The Experts below are selected from a list of 123183 Experts worldwide ranked by ideXlab platform
José M. Arroyo - One of the best experts on this subject based on the ideXlab platform.
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Energy and Reserve Scheduling Under a Joint Generation and Transmission Security Criterion: An Adjustable Robust Optimization Approach
IEEE Transactions on Power Systems, 2014Co-Authors: Alexandre Street, Alexandre Moreira, José M. ArroyoAbstract:This paper presents a new approach for energy and reserve scheduling in electricity markets subject to transmission flow limits. Security is imposed by guaranteeing power balance under each Contingency state including both generation and transmission assets. The model is general enough to embody a joint generation and transmission n-K security criterion and its variants. An adjustable robust optimization approach is presented to circumvent the tractability issues associated with conventional Contingency-constrained methods relying on explicitly modeling the whole Contingency set. The adjustable robust model is formulated as a trilevel programming problem. The upper-level problem aims at minimizing total costs of energy and reserves while ensuring that the system is able to withstand each Contingency. The middle-level problem identifies, for a given pre-Contingency schedule, the Contingency state leading to maximum power imbalance if any. Finally, the lower-level problem models the operator's best reaction for a given Contingency by minimizing the system power imbalance. The proposed trilevel problem is solved by a Benders decomposition approach. For computation purposes, a tighter formulation for the master problem is presented. Our approach is finitely convergent to the optimal solution and provides a measure of the distance to the optimum. Simulation results show the superiority of the proposed methodology over conventional Contingency-constrained models.
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Contingency-Constrained Unit Commitment With $n - K$ Security Criterion: A Robust Optimization Approach
IEEE Transactions on Power Systems, 2011Co-Authors: Alexandre Street, Fabrício Oliveira, José M. ArroyoAbstract:This paper presents a new approach for the Contingency-constrained single-bus unit commitment problem. The proposed model explicitly incorporates an n - K security criterion by which power balance is guaranteed under any Contingency state comprising the simultaneous loss of up to K generation units. Instead of considering all possible Contingency states, which would render the problem intractable, a novel method based on robust optimization is proposed. Using the notion of umbrella Contingency, the robust counterpart of the original problem is formulated. The resulting model is a particular instance of bilevel programming which is solved by its transformation to an equivalent single-level mixed-integer programming problem. Unlike previously reported Contingency-dependent approaches, the robust model does not depend on the size of the set of credible contingencies, thus providing a computationally efficient framework. Simulation results back up these conclusions.
Alexandre Street - One of the best experts on this subject based on the ideXlab platform.
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Energy and Reserve Scheduling Under a Joint Generation and Transmission Security Criterion: An Adjustable Robust Optimization Approach
IEEE Transactions on Power Systems, 2014Co-Authors: Alexandre Street, Alexandre Moreira, José M. ArroyoAbstract:This paper presents a new approach for energy and reserve scheduling in electricity markets subject to transmission flow limits. Security is imposed by guaranteeing power balance under each Contingency state including both generation and transmission assets. The model is general enough to embody a joint generation and transmission n-K security criterion and its variants. An adjustable robust optimization approach is presented to circumvent the tractability issues associated with conventional Contingency-constrained methods relying on explicitly modeling the whole Contingency set. The adjustable robust model is formulated as a trilevel programming problem. The upper-level problem aims at minimizing total costs of energy and reserves while ensuring that the system is able to withstand each Contingency. The middle-level problem identifies, for a given pre-Contingency schedule, the Contingency state leading to maximum power imbalance if any. Finally, the lower-level problem models the operator's best reaction for a given Contingency by minimizing the system power imbalance. The proposed trilevel problem is solved by a Benders decomposition approach. For computation purposes, a tighter formulation for the master problem is presented. Our approach is finitely convergent to the optimal solution and provides a measure of the distance to the optimum. Simulation results show the superiority of the proposed methodology over conventional Contingency-constrained models.
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Contingency-Constrained Unit Commitment With $n - K$ Security Criterion: A Robust Optimization Approach
IEEE Transactions on Power Systems, 2011Co-Authors: Alexandre Street, Fabrício Oliveira, José M. ArroyoAbstract:This paper presents a new approach for the Contingency-constrained single-bus unit commitment problem. The proposed model explicitly incorporates an n - K security criterion by which power balance is guaranteed under any Contingency state comprising the simultaneous loss of up to K generation units. Instead of considering all possible Contingency states, which would render the problem intractable, a novel method based on robust optimization is proposed. Using the notion of umbrella Contingency, the robust counterpart of the original problem is formulated. The resulting model is a particular instance of bilevel programming which is solved by its transformation to an equivalent single-level mixed-integer programming problem. Unlike previously reported Contingency-dependent approaches, the robust model does not depend on the size of the set of credible contingencies, thus providing a computationally efficient framework. Simulation results back up these conclusions.
Shusaku Tsumoto - One of the best experts on this subject based on the ideXlab platform.
