This Is AuburnElectronic Theses and Dissertations

Trading Strategies and Risk Management for Wind Power in the Electricity Market




Li, Shaomao

Type of Degree

PhD Dissertation


Industrial and Systems Engineering


The use of wind energy, one of the main renewable energy sources, has rapidly expanded around the world in past decades. However, the uncertainty and unpredictability of wind power lead to a number of challenges for both power systems and wind power producers (WPPs). In general, WPPs trade part of their production in the short-term electricity market and are exposed to signi cant uncertainties due to the volatility of market price and the limited predictability of real-time generation. This research develops an analytical trading electricity model for WPPs in the short-term electricity market in the U.S. The model is designed to nd the optimal bidding strategy to maximize the expected revenue under the uncertainties. In addition, this research shows how advanced forecasting techniques can be used jointly with the proposed bidding strategy to help WPPs trade energy in the short-term market. Furthermore, this research also evaluates risk management in the bidding strategy problem for WPPs in the short-term electricity market. The conditional value at risk (CVaR) concept is utilized to develop a Mean-CVaR model to address the risk and uncertainty inherent in wind power trading. Bidding strategies with and without considering risk are compared by the Monte Carlo simulation method using real-world data; the simulations show that the results are almost the same in the long run. Finally, this research presents a static hedging strategy for WPPs to manage production revenue risks via future contracts. A 2-factor term structure model in a Heath-Jarrow-Morton framework is developed for the electricity future price. The Monte Carlo simulation method is used to develop scenarios for the evolution of future prices and wind power generation to determine the optimal hedging strategy. A series of sensitivity analyses are conducted to show how the optimal hedge ratio changes with respect to various factors.