Winner of the Best Student Paper Competition at the 33rd USAEE/IAAEE North American Conference

Wind Power Plant’s Cost Structure and Associated Market Power Problem

Yang Yu
Ph.D. Candidate
Stanford University
Stanford, CA


While wind-power penetration has reached a significant level in some countries and continues to growth [1], it is important to clarify whether and when wind power plants (WPPs) have the ability to exercise their market power. If they have, what are their possible strategies? In the paper presented in the 2015 USAEE/IAEE North American Conference, I establish a multi-hour-two-stage stochastic model to analyze these questions. The multi-hour model setting captures the impact of wind-energy ramping. The two-stage model setting is used to simulate the sequential dispatch process and analyze impacts of the wind-energy uncertainty [2].

Examining the above research questions is critical to both the market-power regulation and renewable-energy policy design. Actually, engineering scholars suggest that WPPs should be allowed to aggregately bid in a deregulated electricity system to reduce the forecast errors [3]. However, aggregately bidding leads to the market-power concern. Therefore, the future electricity systems need a tool to measure the aggregated WPPs’ ability to exercise their market power if these systems plan to improve the economic efficiency of using wind energy.

In fact, WPPs are usually considered as price takers because WPPs have zero marginal fuel costs and a low market-concentration level [4]. Therefore, it is thought that WPPs fully compete with either each other or other zero-marginal-cost generators, such as nuclear generators.  Furthermore, the bidding rule regulating WPPs are considered as a mechanism to mitigate WPPs’ market power.  According to the bidding rule, WPPs must make hourly generation commitment with zero cost rather than provide a supply curve as conventional generation companies (GenCos).

However, my analyses based on the multi-hour-two-stage model demonstrate that a WPP has ability to exercise its market power because her marginal operation cost is not zero dues to its uncertain nature. Actually, WPPs have non-zero marginal costs because of the bidding rule regulating WPPs’ behavior in a sequentially dispatched market. A sequentially dispatched market includes a day-ahead (DA) market and a real-time (RT) market. According to the bidding rule, every WPP needs to make hourly generation commitment in the DA market. If a WPP’s generation in the RT market is less than this WPP’s commitment in an hour, the WPP must purchase electricity market from the RT market to fill the gap between her commitment and generation. Therefore, conceptually, a WPP determines her hourly commitment level in the DA market by solving the following profit optimization problem Eq.1.

Here, N is the number of hours. PDA(h) is the price in the DA market for hour h. PRT (h) is the price in the RT market for hour h. WindDA(h) is the WPP’s DA commitment. WindRT(h) is the WPP’s generation. EW(h)[·] is the expected value given the distribution of wind energy in hour h.

Consequently, WPPs marginal costs are non zero. Actually, a WPP faces with an opportunity cost when it makes her hourly generation commitment in the DA market. Actually, the opportunity cost is the WPP’s expected value of the payment for purchasing electricity from the RT market. For example, if the WPP optimize its profit hour by hour, its marginal opportunity cost, which is also its marginal operation cost, is shown in Eq. 2.

The more the WPP commits in the DA market, the higher the expected value of the payment. Therefore, the WPP’s marginal cost is positive.

Actually, a WPP’s marginal cost is determined by the distribution of its generation. Therefore, a WPP’s marginal cost varies by hours. In addition, two WPPs can have different marginal costs in the same hour. While one has a marginal cost similar to a low-cost coal-fired generator, the other’s marginal cost can be similar to a fringe gas-fired generator.  On the basis of the data from the market of Electricity Reliability Council or Texas in 2012, I simulate the WPPs’ marginal cost when they aggregately bid [8]. Figure 1 presents the WPPs’ marginal cost in two days.  The difference of the aggregated WPPs’ marginal costs varies from less than 13$/MWh up to more than 35$/MWh in these two days. Even in neighboring hours, the difference of marginal cost can be large.


