After the 90s, the electric power industry has undergone a considerable change, and the power markets have been restructured in many regions worldwide. The bidding of power plants is always interesting in research to ensure revenue and profit of many generation companies. This paper studies and analyzes profit of generation company in power market by bidding strategy. PowerWorld 13 simulator are chosen to build up the calculation program, the simulations are involved IEEE 39bus test system.
Trang 1Analysis Profit of Generation Company in Power
Market by Bidding Strategy
Hue Industrial College National Taiwan University of
Science and Technology Danang University of Technology National Taiwan University of Science and Technology
Abstract— After the 90s, the electric power industry has
undergone a considerable change, and the power markets have
been restructured in many regions worldwide The bidding of
power plants is always interesting in research to ensure revenue
and profit of many generation companies This paper studies and
analyzes profit of generation company in power market by
bidding strategy PowerWorld 13 simulator are chosen to build
up the calculation program, the simulations are involved IEEE
39-bus test system
Keywords-power market; generation company; bidding
strategy; profit
I INTRODUCTION
Nowadays, many countries changed the economics of their
electricity markets from monopolies to oligopolies in an effort
to increase competition One of the main market competition
structures used in the new deregulated environments is the pool
power market A pool power market is a central auction that
brings regional buyers and sellers together All competitive
power generators (supply) and buyers (demand) are required to
submit blocks of energy amounts and corresponding prices
they are willing to receive from or pay to the pool power
market
The prices and quantities submitted by the market
participants are binding obligations as they require financial
commitments to the market Once all the supply and demand
bids have been submitted and the bidding period ends, an
Independent System Operation (ISO) ranks these quantity-price
offers based on the least-cost for selling bids and the highest
price for buying bids The ISO then matches the selling bids
with buying offers such that the highest offers are matched
with the lowest selling bids
A significant amount of research has been conducted the
past several years concerning the market structure and the
development of efficient bidding strategies for power
producers Evolving trading agents, whose evolution is based
on a genetic algorithm (GA), are used to simulate the electricity
auction [1] In [2], the problem is formulated as a two-level
optimization procedure with a centralized economic dispatch
that determines market clearing prices at the top level, and a
self unit commitment simulator at the second level In [3],
optimal multi-period bidding strategies are developed with the
application of a discrete-state and discrete-time Markov
decision process In [4], a methodology for the development of bidding strategies are presented for electricity producers in a competitive In [5], two methods are presented to create optimal bidding
II BIDDING AND AUCTION IN THE POOL POWER MARKET
A Bidding in the pool power market
A generator offer for the power market is composed of two components, the price and quantity of electricity that a supplier is willing to generate Offers are submitted in blocks
of price quantity pairs Power market allows submitting many blocks for a generator offer Figure 1 show the bidding in the pool power market
Figure 1 Bidding in the pool power market
In the day-ahead or hour-ahead markets, the Generation Companies (GENCOs) and the Distribution Companies (DISCOs) must submit the quotations for Independent Market Operation (IMO) The quotation, which is expressed by the electricity capacity levels and electricity pricing, will show the supply and demand curves of GENCOs and DISCOs [6]
B Auction in the pool power market
Auction in the power market is operated by IMO, which is based on the quotations of the buyer and the seller The auction
is arranged according to the capacity ranges from low to high price for supply curves and vice versa for demand curves If quotations of the demand curve are not requested, it will be perpendicular to the MW-axis with a value of the total of demand capacity
POOL POWER
Quotation
Schedule
Trang 2Figure 2 Auction in the pool power market by “block”
Resource offers and demand bids are illustrated in Figures
2 and 3 Resource offers may be “block” or “slope” A “block”
offer for a GENCO's generator corresponds to a piece wise
linear cost curve (as shown Figure 2) A “slope” offer for a
generator corresponds to a quadratic cost-curve (as shown
Figure 3)
Figure 3 Auction in the pool power market by “slope”
III OPTIMAL POWER FLOW AND PROFIT OF GENCOS IN
THE POWER MARKET
A Optimal power flow in the power market
The optimal power flow (OPF) is developed for
implementation into a power system simulation environment
The OPF performs all system control while maintaining system
security System controls include generator megawatt outputs,
transformer taps, and transformer phase shifts, while
maintenance of system security ensures that no power system
component’s limits are violated the scheduled supplies from
the day-ahead bids establish the dispatch commitments The
