in Deficit Zone

10-Jul-09

11-Jul-09

13-Jul-09

0

5000

10000

15000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Pri

ce R

s./M

Wh

Hour of the Day

Market CLearing Price in August 2009 in Deficit Zone

6-Aug-09

10-Aug-09

11-Aug-09



13

The market-cleared price (MCP) for five months having almost same monthly average congestion is plotted in the

figure below.

FIGURE 6: PRICE SIGNAL DURING PERIODS OF SAME MONTHLY AVERAGE CONGESTION

As outlined above there exists no particular pattern in the cleared prices. The regional deficits for the above

periods are tabulated below:

NR (%) WR (%) SR (%) ER (%) NER (%) All India

(%)

Mar 2010 -11.4 -16.4 -9.4 -6.3 -10.8 -11.9

Feb 2010 -9.3 -16.8 -6.2 -2.9 -8.8 -10.2

July 2009 -11.0 -8.8 -5.8 -4 -12.9 -8.3

Aug 2009 -14.7 -12.6 -6.3 -5.2 -13.8 -10.9

Sep 2009 -13.0 -11.6 -5.2 -4.0 -12.5 -9.6 TABLE 3: MONTHLY DEFICIT IN ENERGY

From the above, it is apparent that there exists a weak correlation between the price signal and the actual deficit

in power supply position or the congestion in the physical infrastructure. Moreover, the price signal is also not an

indicator of the nature of the deficit in the system. With the government promoting investments in generation of

electricity across India to cater to regional imbalances and also planning to increase inter- regional capacity for

flow of power in the national grid, the current price signal cannot be relied upon to assist investment planning in

0

2000

4000

6000

8000

10000

12000

14000

16000

0 50 100 150 200 250 300 350 400

Pri

ce o

f D

efi

cit

Ma

rke

t, R

up

ee

s/M

Wh

Periods of Congestion

Erratic Price Signal During Periods of Similar Congestion

Jul-09

Aug-09



14

terms of capacity, location and the type of technology to be used .Moreover, the current regime is only applicable

to exchanges which creates a distortion in favor of OTC markets and against the exchanges. Further, the prices

from the exchange are very visible leading to increased prices in the entire market.

From the above it is amply evident that the current congestion management regime is not particularly suited to

the Indian context and needs revision.

The Honorable CERC mandated at Para 31 (IV) of the Power Market Regulations 2009,

“The Power Exchanges shall carry out Congestion Management using Market Splitting Mechanism in Day Ahead

Market. The Power Exchange can develop its own Market Splitting Mechanism.”

In line with the above and to suit the contextual requirements, PXIL proposes a variant of present Market Splitting

Method, which brings in improvements over the present method. The proposed method is congestion revenue

neutral, brings down the average price of power by still maintaining the locational price signals.

4.0 PROPOSED MARKET SPLITTING METHOD

The proposed market splitting method the price to be paid by buyers will be the weighted average price of the

sellers calculated as follows:

Let the number of surplus markets is ‘m’

Let the number of deficit markets is ‘n’

The index for surplus markets is ‘j’. Therefore, number of surplus markets will go from 1, 2, 3…j….m.

The index for deficit markets is ‘k’. Therefore, number of deficit markets will go from 1, 2, 3…k….n.

During Congestion, the power can flow from multiple surplus markets to multiple deficit markets. It is not necessary that power

will flow from each surplus market to each deficit market.

Settlement price for sellers in surplus market j = P (Settlement for Seller, Surplus (j)) =

�� × �� − ∑ ��

�� + ∑ �� × ��

�� + �� × �. !

��

Settlement price for buyers in deficit market k = P (Settlement for Buyer, Deficit (k)) =

�� × �� − ∑ ��"

�� + ∑ �� × �� "

�� + �� × �. !

��



15

MCP (Surplus (j)) = Market Clearing Price for surplus market j.

MCV (Surplus (j)) = Market Clearing Volume for surplus market j.

MCP (Deficit (k)) = Market Clearing Price for deficit market k.

MCV (Deficit (k)) = Market Clearing Volume for deficit market k.

$%& = Flow from surplus market j to deficit market k.

Values for many $%& will be zero if there is no flow between these two regions.

�� = 0 (If no flow between surplus region j and deficit region k)

4.1 EXAMPLES ON PROPOSED MARKET SPLITTING METHOD

The proposed market splitting method is discussed through the following example.

