The Research about the Trans-provincial Centralized Bidding Trading Market of East China Power Grid --II：Model Analysis

doi:10.4236/eng.2013.51B022

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Engineering, 2013, 5, 121-126

doi:10.4236/eng.2013. 51b022 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 --II：

Model Analysis

Bin Zou 1, Jie Fan1, Xiao-gang Li2, Li-bing Yan g2

1School o f Mechatronic En gineering and Automation, S hangha i Univer s ity, Shanghai, C hina

2East China Power Grid Co., Ltd. Shanghai, China

Email: [email protected]

Received 2013

Abstract

In this paper a novel cost function based on the relationship between operation cost of unit and generation load rate is

employed in an agent-based model of Trans-provincial Centralized Bidding Trading Market of East China Grid.

Simulation results are compared to real data to prove that the model is correct. Further analysis on simulation results

point out the wa y to achieve a n all-win game for power market member s : generation companies impro ve their average

load rates of the units by selling their electricity in the market, which makes units' cost drop and settlement price stay

lo wer tha n b e nc h mar k p r ice . Consequently electricity-dema nd pr o vinc es save d e xp ens es, and units increase their profits.

In co ncl usion, the tra ns-pro vincial electricity market of East China Po wer Grid is a successive case which improves the

efficiency of the electricity industry b y market-oriented measures.

Keywords: Power Market; Agent-based Simulation; East China Grid; Trans-provincial Centralized Trade; Partial

Electricity Compe tition

1. Introduction

East China power grid is an interconnected power grid,

which contains five provincial power grids, and each

province power grid is a control zone. Each provincial

power grid company is responsible for their respective

provinces electricity and power balance. In order to meet

the load demand, about 1% of the total energy needs to

be exchanged among the provinces. The Trans-provincial

Centralized Bidding Trading Market of East China

Power Grid（TPM-ECPG）is designed to take advantages

of competition to form energy exchange programs

between provinces.

About 9 9% of t he gener ating energy is arranged using

so-called planned schedule method. In the method, each

unit was allocated a certain amou nt of generating energy

during the next year according to the installed capacity of

the un it, t he uni t type and t he for ecast ing lo ad. T hen, t he

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 Refo rm Co mmi s sio n.

The TPM-ECPG that was put into operation in Dec.

2008, is the only one power auction. The result of last

three-year operatio n indicates that market trading volume

increased year after year, and to most degree electricity

demand has been satisfied. The trans-provincial

transaction platform has become the one of the main

ways of the tra ns-provincial transaction in East China

Power Grid.

In this p a pe r agent-based model is proposed as the tool

for the modeling and analysis of the trans-provincial

centralized auction transactio n p latfor m of East C hina. In

a lot of literatures, the agent-based model not only was

employed to estimate market characteristic with different

pricing method, namely uniform clearing pricing,

mid-point pricing and pay-as-bid pricing[5-7], but also

used to discuss the influence on the power market

exerted by blocking, collusionbid and emission trading

of CO2[11-18]. The method is able to show the basic

features that market members bid as the behavior of

maximizing their benefits, and its extensibility allows

taking more practical market factors into consideration.

The important feature of the TPM-ECPG is a part of

the electricity competitio n, part of electricity is

scheduled using traditional cost-based method, and

B.ZHOU ET AL.

122

another is sa le by competition. What is the impact of the

non-competitive part on the competitive market? In fact,

the market shows some interesting phenomenon. For

example, power generation companies are willing to

participate in market competition, even if the market

price is lower than the benchmark price.

This paper is one of the two-part series. This article

will use an agent-based simulation method to model the

TPM-ECPG. By doing so, it can help to analyze

simulated cases based on realistic data of East China

trans-provincial centralized power transaction.

2. Introduction of the TPM-ECPG

The TPM-ECPG functions as a double-auction market,

basic rules are as follows: buyers and sellers initially

report their respectively offered quantities of electricity

(MWh) a nd bid prices (￥/ MWh).

The approach of market clearing applies matchmaking

tradeoff model. The highest bid of demand side would

match the lowest ask price of supply side. Then the

market clearing price is their average value of the two

offered prices. The matchmaking procedure goes on by

the price sequences of sellers and buyers.

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.

More detailed market rules Re ferences [1].

