2026-09-19T01:26:16Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/167782022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Marija Ilić.
Wagner, Michael R. (Michael Robert), 1978-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-05-19T14:33:33Z
2005-05-19T14:33:33Z
2001
2001
http://hdl.handle.net/1721.1/16778
49323276
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 83-84).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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.
by Michael R. Wagner.
M.Eng.
84 p.
1020563 bytes
1020237 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Hedging optimization algorithms for deregulated electricity markets
Thesis
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