{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/16778"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/16778","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Hedging optimization algorithms for deregulated electricity markets","abstract":"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 financial 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 sufficiently, we discuss two major frameworks for hedging risk optimally. We begin by first developing a method for static hedging optimization, where we optimize a hedging strategy from a fixed point of time over a finite delivery period. Then we develop a more robust dynamic optimization, where the hedging strategy is continuously improved over a finite hedging period for a finite delivery period. A very lucid and recent motivation for the research in this thesis comes from California, where deregulation took place five years ago. Within the last year, the spot market behaved erratically, causing utility companies to plummet financially, 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.","abstract_html":"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 financial 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 sufficiently, we discuss two major frameworks for hedging risk optimally. 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