Engineering, 2013, 5, 115-120
doi:10.4236/eng.2013.51b021 Published Online January 2013 (http://www.SciRP.org/journal/eng)
The Research about the Trans-provincial Centralized
Bidding Trading Market of East China Power Grid --I：
Empirical Analysis
Bin Zou 1, Xiao-jun Wang 1, Xiao-gang Li 2, Li-bing Yan g2
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
2East China Power Grid Co., Ltd. Shanghai, China
Received 2013
Abstract
The clearing price and bidding price in electricity market are two key indicators to measure whether it is reasonable or
not. Based on the grey incidence analysis, this paper studies the correlation coefficient between the clearing price and
bidding price with the generation cost, the supervision and rules of the market, the supply and demand situation, the
behavior of market members o ver the same period, which is based on the actual data of the trans-provincial centralized
trading market of East China Power Grid. The results show that the factors affecting the clearing price and bidding price
from largest to smallest are generation cost, supervision and rules of the market, the supply and demand situation, the
behavior of market members. The conclusion is that the trans-provincial tradin g platform of East China Po wer Grid is a
reasonable regional market which can discover the market cost, and regulate the market supply and demand balance,
and promote healthy competition.
Keywords: East China Po wer Grid; Trans-provincial Centralized Bidding Trading; Clearing Price; Bidding Price; Grey
Incidence Analysis
1. Introduction
In China, the split of power plants and power grids had
been completed in 2002[1]-[4], but the electricity market
has not been established. In this case, each unit was
allocated a certain amount of generating energy during
the next year according to the installed capacity of the
unit, the unit type and the forecasting load. Then, the
po wer system operation sched ules the unit outp ut to meet
the actual load and guarantees annual generating energy
of the unit equal to the value to be assigned. And
electricity is purchased by the grid company with the
benchmark price that is validated by the National
Devel opme nt and Refor m Co mmissi on. This app roach is
similar to the cost-based economic dispatch, which is
called as planned generation in China.
Even so, there is still some effort to adjust power
schemes using market competition in China's power
industry, the Trans-provincial Centralized Bidding
Trading Market of East China Power Grid（TPMECPG）
is one of these efforts[5]. East China Power Grid is an
interconnected grid, which contains five provinces, and
each province is a control zone. Each provincial power
grid company is responsible for its own electricity and
power balance. But, in order to meet the load demand,
about 1% of the total energy needs to be exchanged
between these provinces. The TPMECPG is an electric ity
auction, and its purpose is to form energy exchange
programs between provinces.
The marke t co nti nues to r un t wo a nd a ha lf year s fro m
December 2008 to June 2011.The market prices have
been lower than the audited benchmark price of the
government. Coal prices rose too fast, the government
requires that all wholesale electricity prices must be the
benchmark price, the market pause run.
The market shows some interesting phenomenon. For
example, the price has been rising from the second half
of 2010; however, it has been lower than the
government's benchmark price. Even so, the generation
companies are still willing to participate in the market
competition. What is the true power of electricity price
fluctuations? It is because of the market power of
generation companies or rising costs? Why power
generation co mpanies are willing to participa te in market
competition, even if the market price is lower than the
benchmark price?

B. ZOU ET AL.
11 6
This paper is one of the two-part series. This article
uses the grey incidence analysis method, based on actual
data, to study the correlation coefficient between the
clearing price and bidding price with the generation cost,
the supervision and rules of the market, the supply and
demand situation, the behavior of market members.
Another paper explains the cause of the result of the
operation of the market.
2. Brief Introduction of the Market
The TPMECPG adopts the two-sides-bidding method
and the none-united-clearing method. The doubt is that in
this kind of electricity market the clearing price can
correctly reflect the cost and the supply and demand
situation or not.
The buyers are the provinces that lack electricity in
East China region, while the sales are generators that
have a surplus of generating capacity [6]. The two sides
bid for buying (sale) electricity amount and price, and
the traded power will be assigned to each hour on
average in the next month. The workflow of the
TPMECPG is shown below in figure 1.
