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Ignacio J. Prez Arriaga and Carlos Batlle. Leung, Tommy (Tommy Chun Ting) Massachusetts Institute of Technology. Technology and Policy Program. Massachusetts Institute of Technology. Engineering Systems Division Technology and Policy Program 2012-09-13T19:00:12Z 2012-09-13T19:00:12Z 2012 2012 http://hdl.handle.net/1721.1/72895 808482742 Thesis (S.M. in Technology and Policy)-- Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 73-74). The growth of renewables in power systems has reinvigorated research and regulatory interest in reliability analysis algorithms such as the Baleriaux/Booth convolution-based probabilistic production cost (PPC) model. However, while these traditional PPC algorithms can reasonably represent thermal plant availabilities, they do not accurately represent limited energy plants because of their generic treatment of time. In particular, in systems with limited energy plants, convolution-based PPC models tend to underestimate the loss-of-load probability and expected nonserved energy. This thesis illustrates the chronological challenges of the traditional convolution-based PPC, proposes a modification that improves the representation of chronological elements, explores the reliability contribution of LEPs using the new algorithm, and demonstrates two regulatory applications by calculating a capacity payment for an LEP and the expected-load-carrying-capability metric for any generator. To the best knowledge of the author, the introduction of multiple hydro plants with different capacity constraints and the calculations for marginal probabilities, prices, and revenues to a chronological PPC model are novel. by Tommy Leung. S.M. in Technology and Policy 74 p. 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 Engineering Systems Division. Technology and Policy Program. A chronological probabilistic production cost model to evaluate the reliability contribution of limited energy plants Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="cfeecec5-3c37-4182-b380-8780d0dce5f8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A chronological probabilistic production cost model to evaluate the reliability contribution of limited energy plants</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2012</PublicationDate> <Authors> <Author> <DisplayName>Leung, Tommy (Tommy Chun Ting)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering Systems Division.</Keyword> <Keyword>Technology and Policy Program.</Keyword> <Abstract>The growth of renewables in power systems has reinvigorated research and regulatory interest in reliability analysis algorithms such as the Baleriaux/Booth convolution-based probabilistic production cost (PPC) model. However, while these traditional PPC algorithms can reasonably represent thermal plant availabilities, they do not accurately represent limited energy plants because of their generic treatment of time. In particular, in systems with limited energy plants, convolution-based PPC models tend to underestimate the loss-of-load probability and expected nonserved energy. This thesis illustrates the chronological challenges of the traditional convolution-based PPC, proposes a modification that improves the representation of chronological elements, explores the reliability contribution of LEPs using the new algorithm, and demonstrates two regulatory applications by calculating a capacity payment for an LEP and the expected-load-carrying-capability metric for any generator. To the best knowledge of the author, the introduction of multiple hydro plants with different capacity constraints and the calculations for marginal probabilities, prices, and revenues to a chronological PPC model are novel.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>