Assumptions:

• Zone 1 with Generator G1 and loads A & E

• Zone 2 with Generator G2 and load B

• Zone 3 with Generator G3 and load C

• Zone 4 with Generator G4 and load D

Inter regional Flow Paths:

• Zone 1 to Zone 2






Average Price in the

FIGURE

The price bids for the different generators (supply) and loads (Demand) for a particular period are shown below.

Price

Demand

A B C D E

1.4 200 230 220 200 150

1.6 200 230 220 200 150

1.8 200 230 220 180 150

2 175 192 220 170 150

2.2 149 153 163 150 100

2.4 141 115 147 150 100

2.6 129 77 110 100 100

2.8 119 38 73 80

3 111 0 37 60

3.2 103 0 0 40

3.4 0 0 0 20

3.6 0 0 0 0

3.8 0 0 0 0 TABLE 4: PRICE BIDS FOR THE S

The colors on the bids indicate the geographical zone. The Market Cleared Volume (MCV) and Market Cleared

Price (MCP) for the uncongested case are

•Generator G4

•Load D

•Generator G1

•Loads A & E



FIGURE 7: DEFINITION OF ZONES AND FLOW PATHS

nerators (supply) and loads (Demand) for a particular period are shown below.

Aggregated

Demand,

MWh

Supply

G1 G2 G3

150 1000 0 0 0

150 1000 0 0 0

150 980 0 0 0

150 907 213 0 75

100 715 227 200 75

100 653 240 218 75

100 516 253 237 75

50 360 267 255 150

50 258 280 273 150

50 193 280 292 150

50 70 280 310 150

50 50 280 310 150

50 50 280 310 150 PRICE BIDS FOR THE SUPPLY AND DEMAND OF ELECTRICITY


are worked out as per the figure below.

•Generator G3

•Load C

Generator

•Generator G2

•Load B

Generator

Loads A & E

Zone 1

Zone 2

Zone 3

Zone 4


Two or More Sub Markets

16

nerators (supply) and loads (Demand) for a particular period are shown below.

Aggregated

Supply,

MWh G4

0 0

0 0

200 200

200 488

213 715

225 758

235 800

250 922

250 953

250 972

250 990

250 990

250 990




FIGURE

The MCP and MCV for the unconstrained market are 715 M

are as indicated below.

• Zone 1, G1 = 227 MWh, A = 149 MWh and E = 100 MWh. Net

• Zone 2, G2 = 200 MWh, B = 153 MWh. Net inter regional outflow of 47 MWh

• Zone 3, G3 = 75 MWh, C = 163 MWh. Net inflow of 88 MWh

• Zone 4, G4 = 213 MWh, D = 150 MWh. Net outflow of 63 MWh

Inter regional flows are as under:

From

Zone 1

Zone 2

Zone 3

Zone 4

TABLE

The flow scheduling has been done on

Case I: Let us assume that there is congestion on the

on it. Between Zone 4 and Zone 3, no flow is allowed



FIGURE 8: UNCONSTRAINED MARKET SOLUTION

The MCP and MCV for the unconstrained market are 715 MWh and Rs. 2.2 respectively. The schedules and flows

Zone 1, G1 = 227 MWh, A = 149 MWh and E = 100 MWh. Net inter regional inflow of 22 MWh

Zone 2, G2 = 200 MWh, B = 153 MWh. Net inter regional outflow of 47 MWh

Zone 3, G3 = 75 MWh, C = 163 MWh. Net inflow of 88 MWh

Zone 4, G4 = 213 MWh, D = 150 MWh. Net outflow of 63 MWh.

Prioritized Distribution

Flow To

Zone 1 Zone 2 Zone 3 Zone 4

0 0 0 22

0 0 -47 0

0 47 0 41

-22 0 -41 0

TABLE 5: UNCONSTRAINED INTER REGIONAL FLOWS

The flow scheduling has been done on PQT priority basis wherein the priority is decided based on Price

congestion on the corridor between Zone 3 to Zone 2 and only 20MWh can flow

no flow is allowed.