1-promulgate messages
Based on the
electricity demand, the
East China electricity
dispatching trade
organization
promulgate trading
messages to all the
registered members
Included messages:
buyers, demand amount,
demand curve, trading
time, trading electric
curve, limited capacity,
arranged capacity
2-Power Grid of all provinces audit
the messages
Before 11:00 of the
second workday, report
the ceiling of bidding
amount to the electricity
dispatching trade
organization of East
China Power Grid
Before 12:00, the
electricity dispatching
trade organization of
East China announce
the messages after
auditing
3-bidding time
The bidding time is 12:00-15:00 of the second
workday. During this period, the demand
provinces and generators bid for demand price
and supply price.
4-market clearing
Before 10:00 of the third workday, the market
clear under no constraints.
5-security checking
Before 17:00 of the forth workday, Checking
security to get the final clearing result and
announce it through the electricity trade operation
system of East China.
6-sign contracts
Before 12:00 of the fifth workday, East China
Power Grid signs contracts with related provincial
Power Grids. Before 17:00 of the fifth workday,
provincial Power Grids sign contracts with
generators.
Fig.1 workflow of the TPMEC PG
The market clearing mechanism adopts match-clearing
mechanism which matches the largest demand bidding
price to the smallest supply bidding price, and the
clearing price is half of the sum of demand price and
supply price except the transmission fee of provincia l
power grid and trans-provincial trading loss. Figure 2
shows the clearing procedure.
In figure 2, the dashed line divides the trading blocks,
and the clearing price is average price of demand and
suppl y side. As t rans-pr ovinc ial tr ading, the tr ansmission
fee and loss must be considered. If the difference
between demand price and supply price is less than
transmission fee and loss compensation fee, the deal will
be cancelled.
The transmission fee and loss compensation fee
belongs to demand-side power grid, while the
trans-provincial transmission fee and loss compensation
fee belongs to the East China Power Grid. The loss
compensation fee is 1% of benchmark price, and the
transmission fee is the difference between demand price
and supply price in principle. But if the sum of
transmission fee and loss compensation fee is more than
0.03￥/KWh, then the transmission fee is 0.03￥/KWh.
deal amount
supply cumulative curve
demand cumulative curve
bidding price
￥/MWh
bidding amount
MWh
clearing
price
first
trading
block
s
e
c
o
n
d
b
l
o
c
k
third
trading
block fourth
trading
blokc fifth trading
block
difference between demand
and supply price less than
loss, not trade
Fig.2 clearing price in the TPMECPG
That is, the sellers’ settlement tariffs of accepted
quantity is:
( )
,,
1
2
bid bid
sellsellbuyHD losssell transmision
ρρρρ ρ =+− − (1)
Where
bid
sell
ρ
denotes the ask price of sellers in the
matchmaking,
bid
buy
ρ
denotes the bid price of buyers in the
matchmaking,
,,
,
sell lossHD loss
ρρ
denote the transmission net
loss of power suppliers and the East China Power Grid
respectively, which can be calculated as
,,
= 1%
sell lossHD lossbuyer
B
ρρ
= ×
(2)
Where
buyer
B
denotes benchmarking price of
p owe r-demand province.
The bu yers’ settlement tariffs of accepted quantity is
,,buysellHD losssell transimission
ρ ρρρ =++
(3)
Where the transmission payment can be calculated as
follows:
, ,,
-
bid bid
sell transmisionbuysellHDlosssell loss
ρρρρ ρ =−−
(4)
Where
,sell transmission
ρ
denotes the payment of trans-provincial
transaction with an upper limitation constrain of 0.03
￥/KWh.