Figure 1 Aggregated WPPs’ marginal cost as a price taker in the ERCOT market in 2012


Because WPPs are non-zero and heterogeneous by plants, WPPs neither compete with zero-marginal-cost generators nor necessarily compete with each other. Actually, in some hours, WPPs can be fringe generators due to their non-zero marginal costs. Consequently, WPPs can have ability to exercise their market power by adopting the strategy of reducing generation commitment. 

By applying the research framework of [5,6] to my two-hour-two-stage model, this paper analyzes a WPP’s ability to exercise market power by using the strategy of reducing generation commitment. Particularly, in contrast with the one-hour model in the literature on market power [5,6,7], the two-hour model setting revealed the impacts of ramping issues, which include net-demand ramping range and conventional generators’ ramping limits.

The analyses argue that a WPP have a high ability to exercise market power in three cases listed as follow.

  1. Competitors have steep-slope supply curve;
  2. Net demand that is the total demand minus the WPP’s commitment largely changes in neighboring hours;
  3. Competitors have limited ramping capabilities, such as coal-fired steam generators.

Therefore, WPPs can have high abilities to exercise their market power not only in the peak-demand hours, but also in the hours during the morning and later evening, when the demand is quickly and largely changing. Even if the demand keeps stable in some hours, the WPPs can also have high abilities to exercise their market power if the wind energy itself significantly fluctuates.

In addition to the traditional strategy of reducing generation commitment, WPPs have another unique strategy to inflate the price. By analyzing a WPP’s optimal strategy when it optimizes its profits of multi hours, this research reveals that a WPP can inflate the price in an hour by increasing its generation commitment in a neighboring hour. It is important to emphasize that WPPs can adopt this strategy because they are required to separately determine hourly generation and can flexiblely adjust their generation levels among different hours. In contrast, conventional GenCos are hard to adopt this strategy due to their ramping rates and minimal generation constraints.

In summary, this research examines WPP’s market power by analyzing WPP’s cost structure. WPPs have ability to exercise their market power even though their marginal fuel costs are zero. Actually, a WPP’s marginal operation cost is her marginal opportunity cost associated with the wind-energy distribution revealed in the DA market. Therefore, WPP’s cost structure is unique. A WPP’s marginal cost is essentially different from zero and fluctuates by hours. Two WPPs can have significant different marginal costs in the same hour. Therefore, WPPs compete neither with zero-cost generators nor necessarily each other. As a result, in addition to the peak-demand hours, WPPs also have market power when the demand or wind energy is significantly and quickly changing. Because of WPP’s ability to flexibly adjusting their generation level, WPPs have a unique strategy to exercise their market power. Actually, then can inflate the price of an hour by increasing the neighboring hour’s generation level.



[1] Ito, Koichiro, and Mar Reguant. Sequential Markets, Market Power and Arbitrage. No. w20782. National Bureau of Economic Research, 2014.

[2] Yu, Yang, and Ram Rajagopal. "The Impacts of electricity dispatch protocols on the emission reductions due to wind power and carbon tax." Environmental science & technology 49.4 (2015): 2568-2576.

[3] E. Y. Bitar, R. Rajagopal, P. P. Khargonekar, K. Poolla, and P. Varaiya, “Bringing wind energy to market,” Power Systems, IEEE Transactions on, 2012.

[4] Borenstein, S., Bushnell, J. B., Wolak, F. A., 2002. Measuring market inefficiencies in california’s restructured wholesale electricity market. AER 92 (5).

[5] McRae, Shaun D., and Frank A. Wolak. "How do firms exercise unilateral market power? Evidence from a bid-based wholesale electricity market." (2009).

[6] F. A. Wolak, “Measuring unilateral market power in wholesale electricity markets: the california market, 1998-2000,” The American economic review, vol. 93, no. 2, pp. 425–430, 2003.

[7] Hobbs, B., 2001. Linear complementarity models of nash-cournot competition in bilat- eral and poolco power markets. Power Systems, IEEE Transactions on 16 (2), 194–202.

[8] Electric Reliability Council of Texas market (ERCOT). Wind Power Production: Hourly averaged actual and forecasted values 2012 website. URL http://www.ercot.com/gridinfo/generation/


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