17

h and Rs. 2.2 respectively. The schedules and flows

inter regional inflow of 22 MWh

basis wherein the priority is decided based on Price.

corridor between Zone 3 to Zone 2 and only 20MWh can flow



The market therefore splits between Sub Market 1 and Sub Market 2. Sub Market 1 comprises of Zone 1, Zone 2 &

Zone 4 whereas Zone 3 is considered as Sub Market 2. The Aggr

markets are plotted with revised configu

Price

Demand

A B C D E

0.01 200 230 220 200

1.4 200 230 220 200

1.6 200 230 220 200

1.8 200 230 220 180

2 175 192 220 170

2.2 149 153 220 150

2.4 141 115 200 150

2.6 129 77 180 100

2.8 119 38 180 80

3 111 0 180 60

3.2 103 0 160 40

3.4 0 0 100 20

3.6 0 0 100 0

3.8 0 0 100 0

20 0 0 0 0

TABLE 6: CASE I CORRIDOR BE

FIGURE




Zone 4 whereas Zone 3 is considered as Sub Market 2. The Aggregate Demand and Supply position for the two

are plotted with revised configuration of C & G3 and is as shown in table 6.

Aggregated

Demand 1,

MWh

Aggregated

Demand 2,

MWh

Supply

E G1 G2 G3 G4

150 800 220 0 0 20 0

150 800 220 0 0 0 0

150 800 220 0 0 0 0

150 780 220 0 0 0 200

150 707 220 213 0 75 200

100 572 220 227 200 75 213

100 526 200 240 218 75 225

100 426 180 253 237 75 235

50 307 180 267 255 140 250

50 241 180 280 273 140 250

50 213 160 280 292 140 250

50 90 100 280 310 140 250

50 70 100 280 310 140 250

50 70 100 280 310 140 250

0 20 0 280 310 140 250

: CASE I CORRIDOR BETWEEN ZONE 2 AND ZONE 3 CONSTRAINED TO 20 MWH

FIGURE 9: CASE 1 SUB MARKET 1 AND 2, MCP & MCV



18


te Demand and Supply position for the two

Aggregated

Supply 1,

MWh

Aggregated

Supply 2,

MWh,

0 0 20

0 0 20

0 0 20

200 200 20

200 413 95

213 640 95

225 683 95

235 725 95

250 772 160

250 803 160

250 822 160

250 840 160

250 840 160

250 840 160

250 840 160



19

The MCP/MCV for sub market 1 and sub market 2 are Rs 2.2/572 MWh and Rs. 3.11/160 MWh respectively. Sub

Market 1 is surplus and Sub Market 2 is deficit. The price signals also manifest the same. The revised flows are

depicted in table 7.

From

Flow To

Zone 1 Zone 2 Zone 3 Zone 4

Zone 1 0 0 0 0

Zone 2 0 0 20 0

Zone 3 0 -20 0 0

Zone 4 0 0 0 0

TABLE 7: CASE 1 INTER REGIONAL FLOWS

In case of the classical market splitting method, the congestion revenue would have been calculated as per

following equation.

�'�(��'�)�*�� = �',��','��-��(�'�.�'��/'� × ��-��'��-��,'".��

However, in the proposed method, all the buyers in the region would pay the weighted average cost of power for

the region. Therefore the buyers would pay,

012331456782 =140 × 3.11 + 20 × 2.2

160= ?2. 2.996/BCℎ

The sellers would be paid as per the MCP of their respective markets. For the scheduling part, it would be

prioritized with priority of price being the highest followed by quantity. However, equitable and equal methods of

scheduling can also be used.

It is evident from the above case that the locational price signal is intact but the cost of power purchase has been

brought down.

Though the above methods brings down the price of the power in the deficit region keeping the locational signal

intact, it might lead to very high bidding by participants for prioritized scheduling, resulting into price increase.

Moreover, only one of the affected participants is being benefitted. In order to overcome this disadvantage and be

non-partisan to the participants, alternative 2 is proposed.

Alternative 2

In this case, the sellers as well as the buyers would be obligated with the weighted average charges. The power

flow on the congested corridor would be charged at the midpoint of the two MCPs. Therefore,

012331456782EFG7HEIE3JK8B73 =140 × 3.11 + 20 × (2.2 + 3.11)/2

160= ?2. 3.053/BCℎ

M8EI73127NN782EF258ON52JK8B73 =552 ∗ 2.2 + 20 ∗ (2.2 + 3.11)/2

572= ?2. 2.216/BCℎ



20

Congestion fund (in thousands) =

�20 × �3.11 − 2.2 � = Rs. 18.2

Total amount paid by the buyers (in thousands) =

�572 − 20 × 2.2 + 160 × 3.11 = ?2. 1712

Total amount paid to the sellers (in thousands) =

572 × 2.2 + �160 − 20 × 3.11 = ?2. 1693.8

According to new price, total amount paid by the buyers (in thousands) =

�572 − 20 × 2.2 + 160 × 3.053 = ?2. 1702.88

According to new price, total amount paid to the sellers (in thousands) =

572 × 2.216 + �160 − 20 × 3.11 = ?2. 1702.95

We can see that after rounding, congestion fund is distributed evenly between buyers and sellers in surplus and

deficit markets.