The traded amount of electricity in this
trans-provincial centralized bidding platform increases
year by year, from 1282 million KWh in 2009 to 3926
million KWh in 2010, and 2200 million KWh in first half
of 2011. The average price is shown in Figure 3 where
the absci ssa represents the 15 trades from Jan. of 2010 to
Jun. of 2011, and the ordinate represents the average
clearing price. It can be seen from figure 3 that the
average price changes over ti me , and the average price in
2010 is relatively lower while the average price in 2011
is relatively higher. What makes the price fluctuations?
The paper utilizes grey incidence analysis method to
have empirical analysis for this prob lem.

B. ZOU ET AL.
Copyright © 2013 SciRec. ENG
11 7
Fig.3 average clearing price of the TPMECPG
3. Grey Incidence Analysis
The grey incidence analysis [7] was first mentioned by
Professor Deng Julong in the 1980s. This method
measures the correlation degree of different factors based
on the similar or different degree of its inside factors.
And this method avoids the shortage of statistics, for
example, the regre s sio n a na l ysis me t h o d and t he variance
analysis met hod . Whether the sample data is mor e or l ess
or whether the sample data has law or not, this method
can get perfect results [8].
The paper makes the price sequence as referential
sequence, and chooses generation cost sequence,
benchmark price sequence, supply and dema n d situation
sequence and market power sequence as contrasting
sequences. Thr o ugh c ompari s on o f grey incid ence sort of
these contrasting sequences, we can elect the most
influential factor of all the factors.
3.1. Grey Incide nce Factors of Clearing Price
1). Generation Cost
The coal cost is the main operation cost of coal units.
Generally, 70% of generation cost of coal units is coal
cost. In the paper, the coal price is utilized to represent
the ge nerati on cost . Generally speaking, the coal price of
different generators is different because of different
transmission fee. But grey incidence analysis cares more
about the dynamic changes of the data, though different
generators indifferent provinces have different coal price,
but the change trend is the same. So, this paper uses the
price of high-quality-mixed -coal in Shanxi province to
represent coal price.
2). Benchmark Price
In the trans-provincial centralized bidding trading
platform, 99% of electricity is on grid through schedule,
and the on grid price is the benchmark price of
corresponding province; this is actually different from
the situation of full electricity competition. So, it
becomes an important item that if the clearing price is
influenced by the benchmark price.
3) Supply and Dem and Situation
In East China region, some provinces have extra
electricity, while others are lack of electricity. The
electricity surplus and deficiency is defined below: it
equals the integrated adjustable capacity minus the
reserve capacity and the maximum demand. If it is
negative, it means this province is short of electricity, so
it has to buy some from other provinces, and conversely
vice versa.
In East China region, the provinces that have to buy
electricity are Jiangsu province and Zhejiang province,
while the provinces that can sale electricity are Anhui
province and Fujian province. So, when considering the
electricit y surp lus and deficienc y, we only consider these
four provinces.
4). Market Member Behavior
The behavior of market members is a key factor in
electricity market. The strategic behavior of market
members is hard to describe. Overall, the main indicator
of market power is HHI (Herfindahl-Hirschman Index)
[9], it is a integrat ing i ndex of measurin g the monopo listic
degree of a certain industry. The HHI is sum of the
square of the percentage of profit for all competitors in
this i ndustr y. A bigger HHI mea ns a higher mo nopoli stic
degree and bigger market power.
The trans-provincial centralized bidding market is an
electricity market beside the planned generation; it’s not
fit to use the unit capacity as HHI indictor. In the paper,
the amount of bidding electricity is utiliz e d.
Figure 4 shows the relationships of the correlation
factors of the clearing price.