3. Agent-based simulation model

Unlike the model of the conventional electricity market,

the model of the East Chi na po wer mar ket must take into

the planned generation. The proposed model in this paper

employs the characteristics as followed: 1) Market

members in the TPM-ECPG always bid for profit

maximization; 2). Generated electricity and price of

planned generation is described through a cost function,

which can embody the influence exerted on the power

auction market. 3). Transaction rules are set up in

accordance with practical regulations of the TPM-ECPG.

To sum up, the model possesses the most market

characteristic s of TPM-ECPG, and is expected to explore

the inner mar ket mechanism b y simu lation.

3.1 Cost fun c tion mod e l

The generator's cost model presented is divided into two

parts of the operating cost and capacity cost. Capacity

cost is a constant during the modeling and simulation.

The main operation cost is fuel cost, in order to represent

the impact of the planned generation, the power

generation load rate is employed to calculate the

operation cost of the generating u nit in this paper.

For any one of the power supplier

, its planned

generating quantit y of electric it y is divided into t wo parts:

electricity on peak periods and electricity on valley

periods, the accepted quantity of electricity is classified

into peak and valley part as well.

i ip iv

ipipp ips

ivivp ivs

QQ Q

QQQ



= +



= +



(1)

Where

denotes total generating electricity of

Unit

;

denote generating electricity of Unit

on peak period and valley period respectively;

ipp

ivp

denote planned generation of Unit

on peak period

and valley period respectively;

ips

,ivs

Qdenote accepted

bid of Unit

on peak period and valley period

respectively.

In this paper one month is set up as observation period.

The average load rates of Unit

on peak/valley period

are as followed:

,ma x

30 14

30 10

i lp

i lv

=

××





=

××



(2)

Where

,i lp

，

,i lv

denote average load rate of Unit

on peak/valley period.

,maxi

is unit’s maximum output.

30×10, 30×14 represent valley-period of 10 hours per

da y, peak-period of 14 hours per day. Each month has 30

days on an average.

The avera ge output of U nit

on peak/valley perio d is

as followed:

,ma x

30 14

30 10

i lp

i lv

=

××





=

××



(3)

Where

ip iv

denote average output of Unit

peak/valley pe r iod

Coal consumption of Unit

can be calculated by

coal consumption curve in Figure.1, which is expressed

as quadratic function:

iiiii i

raPb Pc×+×+

(4)

where

iii

abc

are coefficients for different types of

generator units,

is unit’s output. Reference [19] shows

that the way to estimate coal consumption can be

accepted.

The running cost of power generation companies

expressed as the product of the coal consumption,

B.ZHOU ET AL.

123

electricity gener ation and coal p r ices

ipi pcip

ivi vciv

CrQ p

××





××





(5)

Where

denote operation cost of unit

period/valley period.

denotes coal price，

,i pc

，

,i vc

can be

obtain from (4).

3.2 Decision - making model

The decision-making model of the power supply

company is just maximizing profit based on its objective

function, and decision variable is the ask price

bid

i sell

( )

, ,,,,, ,,

max+-+ +

bid

i sell

isellibmi planibmi plani peakivalleyicap

RpQpQC CC

=××

(6)

where the planned quantity of generating electricity is

scheduled by provincial regulators and its price is

benchmark price of the respective provinces. Owning to

the proposed model, which is based on load rates, the

impacts of generating plans can be involved in decision

making smoothly.

In terms of electricity-demand provinces, they

purchase insufficient power through the trans-provincial

auction after implementing generating plans, so

insufficient power can also be regarded as a known

constant in the model. The power-demand provinces

expect to bid to satisfy their demand. Similarly, the

decision-making model o f such provinces is maximizi ng

profit of their obj ective function, and dec ision variable is

the bid pric e

bid

i buy

, ,,

max -

bid

i buy

i buyisi buyi buy

Rp QpQ

=××

(7)

Where,

,i buy

,ir

,i buy

and

,i buy

denote profit, retail

price, closing bid price, accepted quantity of bid of

electricit y-demand province

respectively.

3.3 Agent-based simulation

Both the power supply company's decision-making

model (6) and power-demand province's

decision-making model (7) cannot be directly solved by

optimization methods, for they are all game procedures.

The agent-based simulation is appropriate to deal with

the game problems.

According to historical data, planned peak/valley

period generating electricity, quoted quantity, quoted

price, probable quotation set and other arguments can be

set up before simulation, reference [7] introduced this

method in detail. Simple agent-based simulation

procedure is as followed:

Step 1: In according to the roulette-like method, which

acquires selective prob a b ility, quotations can be pick up

for both units and buyers.