Thus, in this case as the MCPs of the two market price has a bearing on the price which participants would be

obligated with, the incentive to disorderly bidding is inherently reduced. Moreover, the method is non-

discriminatory to all the participants at the exchange.

Example 2

This example is for multiple surplus and multiple deficit regions.

Number of surplus regions = 2

Number of deficit regions = 3

This is the price volume data for all regions.

SR1 SR2 DR1 DR2 DR3

MCP(Rs.) 3 5 7 8 9

MCV(MW) 250 360 70 105 55

TABLE 8: MCP-MCV for Different Regions



21

TABLE 9: Flow between different regions

Total Flow = 50 MW Total Flow = 60 MW

20MW 10MW 25MW 25 MW

30MW

Total congestion Fund

FIGURE 10: FLOW DISTRIBUTION


�20 × �7 − 3 + 30 × �8 − 3 + 10 × �7 − 5 + 25 × �8 − 5 + 25 × �9 − 4 � = Rs. 425

Congestion rent for flow between SR1 and DR1 80





Total Congestion(Rs. in thousands) 425 TABLE 10: Congestion fund contribution


70 × 7 + 105 × 8 + 55 × 9 + �250 − 20 − 30 × 3 + �360 − 10 − 25 − 25 × 5 = ?2. 3925

To/from SR1 SR2

DR1 20 10

DR2 30 25

DR3 0 25

Surplus Region 1 Surplus Region 2

Deficit Region 3 Deficit Region 2 Deficit Region 1



22


�70 − 20 − 10 × 7 + �105 − 30 − 25 × 8 + �55 − 25 × 9 + 250 × 3 + 360 × 5 = ?2. 3500

M8EI731U7OKEG3127NN782EFV58ON52JK8B731=

(W!� − X� − W�) × X + X� ×(X + Y)

W+ W� ×

(Z + X)W

W!�= X. [\

M8EI731U7OKEG3127NN782EFV58ON52JK8B732 =

(X\� − �� − W! − W!) × ! + �� ×(! + Z)

W+ W! ×

(! + Y)W

+ W! ×(! + ])

WX\�

= !. WZ

012331U56782EF^7HEIE3JK8B731 =

�Z� − �� − W� × Z + �� ×�! + Z

W + W� × �Z + X W

Z�= \. W]

012331U56782EF^7HEIE3JK8B732 =

(��! − X� − W!) × Y + X� ×(X + Y)

W+ W! ×

(! + Y)W

��!= \. ]X

012331U56782EF^7HEIE3JK8B733 =

�!! − W! × ] + W! × �! + ] W

!!= Y. �]

SR1 SR2 DR1 DR2 DR3

New P(in Rs) 3.46 5.27 6.29 6.93 8.09

MCP(in Rs) 3 5 7 8 9

TABLE 11: MCP and new settlement price


��250 − 20 − 30 × 3 + �360 − 10 − 25 − 25 × 5 + 6.29 × 70 + 6.93 × 105 + 8.09 × 55 = ?2. 3712.5


�250 × 3.46 + 360 × 5.27 + �70 − 20 − 10 × 7 + �105 − 30 − 25 × 8 + �55 − 25 × 9 = ?2. 3712.5



23

SR1 SR2 DR1 DR2 DR3 Rs(in thousands)

Buyer 600 1500 440 727.5 445 3712.5

Seller 865 1897.5 280 400 270 3712.5

TABLE 12: Pay In and Pay out for sellers and buyers

We can see that the buyers and sellers have equal share from the congestion fund.

Example 3

This example is for multiple surplus and multiple deficit regions.

Number of surplus regions = 1

Number of deficit regions = 3

This is the price volume data for all regions.