3.2. Calculation Steps of Grey Incidence Analysis
Method
1). Definition of Grey Incidenc e
000 0
((1), (2),..., ())Xx xxn=
is the referential sequence,
for example, the clearing price or bidding price, and
n
means that the sequence has
n
numbers. And there are
clearing price of trans-provincial centralized bidding trading market
surpervision
b
e
n
c
h
m
a
r
k
p
r
i
c
e
o
f
A
n
h
u
i
b
e
n
c
h
m
a
r
k
p
r
i
c
e
o
f
F
u
j
i
a
n
b
e
n
c
h
m
a
r
k
p
r
i
c
e
o
f
J
i
a
n
g
s
u
b
e
n
c
h
m
a
r
k
p
r
i
c
e
o
f
Z
h
e
j
i
a
n
gb
e
n
c
h
m
a
r
k
p
r
i
c
e
o
f
S
h
a
n
g
h
a
i
supply and demand
situation
e
l
e
c
t
r
i
c
i
t
y
s
u
r
p
l
u
s
a
n
d
d
e
f
i
c
i
e
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c
y
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f
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h
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r
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y
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r
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l
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e
f
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e
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c
y
o
f
Z
h
e
j
i
a
n
g
cost
H
i
g
h
-
q
u
a
l
i
t
y
-
m
i
x
e
d
c
o
a
l
p
r
i
c
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i
n
S
h
a
n
x
ibehavior of market members
H
H
I
Fig.4 relationships of t he correlation factors of the
clearing price

B. ZOU ET AL.
11 8
m
correlation factors,
((1), (2),..., (),),1,2,...,
iii i
Xxxxn im= =
,
which also named contrasting sequences.
Then the grey incidence of the k-th data of
i
X
and
0
X
is defined as below:
00
000
minmin|( )( )|maxmax|( )( )|
((),( ))| ()()|maxmax| ()()|
ii
ik ik
iii
ik
xk xkxk xk
xk xkxk xkxk xk
ρ
ξρ
− +×−
=− +×−
(1)
ρ
is the differential coefficient, and
[0,1] ρ ∈
. In the
paper,
0.5 ρ =
.
The grey i ncidence of
i
X
and
0
X
is defined belo w:
00
1
1
(,)((),( ))
n
ii
k
XXxk xk
n
γξ
=
=
∑
(2)
Usually,
0
(,)
i
XX γ
is also written as
0i
γ
, and
0
(( ),( ))
i
xk xk ξ
is also writte n as
0i
ξ
.
2). Calculatio n Steps
Step 1: Unitary operation for all data. That is, every data
in a certain sequence is divided by the maximum data of
this seque nce.
'max ' ''
/((1), (2),..., ());0,1,2,...,
i iiiii
XXXxxxn im= ==
(3)
max
i
X
is the maxi mum da ta of
i
X
.
Step 2: get the difference sequences. The
m
contrasting
sequences mi nus t he ref erenti al seque nce to get the
difference sequences.
''
0
() |()()|
((1), (2),..., ());1,2,...,
ii
iii i
kxk xk
ni m
∆= −
∆=∆ ∆∆=
(4)
Step 3: get the maximum and minimum difference.
max max()
i
ik
Max k= ∆
(5)
min min()
i
ik
Min k= ∆
(6)
Step 4: get the incidence coefficient. According to (1), (5)
and (6)
0
0.5
( );1,2,...,;1,2,...,
( )0.5
i
i
Min Max
kkni m
k Max
ξ
+×
== =
∆ +×
(7)
Step 5: get the grey incidence. According to (2)
and (6)
00
1
1( );1,2,...,
n
ii
k
ki m
n
γξ
=
= =
∑
(8)
3). Data Initializatio n
Before calculating the grey incidence, there must be
initialization to getting rid of the da ta .
a) Some sequences may be much bigger than the
others, it’s necessary to have unitary operation before
calculation. In the paper, all the data in a certain
sequence is divided by the maximum value of this
sequence.
b) Due to the use of absolute value of difference
between referential sequence and contrasting sequence in
defini tion of gre y incidence, it must guarantee that if the
contrasting sequence becomes bigger, the referential
sequence must also become bigger, and this is called
positive ope r a tion. The d e ta ils are shown as belo w.
