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Ji, Yuting Tong, Lang Bitar, Eilyan Yamen Mount, Timothy Douglas Thomas, Robert John 2017-04-04T20:27:06Z 2018-02-01T07:00:28Z 2017-01-30 ProQuest Submission ID: 10109 ProQuest Publication ID: 10252729 https://hdl.handle.net/1813/47738 https://doi.org/10.7298/X4J38QHQ 9905984 Uncertainty is a major factor in power system operations. In recent years, with the emergence of the smart grid, uncertainty level has been further elevated in both the generation and demand side of power systems. Increasing uncertainty exposes the electric grid to potential safety issues and economic loss, thus posing significant challenges to the grid operations. Traditionally, power system operations use certainty equivalent approach to deal with uncertainty, i.e., replacing random variables by their expected values. With this simplification, the original stochastic optimization is reduced to a deterministic problem. However, the certainty equivalent method is inadequate for the modern electric grid with deep penetration of distributed energy resources. Due to increasing uncertainty, operations and decision makings need to incorporate system dynamics over a broad range of temporal and spatial horizons. To this end, this thesis provides a new paradigm for operation under uncertainty and computationally efficient algorithms based on multiparametric programming theory. Under this new paradigm, uncertainty is characterized by conditional distributions and decisions are made by incorporating such probabilistic descriptions. To illustrate the new paradigm, we consider two specific problems. For characterization of system uncertainty, we develop a formal methodology for probabilistic forecasting of real-time operations and locational marginal prices. Conditioning on the current system state, we provide a full distribution of future operations and prices. For operational decision making, we propose an optimal stochastic approach to interchange scheduling in multi-area systems. By incorporating the conditional distribution of load and generation, the optimal interchange is obtained through an iterative process. en_US Electrical engineering multiparametric programming power system probabilistic forecasting smart grid stochastic optimization Operation under Uncertainty in Electric Grid: A Multiparametric Programming Approach dissertation or thesis application/pdf Electrical and Computer Engineering Cornell University Doctor of Philosophy Ph. D., Electrical and Computer Engineering https://hdl.handle.net/1813/59810 Publication Cornell Theses and Dissertations Ji, Yuting 5893a6ea-7af3-41d7-abc6-04bcd26ab5df open.access open.access <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="025c319c-dfd0-454b-afb4-51b30514bfb5"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>en_US</Language> <Title>Operation under Uncertainty in Electric Grid: A Multiparametric Programming Approach</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017-01-30</PublicationDate> <DOI>https://doi.org/10.7298/X4J38QHQ</DOI> <Authors> <Author> <DisplayName>Ji, Yuting</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <OrgUnit /> </Publisher> </Publishers> <Keyword>Electrical engineering</Keyword> <Keyword>multiparametric programming</Keyword> <Keyword>power system</Keyword> <Keyword>probabilistic forecasting</Keyword> <Keyword>smart grid</Keyword> <Keyword>stochastic optimization</Keyword> <Abstract>Uncertainty is a major factor in power system operations. In recent years, with the emergence of the smart grid, uncertainty level has been further elevated in both the generation and demand side of power systems. Increasing uncertainty exposes the electric grid to potential safety issues and economic loss, thus posing significant challenges to the grid operations. Traditionally, power system operations use certainty equivalent approach to deal with uncertainty, i.e., replacing random variables by their expected values. With this simplification, the original stochastic optimization is reduced to a deterministic problem. However, the certainty equivalent method is inadequate for the modern electric grid with deep penetration of distributed energy resources. Due to increasing uncertainty, operations and decision makings need to incorporate system dynamics over a broad range of temporal and spatial horizons. To this end, this thesis provides a new paradigm for operation under uncertainty and computationally efficient algorithms based on multiparametric programming theory. Under this new paradigm, uncertainty is characterized by conditional distributions and decisions are made by incorporating such probabilistic descriptions. To illustrate the new paradigm, we consider two specific problems. For characterization of system uncertainty, we develop a formal methodology for probabilistic forecasting of real-time operations and locational marginal prices. Conditioning on the current system state, we provide a full distribution of future operations and prices. For operational decision making, we propose an optimal stochastic approach to interchange scheduling in multi-area systems. By incorporating the conditional distribution of load and generation, the optimal interchange is obtained through an iterative process.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>