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Contingency matrix theory statistical dependence in a Contingency table
Information Sciences, 2009Co-Authors: Shusaku TsumotoAbstract:Chance discovery aims at understanding the meaning of functional dependency from the viewpoint of unexpected relations. One of the most important observations is that such a chance is hidden under a huge number of coocurrencies extracted from a given data. On the other hand, conventional data-mining methods are strongly dependent on frequencies and statistics rather than interestingness or unexpectedness. This paper discusses some limitations of ideas of statistical dependence, especially focusing on the formal characteristics of Simpson's paradox from the viewpoint of linear algebra. Theoretical results show that such a Simpson's paradox can be observed when a given Contingency table as a matrix is not regular, in other words, the rank of a Contingency matrix is not full. Thus, data-ordered evidence gives some limitations, which should be compensated by human-oriented reasoning.
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on pseudo statistical independence in a Contingency table
Data Mining: Foundations and Practice, 2008Co-Authors: Shusaku TsumotoAbstract:A Contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other with the information on a partition of universe generated by these attributes. Thus, this table can be viewed as a relation between two attributes with respect to information granularity. This chapter focuses on several characteristics of linear and statistical independence in a Contingency table from the viewpoint of granular computing, which shows that statistical independence in a Contingency table is a special form of linear dependence. The discussions also show that when a Contingency table is viewed as a matrix, called a Contingency matrix, its rank is equal to 1.0. Thus, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given Contingency table. Furthermore, it is found that in some cases, partial rows or columns will satisfy the condition of statistical independence, which can be viewed as a solving process of Diophatine equations.
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Contingency matrix theory I: rank and statistical independence in a contigency table
Lecture Notes in Computer Science, 2008Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:A Contingency table summarizes the conditional frequenciesof two attributes and shows how these two attributes are dependent oneach other with the information on a partition of universe generatedby these attributes. This paper discusses statistical independence in aContingency table from the viewpoint of matrix theory. Statistical independenceis equivalent to linear dependence of all columns or rows.Also, the equations of statistical independence are equivalent to thoseon collinearity of projective geometry.
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statistical independence from the viewpoint of linear algebra
International Syposium on Methodologies for Intelligent Systems, 2005Co-Authors: Shusaku TsumotoAbstract:A Contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other with the information on a partition of universe generated by these attributes. Thus, this table can be viewed as a relation between two attributes with respect to information granularity. This paper focuses on statistical independence in a Contingency table from the viewpoint of granular computing, which shows that statistical independence in a Contingency table is a special form of linear dependence. The discussions also show that when a Contingency table is viewed as a matrix, its rank is equal to 1.0. Thus, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given Contingency table.
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Statistical Independence as Linear Independence
Electronic Notes in Theoretical Computer Science, 2003Co-Authors: Shusaku TsumotoAbstract:Abstract A Contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other. Thus, this table is a fundamental tool for pattern discovery with conditional probabilities, such as rule discovery. In this paper, a Contingency table is interpreted from the viewpoint of granular computing. The first important observation is that a Contingency table compares two attributes with respect to the number of equivalence classes. The second important observation is that matrix algebra is a key point of analysis of this table. Especially, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given Contingency table.
Shoji Hirano - One of the best experts on this subject based on the ideXlab platform.
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Contingency matrix theory I: rank and statistical independence in a contigency table
Lecture Notes in Computer Science, 2008Co-Authors: Shusaku Tsumoto, Shoji HiranoAbstract:A Contingency table summarizes the conditional frequenciesof two attributes and shows how these two attributes are dependent oneach other with the information on a partition of universe generatedby these attributes. This paper discusses statistical independence in aContingency table from the viewpoint of matrix theory. Statistical independenceis equivalent to linear dependence of all columns or rows.Also, the equations of statistical independence are equivalent to thoseon collinearity of projective geometry.
Fabrício Oliveira - One of the best experts on this subject based on the ideXlab platform.
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Contingency-Constrained Unit Commitment With $n - K$ Security Criterion: A Robust Optimization Approach
IEEE Transactions on Power Systems, 2011Co-Authors: Alexandre Street, Fabrício Oliveira, José M. ArroyoAbstract:This paper presents a new approach for the Contingency-constrained single-bus unit commitment problem. The proposed model explicitly incorporates an n - K security criterion by which power balance is guaranteed under any Contingency state comprising the simultaneous loss of up to K generation units. Instead of considering all possible Contingency states, which would render the problem intractable, a novel method based on robust optimization is proposed. Using the notion of umbrella Contingency, the robust counterpart of the original problem is formulated. The resulting model is a particular instance of bilevel programming which is solved by its transformation to an equivalent single-level mixed-integer programming problem. Unlike previously reported Contingency-dependent approaches, the robust model does not depend on the size of the set of credible contingencies, thus providing a computationally efficient framework. Simulation results back up these conclusions.