SR1 DR1 DR2 DR3

MCP(Rs.) 4 5 6 4.5

MCV(MW) 475 65 80 125

TABLE 13: MCP & MCV VALUES

To/from SR1

DR1 10

DR2 40

DR3 25

TABLE 14: Flow between different regions



24

Total Flow = 75MW

10MW 40MW 25MW

FIGURE 11: FLOW DISTRIBUTION


�10 × �5 − 4 + 40 × �6 − 4 + 25 × �4.5 − 4 � = Rs. 102.5



Congestion rent for flow between SR1 and DR3 12.5

Total Congestion(Rs. in thousands) 102.5 TABLE 15: Congestion fund contribution


65 × 5 + 80 × 6 + 125 × 4.5 + �475 − 10 − 40 − 25 × 4 = ?2. 2967.5


�65 − 10 × 5 + �80 − 40 × 6 + �125 − 25 × 4.5 + 475 × 4 = ?2. 2865

M8EI731U7OKEG3127NN782EFV58ON52JK8B731=

([Z! − �� − [� − W!) × [ + �� ×([ + !)

W+ [� ×

([ + \)W

+ W! ×([. ! + [)

W[Z!

= [. ��Y

012331U56782EF^7HEIE3JK8B731 =

�\! − �� × ! + �� ×�! + [

W\!

= [. ]WX

012331U56782EF^7HEIE3JK8B732 =

(Y� − [�) × \ + [� ×(\ + [)

WY�

= !. !

Deficit Region 3 Deficit Region 2 Deficit Region 1

Surplus Region 1



25

012331U56782EF^7HEIE3JK8B733 =

��W! − W! × [. ! + W! × �[. ! + [ W

�W!= [. [!

SR1 DR1 DR2 DR3

New P(in Rs) 4.108 4.923 5.5 4.45

MCP(in Rs) 4 5 6 4.5

TABLE 16: MCP and new settlement price


��475 − 10 − 40 − 25 × 4 + 4.923 × 65 + 5.5 × 80 + 4.45 × 125 = ?2. 2916.24


�475 × 4.108 + �65 − 10 × 5 + �80 − 40 × 6 + �125 − 25 × 4.5 = ?2.2916.30

We can see that after rounding the buyers and sellers have equal share from the congestion fund.

5.0 CONCLUDING REMARKS

In the preceding analysis, the following key issues have been brought out with respect to the current classical

market splitting method mandated by CERC and being followed by exchanges.

Applicability:

• This method has been adopted in Nordic market in conjunction with re-dispatch during operational phase.

Moreover, counter trade provide means for hedging the price risk arising out of congestion in the Nordic

market. It is also assumed that surplus supply is available to be dispatched at a higher cost

Prior to establishment of Nordpool, trading of electricity between the countries was enabled through Nordel,

an organization set up in the 1960s to promote cooperation amongst the largest electricity producers in each

country. Nordel was based on the principle that each country would build enough generating capacity to be

self-sufficient. Trading was meant to achieve optimal dispatch of a larger system and investment in

interconnection was generally based on net exports but on expected savings from pooling available

generation capacity. The countries exchanged information about their marginal cost of production. When

there was a difference, trading took place, at a price, which would ideally be average of the two marginal

costs. The cost plus approach led to over investment and poor return on equity but prior to introduction of

competition, the operating efficiencies of the utilities were high.



26

Norway started reforms and a power market was opened in 1992. Owing to almost 100% hydro capacity, the

prices in the spot market were very volatile owing to uncertainty in the hydrological flows. In Sweden, the two

largest companies enjoyed 75% of the generating capacity and market would have been difficult to manage. A

combined Norwegian, Swedish market would address the problem of both the countries. A decision was

therefore made to establish a joint electricity trading exchange. The Smooth transition can be attributed to

the long tradition of cross – border bilateral energy trade, cooperation and the existence of cross- border

transmission.

In Indian context, the spatial differentiation of supply as well as the load along with none of the region being

self sufficient (except NER) necessitated regional integration leading to formation of national grid. The deficit

in different zones was dynamic as well as transitory. Therefore, the basic premise of power trading in Indian

case is different from Nord pool.

• The classical method of market splitting has its limitation when applied on a larger scale

Owing to uneven spatial distribution of load centers and generation plants in large zones, there exists a

requirement of energy transport across large distances and therefore, exists a possibility of multiple price

levels. Secondly, there exists a diverse generation mix in terms of technology and storage capabilities (Hydro

Power) that cause time and price dependent energy flows. A priori declaration of the zones in case of

congestion forces one-price signal for the zone. Therefore, in order to have a better correlation between price

signal and congestion, the number of zones needs to be increased leading to higher complexities.

