On-line monitoring of eigen-frequency and stability of power system by use of SMES

Shirai, Yoshihito; Nitta, T.; Shibata, Katsuhiko

doi:10.1016/j.ijepes.2012.03.052

Cited by 3 publications

(1 citation statement)

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“…In addition, there are myriads of methodologies and mathematical models for the selection of network parameters and compensation in the power system planning and operations, e.g., optimization techniques, genetic algorithm (GA), trial and error methodologies, fuzzy integrated with dynamic programming, artificial neural network (ANN) and heuristic analysis [3,[6][7][8]10,11,16,[22][23][24][25][27][28][29][30] which have been in use in the most of the existing software's [31,32]. It is worth emphasizing that the precise estimation of the transmission line parameters and associated compensation could hinder an occurrence of blackout in the power system with an increasing efficiency.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic geometric analysis of the network reliability and voltage stability

Gupta

Tiwari

Bellucci

2013

International Journal of Electrical Power & Energy Systems

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a b s t r a c tThis paper presents the intrinsic geometric model for the solution of power system planning and its operation. This problem is large-scale and nonlinear, in general. Thus, we have developed the intrinsic geometric model for the network reliability and voltage stability, and examined it for the two and three parameter systems. The robustness of the proposed model is illustrated by introducing variations of the network parameters. Exact analytical results show the accuracy as well as the efficiency of the proposed solution technique.

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Section: Introductionmentioning

confidence: 99%

Intrinsic geometric analysis of the network reliability and voltage stability

Gupta

Tiwari

Bellucci

2013

International Journal of Electrical Power & Energy Systems

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Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis

Hemmati

Saboori

2016

Journal of Advanced Research

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Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics.

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