Step 2: After executing market-clearing, profits of units

and buyers can be calculated by ( 6) and (7).

Step 3: Units and buyers amend their selective

probability of quotation sets based on profit changes. The

target is to increase the probabilities of one quotation

whic h make s hi ghe r pr o fit.

Step 4: Iteration pluses one. If any quotation in the two

sets does not converge, go to Step 1.

The selective probability learning process, i.e., step 3,

using the Roth-Ere algorithm [7][8][9], detailed discussion

of the algorithm exceeds the scope of this article, please

refer to the literature [7][8][9].

4. Case Study

4.1 Data setting

In order to keep accordance with actual operating data of

the trans-provincial centralized bidding transaction

Table.1 Data of generators’ units

NO.

Installed

Capacity

Province

Offered

Quantity

￥/MWh

Planned

Generation

MWh

1 300 A

6480

179280

2 600 A

12960

358560

3 1000 A

21600

597600

4 600 A 12960 358560

5 300 A

6480

179280

6 1000 A

21600

597600

7 1000 B 21600 597600

8 300 B

6480

179280

9 600 B

12960

358560

10 600 B 12960 358560

11 1000 B

21600

597600

12 300 B

6480

179280

600

12960 358560

Figure.1 coal consumpt ion curves of 3 ty pes of units

B.ZHOU ET AL.

124

platform of East China, the experimental environment

for power supply companies and their units in this paper

are set up and present in Tab.1. These power companies

with the sole unit are a ffiliate d to two di fferent pro vince s

of A and B, the electricity-demand provinces are C and D.

It is assumed that the coal price of province A is 720

Yuan/ton, and the coal price of B is 750 Yuan/ton.

4.2 Analysis on market equilibrium

Based on the data setting, market equilibrium res ults can

be obtained by agent-based simulation. Table.2 shows

the market clearing results of electricity-demand

provinces and units. The average bid prices of C and D in

Tab.3 is close to the actual bid of demand provinces in

2010 transactions(422.26￥/MWh vs 421.00￥/MWh),

relative error is about 0.3%. In addition the average

units’ ask prices(335.82￥/MW h) also approximate to

those in 2010(335.82￥/MWh), which leads to a relative

error of 4.6%.

Table.2 Simulat ion results on the o ffer and bid

Market

players

Quotation

￥/MWh

Winning

Bid

￥/MWh

Accepted

Quantity

MWh

Province C

416

400

79000

Province D

426

389

79000

300MW Unit

368

367

3400

600MW Unit

330

359

12960

1000MW Unit

326

358

21600

Furthe r ana lysi s on the res ults of Table.2 show that the

average winning bid prices of units and

electricit y-demand provinces compare to their actual

transaction data respectively, relative errors are 1.6% and

1.8%(absolute errors are 6.23 ￥/MWh and 6.51

￥/MWh). All the results and analysis figure out that

market equilibriu m of the model in this paper is close to

real market operation, and the model can embody most

of trans-provincial elec tr ic auction market.

According to Table.2, average ask-prices of 300 MW

units, 600MW units and 1000MW units are sorted in

descending order, which is as same as their operation

cost sorted order. It is indicated that generation

companies who owe auction units always bid on the

basis of units’ cost. The market characteristic makes

low-cost units take a dvantages in market auction.

The simulated winning bid prices of province C and

D(416.00￥/MWh and 426.00￥/MWh) are lower than

their benchmark prices(430.00 ￥/MWh and 457.00

￥/MWh) respectively, that saves expenses for both

demand-p rovince C and D.

Simultaneou sly the average winning bid price of units

in province A and B (359.27 ￥/MWh and 359.42

￥/MWh) are also lower than their benchmark prices

(410.00￥/MWh and 400.00￥/MWh) respectively.

To find out why generation companies are willing to

bid at a low price, average profit per unit (total profit

divide total ge neratio n) a nd average load rate are put into

consideration as shown in Table.3.

As it presents in Table.3, load rate increases after

generation companies participate in auctio n market. T hat

makes running cost go down. Moreover, when total

generation increases by 3%, the settlement price of

incremental part decreases nearly by 10% comparing to

benchmark price. Whereas the cost drops, total profit

grow by 9% and unit profit also goes up.

