Research on Solving Method of Security Constrained Unit Commitment Based on Improved Stochastic Constrained Ordinal Optimization

Yang, Nan; Ye, Di; Zheng, Zhou; Huang, Yü; Dong, Bangtian

doi:10.1109/igbsg.2018.8393518

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…To the best of our knowledge, there are few studies that address the computational complexity of the SSCUC problem and focus on solving the problem quickly and effectively. Among them, Wang and Fu [1] proposed a fully parallel stochastic SCUC approach to obtain efficient and fast solutions in large-scale electrical systems and N. Yang et al [25] proposed a method that aims to improve the efficiency of the SSCUC problem under constrained ordinal optimization (COO). This approach has two advantages: The first one is a recognition of discrete variables to reduce the solution space of the approximate COO rough model.…”

Section: Introductionmentioning

confidence: 99%

“…Following the authors of [1,25], in this work, we propose a novel and easy-to-implement strategy to efficiently solve a SSCUC problem under the uncertainty of large penetration of intermittent energy sources. This strategy has the following features: (i) it uses sensitivity linear factors to compute power flows to reduce the number of variables and constraints when compared to conventional network modeling; (ii) it considers user cuts to dynamically add active N − 1 security constraints, as proposed in [14]; and (iii) it uses the PHA decomposition algorithm to relax the SSCUC model.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

et al. 2020

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The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

et al. 2020

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“…Ref. [17] clustered and preprocessed the historical scheduling based on the K-means algorithm then constructed a deep learning model of unit combination based on long short-term memory network and proposed a data-driven intelligent decisionmaking method for unit combination with self-learning ability. Ref.…”

Section: Introductionmentioning

confidence: 99%

Visual Extraction of Refined Operation Mode of New Power System Based on IPSO-Kmeans

et al. 2023

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Due to the influence of the high proportion of renewable energy penetration, the time-varying and complex operation mode of the new power system is gradually increasing, leading to a lack of fineness and practicality of traditional operation modes. To this end, a new visual extraction method for fine operation mode of power system is proposed. Specifically, aiming at the dimensional problem between high-dimensional electrical characteristic variables, a power grid operation data preprocessing method based on maximum absolute standardization (MaxAbs) is designed. Then, in order to reduce the impact of redundant features on the accuracy of the operation mode extraction results, the Pearson correlation coefficient is introduced to optimize the feature space relationship matrix, constructing a screening model of operating mode characteristic variables based on pearson kernel principal component analysis (P_KPCA). Then, with the clustering elbow index as the constraint condition, a K-means algorithm based on improved particle swarm optimization (IPSO-Kmeans) was proposed to realize fine operation mode extraction. Finally, the experimental analysis is carried out with the actual operation data of the power grid for one year and based on uniform manifold approximation and projection (UMAP) to visualize the extraction results of the operation mode. The validity and accuracy of the proposed method are verified.

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“…Hoy en día existe poca literatura que aborde de manera estratégica la complejidad computacional del SSCUC. En este sentido, en [13] se propone un procedimiento totalmente paralelizado para resolver el SSCUC obteniendo soluciones eficientes y rápidas en sistemas eléctricos a gran escala y en [14] se propone un método que apunta a mejorar la eficiencia del SSCUC bajo optimización ordinal restricta. Siguiendo el enfoque de investigación abordado en [13,14], en este trabajo se propone una estrategia para resolver eficientemente el SSCUC considerando la incertidumbre de fuentes de generación intermitentes.…”

Section: Introductionunclassified

Metodología computacionalmente eficiente para resolver el despacho económico multiperiodo estocástico con restricciones de seguridad

López-Lezama

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Cano

et al. 2021

Investigación e Innovación en Ingenierías

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Objetivo: Validar una metodología computacionalmente eficiente para resolver el problema de despacho económico multiperiodo estocástico con restricciones de seguridad. Metodología: Se utilizan factores lineales de sensibilidad para calcular flujos de carga de forma rápida y precisa. También se usa un método iterativo, que identifica y agrega como cortes de usuario, las restricciones de seguridad activas. Estas restricciones establecen la región factible de un modelo embebido dentro de un algoritmo Progressive Hedging, el cual descompone el problema principal en un conjunto de sub-problemas computacionalmente más tratables, al relajar las restricciones de acoplamiento entre escenarios. Resultados: Los resultados numéricos sobre el sistema IEEE RTS96, muestran que la estrategia propuesta entrega soluciones de alta calidad en bajos tiempos de cálculo. Conclusiones: La metodología propuesta permite solucionar el despacho económico multiperiodo estocástico seguro hasta 50 veces más rápido cuando se compara con su formulación extensiva.

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Research on Solving Method of Security Constrained Unit Commitment Based on Improved Stochastic Constrained Ordinal Optimization

Cited by 4 publications

References 3 publications

A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

Visual Extraction of Refined Operation Mode of New Power System Based on IPSO-Kmeans

Metodología computacionalmente eficiente para resolver el despacho económico multiperiodo estocástico con restricciones de seguridad

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