Optimal Bidding Strategy of a Strategic Wind Power Producer in
the Short-term Electricity Market
Ting Dai and Wei Qiao
Department of Electrical Engineering, University of Nebraska-Lincoln
• Upper-level Problem (1)
 Maximize the total profit of the wind power producer
 Subject to bid price constraints
Case Studies
• The proposed model is tested using the IEEE
Reliability Test System.
• Case 1: the impact of wind penetration level
15
 The day-ahead market clearing problem minimize the total cost
of the energy offered.
 The constraints include power balance, energy offer limits of
each producer, the transmission constraints and the bus angle
limit.
6000
10
4000
2000
Profit in day-ahead market
Profit in real-time market
Total profit
0
5
0.05
0.1
0.15
0.2
0.25
Penetration Level
0.35 -2000
0.05
0.3
0.1
0.15
0.2
0.25
Penetration Level
0.3
0.35
 The extra wind power is sold into the real-time market, which results in a
decline in the real-time price.
• Lower-level Problem(3) – Real-time market clearing
 The real-time market clearing problem minimize the total cost of
the redispatching the energy.
 The constraints include the power balance in the real-time
market, energy change limits of each producer, the
transmission constraints and the bus angle limit.
8000
Day-ahead LMP
Real-time LMP
Average Selling Price
Profit($)
• Lower-level Problem(2) – Day-ahead market clearing
Price($/MWh)
• The previous researches considered wind
power producers as a price-taker in the
electricity market. However, wind power
producers have nonegligible market power
and should be considered as price-makers.
• This paper proposes a bilevel stochastic
MPEC model for generating the optimal
bidding strategies for price-maker wind
power producers.
• .The upper-level problem of the model
represents the profit maximization of the
wind power producer while the lower-level
problem represents the market clearing
processes of both day-ahead and real-time
markets. The uncertainties of wind power and
demand are managed through the stochastic
programming.
Bilevel Stochastic Model
• Case 2: the impact of transmission constraints
30
Real-time LMP($/MWh)
Introduction
Model Description
Profit in
Case day-ahead
market ($)
Case(a)
Case(b)
Case(c)
25
20
Day-ahead
LMP at
bus 8
($/MWh)
Profit in
real-time
market ($)
Total
profit ($)
a
4136.08
10.66
1834.7191
5970.79
b
3209.34
10.68
2802.2018
6011.54
c
1818.76
11.09
3653.6266
5472.38
d
1885.15
10.68
2230.9775
4116.12
15
10
5
0
0
5
10
15
Scenario
 Transmission congestion sometimes can be beneficial to wind power
producers and can be used as a strategic mechanism to increase their
profits.
• Electricity Market Framework
6000
=0
=0.05
Profit($)
5800
=0.1
=0.6
5600
=1.0
=1.5
5400
=2.0
5200
0
• Uncertainty Modeling in the Market Clearing
500
1000
1500 2000
CVaR($)
2500
3000
Power sold in day-ahead market (MW)
• Case 3: the impact of risk management
400
350
300
250
200
0
0.5
1

1.5
2
 The wind bidding capacity in the day-ahead market decreases when β
increases.
Conclusion
 The uncertainties comes from other producers’
bidding strategies, wind production, demand and can
be represented via scenarios.
 The decision process of the wind power producer
can be arranged in a two-stage scenario tree.
• To facilitate the solution process, the bilevel programming
problem is transferred into an equivalent single-level
MPEC problem through the KKT conditions of problem (2)
and (3).
• By linearizing the nonlinear terms, the MPEC problem is
converted into a MILP problem, which can be solved
effectively.
• This paper has proposed a model considering the
strategic bidding behavior of a wind power producer in
the electricity market.
• Results show that as the wind penetration level goes
higher, the day-ahead and real-time LMPs decrease.
The network congestion has inevitable impact on the
bidding strategy and can be used as a strategic
mechanism to further increase the profit. The optimal
bidding strategy is sensitive to the risk parameters,
which should be chosen carefully.
• Future work will be done on multiple wind producers.