Historically, planning in Indian case was done at regional basis. Currently, we operate in five regional grids

spread over 28 States & 6 Union Territories leading to multiple layers of losses. Owing to heterogeneity across

different regions and sub-regions (due to multiple states), the impact of less number of bid areas gets

pronounced. Currently, we have ten bid zones, which need to be increased to fall in line with the geographical

boundaries of STUs. Even further, for some of the larger states there could be multiple bidding areas.

• Nordic market enjoys 72% of the market share

The total short-term market in India is roughly 8% of the total generation. Even out of total short term market,

the Day Ahead Market through exchanges contributes only about 19%.

• The entire inter regional capacity lies with the Nordic market. Even the reservations for the long term

players have been taken back since early 2000

We follow allocation of transmission corridors and the prioritization is for long-term users, followed by

medium term users and finally short-term users. Even for short-term users, the capacity left after short-term



27

open access customers is available to exchange. This introduces an arbitrage between different segments of

short-term consumers. On top of it, the balancing mechanism gives flexibility on quantum of usage.

• In general, the geographical areas have been defined in line with TSO areas

The geographical division of the bid areas has been done based on the operating regime of the grid. Each of

the five regional grids has been divided into two bid zones. Therefore, currently we have multiple transmission

operators in each bid areas.

Assumptions:

• As per the CERC ‘Staff Paper on Developing a Common Platform for trading’, it was assumed that there

are practically nil instances of congestion. However, the instances of congestion are transitory, dynamic

and overlap with the periods of high demand. The assumption, therefore, does hold in the current

context.

Part adoption of principles i.e. market splitting along ten bidding zones which are not in sync with the lines of

control boundaries of system operation and short term market constituting a very small fraction of the market,

clubbed with some of the assumptions not holding good in the current scenario, has led to the following effects:

• Introduces price risk as well as quantity risk for the participants at exchanges

• Incentivizes the participants at the exchange towards disorderly bidding, thereby increasing the price of

electricity

• The congestion revenue is not only a function of instances of congestion but also of the discovered price

• Progressive loss of confidence of the participants at the exchanges to buy short term power

• In the long run, present mechanism might not provide the right investment signals with respect to the

location as well as type of generation.

• Buyers as well as sellers at the exchanges are not content owing to sellers being paid less and buyers

being charged high

In view of the above, we propose to adopt a new congestion management regime to better suit the current

context. We propose to adopt the Alternative 2, for which the applicability and advantages are mentioned

below.

In the proposed modified market splitting method, wherein the sellers as well as the buyers would be

paid/charged based on the weighted average cost of electricity. The following issues have been brought out

in the preceding sections.



28

Applicability:

• Currently, participants at exchange constitute only 0.77% of the total generation in India.

• In the absence of ex ante information about the probability of congestion, participants are not in a

position to plan their bids.

• There exists an arbitrage between the corridor allocations amongst the short-term players, medium and

long-term players.

• There are no long-term products available to plug the arbitrage between the short-term markets.

Advantages:

• Would reduce the cost of procurement of power for buyers, thereby reducing their financial burden. The

purchase cost of electricity being passed through in tariff would lead to some reduction in retail cost of

electricity

• Reduce the disorderly bidding and therefore, reduce inefficiencies in price discovery

• Retains locational commercial signals for investment planning

• Capable of splitting the markets into ‘n’ sub markets. Moreover, with development of electricity markets,

the sub market splitting would be required to match the SLDC boundaries

• Is non partisan and non discriminatory to all participants

References:

1. CERC, Development of a Common Platform for Electricity Trading in India,2006

2. CERC, Annual Report of the Market Monitoring Cell 2009

3. CERC, Power Market Regulation 2010

4. Power Exchange Implementation in India and Congestion Management in Multi Exchange Scenario – S K

Soonee & others 2009

5. Nordpool Spot – Annual Report 2009

6. Congestion Management in Liberalized Electricity Markets – Theoretical Concepts & International

Applications – Thilo Krause

7. ETSO – Development and Implementation of a Coordinated Model for Regional and Inter Regional

Congestion Management

8. Report of the Working Group on the Development of Nordpool and Exchange Operations

9. Congestion Management Guidelines – NordREG

10. Congestion Management in Nordic Market – Evaluation of different market Models, Final Report

Documents

Revised Pricing in Case of Market Splitting