Optimal Bidding Strategy for Wind Power Generation in Day-ahead Market

Wind Power Generation is one of the major sources for clean energy around the world in the industrialized countries or regions.  It is well known that the WPG has the following characteristics: the input is uncertain; the volatility is high with jumps and lags; the correlation between its output and demand is most likely zero; WPG usually locates at a remote node in the transmission network. The proposed research will help to answer how profitable a WPG could be in a day-ahead market when its bidding strategy is optimal. The answer will also be a key reference in making supportive regulation for this new industry.

The problem will be considered both with strategy and without strategy. When the problem is considered with strategy, we explicitly model other competitor’s behavior. When the problem is considered without strategy, we model the clearing price as a random variable following its historic pattern. For both approaches, we will explicitly model demand constraints and other physical constraints for the segmented 24-hour market. Scenario generation will be an important part of the project, where a set of scenarios representing the future wind pattern will be created and used in the optimization model. 

The student shall actively involve in modeling of the problem, discuss the critical component of the model, tradeoffs between different factors in the model. The student will also help on collecting data for the model. Though it might be challenging, the student is expected to make contribution in the optimal algorithm design phase as well. The student will receive mini-lectures on stochastic programming and real option theory, which is expected to be the major tools in solving the optimization model.

Savil Gupta
Faculty Supervisor: 
Dr. Michael Chen