Probabilistic Congestion Management Considering Power System Uncertainties Using Chance-constrained Programming

Hojjat, Mehrdad

doi:10.1080/15325008.2013.801054

Cited by 20 publications

(27 citation statements)

References 21 publications

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“…The same value is employed for α l to have a better comparison between the stochastic CM approaches. The proposed stochastic CM is solved with 3 methods: the method proposed by [18], and the linear and nonlinear analytical approaches. It is clear that the solution of the analytical method has prominence over the solution obtained by the numerical method, because the proposed method not only has the flexibility to set the confidence level, but also uses fewer approximations compared to the method in [9].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This is due to the fact that this model has a smaller value of constraint violation probability. Determ n st c [11] Numer cal [18] L near Nonl near Figure 5. Redispatch power of the market participants using different approaches.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The stochastic CM problem, formulated by CCP, was solved in [18] by using a combined approach that includes real-coded GA and Monte Carlo simulation, as in [16]. The real-coded GA was incorporated into the Monte Carlo simulation to solve the proposed stochastic CM.…”

Section: Formulating the Stochastic CM Based On Ccpmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Hojjat¹,

Javidi²,

Yazdanpanah³

2016

Turk J Elec Eng & Comp Sci

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Considering system uncertainties in developing power systems, algorithms such as congestion management (CM) are vital in power system analysis and studies. This paper proposes a new model for power system CM by considering power system uncertainties based on chance-constrained programming (CCP). In the proposed approach, transmission constraints are taken into account by stochastic, instead of deterministic, models. The proposed approach considers network uncertainties with a specific level of probability in the optimization process, and then an analytical approach is used to solve the new model of stochastic congestion management. In this approach, the stochastic optimization problem is transformed into an equivalent deterministic problem. Moreover, an efficient numerical approach based on a real-coded genetic algorithm and Monte Carlo technique is proposed to solve the CCP-based congestion management problem in order to make a comparison to the analytical approach. The effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30-bus test system. The results show that the proposed CCP model and the analytical solving approach outperform the existing models.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Hojjat¹,

Javidi²,

Yazdanpanah³

2016

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the congestion at hour 17-19 is solved relatively slow. It takes about 200 iterations for the blue, magenta, yellow and purple (hour [16][17][18][19] curves, especially in the second, third and fourth sub graphs, to settle down. Two reasons are accountable for this.…”

Section: ) Congestion Management Resultsmentioning

confidence: 99%

Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

Huang

Zhao

et al. 2019

IEEE Trans. Smart Grid

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“…The adoption of WAMSs based on PMUs could allow the SGs to be operated closer to its stability limits by: (i) monitoring in real time the power flows in interconnected areas; (ii) identifying the power system dynamics and (iii) detecting inter-area oscillations [13,14,15]. As a consequence, the SGs operator could exploit the transmission and generation capacity more efficiently, the renewable power generators can be used more effectively, and the marginal cost of power generation can be reduced [16,17].…”

Section: Introductionmentioning

confidence: 99%

Vulnerability Analysis of Satellite-based Synchronized Smart Grids Monitoring Systems

Vaccaro

Zobaa

Formato

2014

Electric Power Components and Systems

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The large scale deployment of Wide-Area Monitoring Systems (WAMSs) could play a strategic role in supporting the evolution of the traditional power systems towards smarter and self healing grids. The correct operation of these synchronized monitoring systems requires a common and accurate timing reference usually provided by satellite based Global Positioning System (GPS). Although, these satellites signals provide timing accuracy that easily exceeds the needs of the power industry, they are extremely vulnerable to radiofrequency interference. Consequently a comprehensive analysis aimed at identifying their potential vulnerabilities is of paramount importance for a correct and safe WAMSs operation. Armed with such a vision, this paper presents and discusses the results of an experimental analysis aimed at characterising the vulnerability of GPS-based WAMSs to external interferences. The paper outlines the potential strategies that could be adopted to protect GPS receivers from external cyber-attacks and proposes decentralized defense strategies based on self organizing sensor networks aimed at assuring the correct time synchronization in the presence of external attacks.

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Probabilistic Congestion Management Considering Power System Uncertainties Using Chance-constrained Programming

Cited by 20 publications

References 21 publications

Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

Vulnerability Analysis of Satellite-based Synchronized Smart Grids Monitoring Systems

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