Hedging Optimization Algorithms for Deregulated Electricity Markets

Recent trends in many U.S. states are to deregulate their electric power industry and markets with the desire to provide a more consumer-friendly environment than under regulation. However, deregulation also creates uncertainty and risk. It is this risk that we wish to address and contain. In this thesis, we review recently developed stochastic models of physical and nancial aspects of deregulated electricity markets and research algorithms to utilize these models to hedge risk. First, we consider the issue of calibrating these models to historical data. Once the models are calibrated suÆciently, we discuss two major frameworks for hedging risk optimally. We begin by rst developing a method for static hedging optimization, where we optimize a hedging strategy from a xed point of time over a nite delivery period. Then we develop a more robust dynamic optimization, where the hedging strategy is continuously improved over a nite hedging period for a nite delivery period. A very lucid and recent motivation for the research in this thesis comes from California, where deregulation took place ve years ago. Within the last year, the spot market behaved erratically, causing utility companies to plummet nancially, ultimately resulting in many declaring bankruptcy and requiring the state of California to intervene so that California did not fall dark. The hedging optimization algorithms developed in this thesis could be used in deregulated electricity markets to possibly avoid a repetition of the situation that occurred in California. Thesis Supervisor: Marija Ili c Title: Senior Research Scientist