Higher coal price turns to higher clearing price, and
higher b enc h ma rk p r ic e t ur ns to hi gher clearing price, so,
they have already met the positive relationship. Bigger
electricity surplus and deficiency fo r generators in suppl y
provinces means a more enough supply which results to
a lower clearing price, it must have positive operation
before calculating, so the recipro cal of electricity surplus
and deficiency is used. The electricity surplus and
deficiency in demand provinces is negative, if the
absolute value of this negative value is bigger, then it
means the demand amount is bigger, and then the
Tab.1 data of clearing price, bidding price, coal price, electricity surplus and deficiency and HHI
year month clearing price(￥/MWh) bidding price (￥/MWh)
coal
pric e
(￥/ton)
electricity surplus and
deficiency(104KWh) HHI
Zhejiang Jiangsu Anhui Fujian
2010
Jan. 387 - 795 -283.8 -91.0 72.0 84.0 0.212
Mar. 325 299 679 -21.6 402.0 59.0 90.0 0.156
Apr.(1) 321 311 684 -10.0 511.0 155.0 251.0 0.285
Apr.(2) 311 277 684 -10.0 511.0 155.0 251.0 0.327
May 337 233 745 97.2 319.0 150.0 269.0 0.102
Jul. 383 370 748 -282.9 -179.0 87.0 390.0 0.170
Aug. 382 370 725 -196.3 -194.0 82.0 311.0 0.153
Dec. 382 375 789 -191.1 381.0 147.0 257.0 0.174
2011
Jan. 394 385 774 -258.0 -462.0 77.0 195.0 0.095
Jan.-Feb. 396 - 775 -305.0 -566.0 97.0 334.0 0.250
Feb. 390 373 765 -305.0 -566.0 97.0 334.0 0.103
Mar. 396 385 761 -151.0 -613.0 9.0 99.0 0.098
Apr. 412 - 780 -332.0 -163.0 88.0 97.0 0.167
May 421 - 817 -181.0 -57.0 20.0 53.0 0.167
Jun. 417 396 838 -9.0 -222.0 20.0 248.0 0.094

B. ZOU ET AL.
Copyright © 2013 SciRec. ENG
11 9
clearing price becomes higher. It has already met the
positive relationship. Bigger HHI means higher
centralized degree in the industry; the members have
more opportunities to force up the clearing price. It has
met the positive relationship.
3.3. Overall Design of Grey Incidence Analysis
In order to completely analyze the relationships between
the price and the correlation factors, four groups of grey
incidence calculatio n are designed as below:
1) The first group is the calculation based on the data
of the 15 exchanges from 2010 to 2011. Figure 2 has
shown changes of the clearing price. From these
exchange data, we can analyze the grey incidence sort of
correlation factors.
2) The second and third group are the calculations
based on the data of 2010 and 2011. We can compare the
grey incidence sort in the first three gr oups to make sur e
whether they are the same and whether they have
robustness or not.
3) The fourth group is based on the data of a typical
gener ato r i n F uj i an p r ovi nc e . T he b id d ing price is cho sen
as referential sequence, and the benchmark price and
electricity surplus and deficiency are that of Fujian
province, while the coal price and HHI are the same as
the data above. Based on these data, we can analyze the
grey incidence sort between the bidding price and the
four correlation factors. If the grey incidence sort in this
group is similar to the three groups before, it means that
this kind o f sort has a considerable credibility.
4. Case Study
The original data is shown in table 1 and 2, using the
method provided in part 3, we can get the grey incidence
resul ts shown in table 3 and 4.
Tab. 2 cha nges of benc hmar k price of all provi nces i n
East China reg ion
Variable ti me S hanghai Jiangsu Zhejiang Anhui Fujian
2009 456.8 430 457 398 414.3
2011.4.20 457.3 430 457 400 417.4
2011.6.1 457.3 430 457 418 417.4
According to the grey incidence results, we can know
that:
1) The coal price has the highest grey incidence with
the clearing price under any cases (table 3). Not only in
the single calculations in 2010 and 2011, but also in the
combine calculation in 2010 and 2011, it always show
that the coal price has the highest grey incidence with the
clearing price. And even more, in the fourth group, the
coal price also has the highest grey incidence with the
bidding pric e (table 4 ), this means that t he generator s bid
price based on the cost.