Table.3 the profi ts a nd load rate of the power s uppliers

part icipating or not in i nter-provinvial t r ans ac tio n

Total

generation

MWh

Total

profit

￥

Profit

per

unit

￥/M

Load rate

P V

Without

auction

transaction

4900320 2209618

65 45.09 75% 89%

Wit h bid

transactions 5058320 2414121

93 47.72 77% 91%

In order to further study the characteristics of the

TPM-ECPG, in this paper 11 trading scenarios on

different market scales, of which total installed is fixed,

are set up. Owing to a small wiggle room for buyers’

quoted prices, units’ average winning bid price can

mostly indicate market trading status(As shown in

Figure.2, where the horizontal axis represents the

percentage of maximum offer of electricity in market

biding to total installed capacity, namely market scale).

As can be seen from the simulation results of

Figure.2, When the TPM-ECPG maintain in a small

transaction volume, namely the competitive part of

electricit y accounted for less than 25%, units average

clearing price overall keeps a downward trend. It is

pointed that the market within a c e r ta in small siz e has the

ability to reduce the generation costs continually, to

promote genera tion compa nies to participate in bid ding，

and possess es the conditions to expand transaction scale.

When the transaction scale goes up, average clearing

electricity price rebounded, and presents a substantia l

rebound trend constantly. As the market expands to the

B.ZHOU ET AL.

125

scale of trans-provincial trading electricity accounting

for more than 75%, then, the total capacity which can be

put into auction is large enough to approach to

Figure.2 Units’ average bid pri ce under different market

scale

deregulated electricity competition scenario, and the

average clearing price is slightly lower to medium-sized

market, but still higher than smaller one.

In order to study the root cause of this situation, the

study focused on the units’ average unit profit (total

revenue / (planned generation + accepted bided

generation)) under different market size when the

simulated market get an equilibrium, as showed in Figure.

Figure.3 Units’ marg in pr of i t under different market scale

As can be seen from Figure.3, the units' average unit

profit decrease constantly when the market scale

gradually expands. On the one hand it is indicated that

the planned power generation is still as dominant

position in both supply and demand side, and generation

companies which participate in electricity auction cannot

get enough profit caused by coal consumption reduction

to compensate the loss of planned generation. On the

other hand, before the trans-provincial transactions

accounts for 25% of total capacity, the unit profit is

greater than zero, so the power companies' profitable

opportunitie s still e xist u ntil p ro fit is less tha n zero as the

scale of market transactions expand. That is to say

generation companies have to bid at a high price level in

a larger auction market with current rules. It is also

explained why there is a sudden rebound of price in

Figure.2.

Combining all above simulation analysis, the planned

generation as the main method of electricity supply and

demand in the current market environment cannot be

shake n, b u t T PM-ECPG mak es a better market efficiency,

and to some extent be on the potential for further

expansion.

5 Conclusion

In this paper, the trans-provincial centralized bidd ing

trading market of East China is modeled by an

agent-based method, a novel cost function and cases

based on actual data are employed to simulate the

operation of the market. The simulation results are used

to analyze market characteristics on the equilibrium

status. Here I summarize what we can learn from the

modeling and simulations:

1) The proposed model in this paper reflects basic

characteristics of the trans-provincial centralized

transaction platform, in which its market

equilibrium accord with practical statistics of the

power auction market.

2) The generation companies that participate in the

trans-provincial auction platform possess restricted

strategy space, they bid base on cost, so that is

possible for e fficient units to a cquire more available

electricity to generate. It helps to achieve the goals

of energy saving and emission abatement

3) Trans-pr ovincial tra nsactions make the

electricit y-demand side save purchasing cost, the

supply side cut down operating cost, and it is a well

designed power market which satisfy the society,

electricity generation enterprise and grid co mpa nies.

The essence of the all-win game is that the

generation companies bid base on their cost, and the

transactions make the generating cost cutoff by

increasing the units' load rates. For all the reasons

above, both buyers and sellers can gain profit when

the market closing prices are lower than benchmark

prices.

4) Simulated results indicate that the trans-provincial

centralized bidding transaction platform can

preserve basic market characteristics after

expanding its tr a nsaction sca le within constraints.

5) Trans-provincial centralized auction platform ca n

further expand on the existing basis, but planned

generation is still the main profitable part for

generation companies, to expand the scale is not

easy to be too large.

B.ZHOU ET AL.

126

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