A MILP-based restoration planning method for generator start-up considering flexible re-energizing times of transmission lines

Xie, Yunxiang; Li, Dezheng; Xu, Yan; Wu, Qiuwei

doi:10.1016/j.ijepes.2020.106357

Cited by 11 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where V i is the voltage at the i th node, V i+1 is the voltage at (i + 1) th node, P r + jQ r is the receiving end power, P i + jQ i is the sending end power, and R + jX is the impedance of the line. Te line stability factor values are calculated using equations ( 12)- (15). Te line stability factors show four diferent values for a single line.…”

Section: Startmentioning

confidence: 99%

“…Te set of nodes belonging to some initial partitions are represented by a single node for simplifcation, V40 � {V1, V2, V3, V4, V5, V6, V7, V10, V11, V13, V14, V25, V30, V31, V32, V37} and V41 � {V15, V16, V17, V19, V20, V21, V22, V23, V24, V26, V27, V28, V29, V33, V34, V35, V36, V38}. Te cutting sets to separate the partitions are e (14,15), e (3,18), and e (25,26), where the maximum generation-load diference of the 1 st partition is −96.11 MVAR and the 2 nd partition is 321.18 MVAR.…”

Section: Agglomerative Hierarchical Clusteringmentioning

confidence: 99%

“…When a proper cranking path is ensured, there is a necessity for a proper generator startup sequence. Te generator startup sequencing afects the restoration time and plays a crucial factor during the restoration of a system [15]. For fast and efcient restoration of a system, a study [15] shows a new serial generator startup sequencing (GSUS) optimization model with fexible reenergizing times of transmission lines.…”

Section: Introductionmentioning

confidence: 99%

“…Te generator startup sequencing afects the restoration time and plays a crucial factor during the restoration of a system [15]. For fast and efcient restoration of a system, a study [15] shows a new serial generator startup sequencing (GSUS) optimization model with fexible reenergizing times of transmission lines. Te GSUS is formulated considering mixed integral linear programming (MILP), in which variables are restoration time constraints for the transmission lines, startup time constraints for the buses on the restoration paths, and a serial restoration constraint, whereas the restart time constraints of the generators and the transient frequency requirements were modeled as mixed integer linear constraints.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Khadka,

Wagle,

Dhakal

et al. 2024

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

Despite there are significant advancements in modern power systems, blackouts remain a potential risk, necessitating efficient restoration strategies. This paper introduces an innovative concept for power system restoration, focusing on balancing active and reactive power while ensuring voltage stability. For instance, this paper employs an agglomerative clustering technique, which partitions the power system into segments with balanced reactive power, facilitating swift restoration postblackout. Central to this methodology is the use of the line stability factor, which assesses the voltage stability of individual lines, identifying the system’s stronger and weaker sections based on voltage stability levels. This paper demonstrates the effectiveness of the proposed methodology through case study analysis, comparing voltage stability levels across agglomerative clusters and their geographical locations. The power system is divided into two stable partitions, considering the number of black-start generators, available reactive power, and voltage stability levels. This partitioning reveals that the clusters formed by the agglomerative method are inherently stable, suggesting enhanced system stability, dependability, and availability during the restoration phase following a blackout. In addition, this paper discusses the potential causes of blackouts, offering insights into their prevention, and finishes with a novel clustering methodology for power systems, considering reactive power and voltage stability. This method facilitates the parallel restoration of the system’s independent partitions, significantly reducing restoration time; it addresses critical challenges and outcomes, underscoring the methodology’s potential to revolutionize blackout recovery processes in modern power systems.

show abstract

Section: Startmentioning

confidence: 99%

Section: Agglomerative Hierarchical Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Khadka,

Wagle,

Dhakal

et al. 2024

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…The MILP formulations can be also applied to represent other types of operations, for example, restoration. In this context, the authors of (Xie et al, 2021) develop an optimization model that can generate a flexible reenergizing of transmission lines. Besides, both start-up time and serial restoration constraints are considered.…”

Section: Introductionmentioning

confidence: 99%

Improving the optimization of electric power systems through a discrete event based simulation model

Blas

Alvarez²,

Sarli³

2023

JART

View full text Add to dashboard Cite

This paper presents a novel hybrid approach for studying electric power systems as a combination of the electrical power generation process and the actual state of the transmission lines. The approach is composed of a discrete-event simulation model and a mathematical programming model. The simulation model is designed as a routing problem defined over a set of transmission lines considered (individually) as active, inactive, under maintenance, and outage. On the other hand, the mixed-integer lineal programming model determines the best way to generate and transmit power flows (considering, simultaneously, all possible solutions). This type of model allows solving the economic dispatch problem in lower computational times. It also ensures reaching the global optimum. When adding the discrete-event simulation model for studying the state of transmission lines, the final hybrid approach allows obtaining feasible solutions when the system parameters change. Here, both models (i.e., simulation and optimization models) are combined to improve the capabilities of the model structure to represent real-life scenarios. Our proposal is used to analyze a case study composed of an electric system with six buses, eleven lines, and three generators.

show abstract

Optimization of loop-network reconfiguration strategies to eliminate transmission line overloads in power system restoration process with wind power integration

Wang

et al. 2022

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

A MILP-based restoration planning method for generator start-up considering flexible re-energizing times of transmission lines

Cited by 11 publications

References 28 publications

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Improving the optimization of electric power systems through a discrete event based simulation model

Optimization of loop-network reconfiguration strategies to eliminate transmission line overloads in power system restoration process with wind power integration

Contact Info

Product

Resources

About