2) From table 3, it can be seen that the second grey
incidence factor is the benchmark price in all provinces.
And there are three levels for the benchmark price, the
first highest level is benchmark price in Anhui province,
the second one is that of Fujian province, and the third
one is that of other p rovinces. This kind of sort indicate s
that the trans-provincial centralized bidding trading
platform was actually guided by the market supervision.
During the observing period, the supply provinces are the
Anhui Power to East and Fujian province while the
demand provinces are Zhejiang province and Jiangsu
province. F orm the truth that the gre y incidence o f Fuj ian
province and Anhui province are higher than others, it
can conclude that the supervision of supply side is more
powerful than that of demand s ide.
3) The third highest one is the electricity surplus and
deficiency. As demand provinces, the electricity surplus
and deficiency of Jiangsu and Zhejiang province are
higher than that of others, which means that the trading
amount is mainly depending on demand side, if there is a
bigger demand, there will be a higher price.
4) The HHI indicator which represents the market
power of generators ranks the last. This means that the
strategy space of generators is very small; at least, it
ranks behind the cost, benchmark price and supply and
dema nd situatio n.
5) The grey incidence sort between the bidding price
of generators and correlation factors is the same as that
of clearing price, that is, the highest one is coal p rice, the
second one is benchmark price, the third one is
electricity surplus and deficiency, the last one is HHI.
The volatility of bidding price is more active than
clearing price, but the sort is the same, which means this
kind of sor t has a strong robustness.
5. Conclusion
Based on the grey incidence analysis, this paper studies
the correlation coefficient between the clearing price and
bidding price with the generation cost, the supervision
and r ule s o f t he mar ket , the su p p ly a nd demand s it ua tion,
the behavior of market members over the same period
based on the actual data of the trans-provincial
centralized trading market of East China Power Grid in
2010 and first half of 2011.
The paper designed four groups analysis to confirm
the grey incidence sort of all the correlation factors. All
the four groups show that the correlation factor with the
highest grey incidence is coal price, and the second one
is benchmark price, the third one is supply and demand
situation, the la st o ne is the HHI.
The conclusion is that this market is a reasonable
regional market which can find the cost, and promote
healthy competition.

B. ZOU ET AL.
120
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Tab.3 grey incidence results of c learing price in 2010 and first hal f of 2011
correlat ion factors all data 2010 2011
grey incidence sort grey incidence sort grey incidence sort
high-quality-mixe d coal pr ice
in Shanxi
0.93 1 0.92 1 0.96 1
benc hm a rk price in A n hui 0.89 2 0.85
3-7
0.96 2
benc hm a rk price in F uji a n 0.85 3 0.85 0.93 3
benc hm a rk price in Sh angha i 0.84
4-6
0.85 0.92
4-6
benc hm a rk price in Ji ang su 0.84 0.85 0.92
benc hm a rk price in Zhejiang 0.84 0.85 0.92
electricity surplus and
deficiency in Jiangsu
0.66 7 0.58 9-10 0.68 8
electricity surplus and
deficiency in Zhejiang 0.65 8 0.58 0.71 7
electricity surplus and
deficiency in Fujian
0.61 9 0.84 8 0.52 10
electricity surplus and
def iciency in Anh ui 0.59 10 0.88 2 0.47 11
HHI 0.56 11 0.47 11 0.56 9
Tab.4 grey incide nce orders of bidding price of ge nerators
correlation factors all d ata 2010 2011
grey incidence sort grey incidence grey incidence sort grey incidence
high-quality-mixed coal price in Shanxi 0.89 1 0.78 0.89 1 0.78
benc hm a rk price in F uji a n 0.83 2 0.71 0.83 2 0.71
electricity surplus and deficiency in Fujian 0.63 3 0.66 0.63 3 0.66
HHI 0.58 4 0.47 0.58 4 0.47