quotations of the GENCOs from scheduled supplies can be
approximated to the quadratic function or higher degree
function of capacity [6]:
2
Gi i Gi i i
C (1)
The generally accepted objective is minimization of total
generator operational costs [9]:
min f ( PG) (2)
Subject to G ( PG , QG , V , ) 0 (3)
H ( PG , QG , V , ) 0 (4) The Lagrangian fuction defined as:
) , , , ( ) V, , , ( )
f
Where:
i fi P P
f ( G) 1 ( G): scalar, short-term operating cost, such as fuel cost;
: ) , , , ( )
, , , ( PG QG V n i1gi PG QG V
equality constraints, such as bus power flow balances;
: ) , , , ( )
, , ,
i hi P Q V V
Q P
inequality constraints including limits of all variable;
i1i
i 1i
B Profit of GENCOs in the power market
Locational marginal pricing (LMP) at a location (bus) of a transmission network is defined to be the minimal additional system cost required to supply an additional increment of electricity to this location LMP at Busi is three components included in the marginal price at reference bus, marginal loss cost from reference bus to Busi and marginal congestion price from reference bus to Busi [6]:
ij j i
loss ref ref i i
P
P P
P LMP
1
Revenue of GENCOs can be found as:
RGi LMPi * Pi (USD/h) (7) With real power at buses:
n
j
j i ij ij
j i
P
1
)
Therefore, profit of GENCOs can be determined as: Gi R Gi CGi (USD/h) (9)
9
G2 G2 G1
5
G2
3
G1 G1
1
G2
P
P min1 P min2 P max1 P max2 P 1 P 2 P 3 P 4
1
2
3
4
Trang 3The equation (9) represents the profit of GENCOs, which
is the difference between the revenue and the cost of power
generation
IV CASE STUDY
A Test model
This paper proposed calculation model with 39-bus IEEE
test system (New England) to assess revenues, expenses and
profits of GENCOs in the power market Bidding strategy is
analyzed on this system so that profit can improve effectively
The calculation has been run by PowerWorld Simulator 13
Figure 4 and Table 1 show parameters and diagram of IEEE
39-bus test system as follows [7]:
Figure 4 Diagram of IEEE 39-bus
TABLE I P ARAMETERS O F I EEE 39-B US P OWER M ARKET
GenCo (MW) Pmin (MW) Pmax ($/MW) b ($/MWc 2)
B Caculation and discussion
In this survey, this problem was described as a two
scenarios At the high level, a bidding strategy is surveyed to
determine profit at high cost, but in contrast, lower level is determined profit in low cost The quotations can be approximated to the quadratic function or higher degree function of capacity Simulator software of the power market such as PowerWorld simulator 13 also has this feature
In the first scenario, bidding curves of GENCOs have been showed in Figure 5 The bidding strategy is distributed in such
a way that GENCO30 owns the most expensive generator (b=6.9$/MW, c=0.019$/MW2) while other GENCOs owns those with cheaper operating costs This paper choose load factor script of 1.1 to calculate and analyze market
0.0 5.0 10.0 15.0 20.0 25.0 30.0
50 100 150 200 250 300 350 400 450 500
MW
Figure 5 Bidding strategy in the first scenario
From Table 2, with the first scenario, GenCo30 will has benefit from the law of the market like any other member
When output capacity of GenCo30 is 250MW, its revenue is 4283$/h and profit is 1370$/h
TABLE II T ARGETS O F G EN C OS I N T HE F IRST S CENARIO GenCo (MW) P ($/MWh) LMP Revenue ($/h) ($/h) Cost Profit ($/h)
GENCO30
Trang 4In contrast, with the second scenario, if the bidding
strategy of GENCO30 is lower level (b=4$/MW,
c=0.007$/MW2), bidding curve of GENCO30 will be lower than
the previous scenario (as shown Figure 6)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
50 100 150 200 250 300 350 400 450 500
MW
Figure 6 Bidding strategy in the second scenario
On the other hand, in the comparison between two scenario
(as shown Tables 2 and 3), because revenue of GENCO30 has
increased from 4283$/h to 5996$/h as well as cost has reduced
from 2913$/h to 2258$/h, profit of GENCO30 has increased
from 1370$/h to 3738$/h At the same time, total market profit
has been improved effectively
TABLE III T ARGETS O F G EN C OS I N T HE S ECOND S CENARIO
GenCo (MW) P ($/MWh) LMP Revenue ($/h) ($/h) Cost Profit ($/h)
Moreover, Tables 4 shows the other target of GENC30 for
some typical level on the daily load curve with high and low
bidding scenario Simulations are done on with varying load
conditions from 0.3pu to 1.1pu load Results are then tabulated
in the following few sections
TABLE IV O THER T ARGETS O F G ENCO 30 Load
value
P (MW)
LMP ($/MWh)
Revenue ($/h)
Cost ($/h)
Profit ($/h)
High bidding scenario
Low bidding scenario
V CONCLUSION
To sum up, a brief survey of bidding strategies of GENCOs
in the power market is made in this paper Participating in the power market and knowing how to forecast quotation for day-ahead market through load forecasting, LMP forecasting, etc If the GENCOs sign bilateral contracts with power trading companies, they will encounter investment risks such as exchange rates, inflation, etc Therefore, the GENCOs should
be carefully forecasted in bidding strategy on the day-ahead market This will optimize the profits as well as reduce risk of the business
REFERENCES
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[3] B R Song, C.-C Liu, J Lawarree, and R W Dahlgren,“Optimal elec-tricity supply bidding by Markov decision process,” IEEE Trans Power Syst., vol 15, May 2000, pp 618–624
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GenCo30