Probabilistic Steady-State Security Assessment of an Electric Power System Using A Monte Carlo Approach

Agreira, C. I. Faustino; Jesus, S. M. Fonseca de; Figueiredo, S. Lopes de; Ferreira, C. Machado; Pinto, Jervis; Barbosa, F. P. Maciel

doi:10.1109/upec.2006.367509

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Monte Carlo simulations were performed to predict the probabilistic wind power generation scenarios based on the probability distribution parameters; these were calculated by providing a probabilistic fitting of the clustered or regional data. In Monte Carlo simulations, performance data are randomly sampled based on parameters extracted with a probabilistic fit; the results are used to model the uncertainty of system elements and evaluate the probabilistic steady-state stability of a power system [29]. In addition, the normal and Weibull distribution parameters for the Monte Carlo simulations were calculated.…”

Section: B Monte Carlo Simulationmentioning

confidence: 99%

Probabilistic Estimation of Wind Generating Resources Based on the Spatio-Temporal Penetration Scenarios for Power Grid Expansions

2021

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The proportion of renewable energy generation is expanding worldwide with the goal of reducing greenhouse gas. According to the 8th Basic Plan for Long-term Electricity Supply and Demand in South Korea, South Korea reduces traditional energy generation such as nuclear and coal plants and achieves 20% (58.5GW) of renewable energy generation by 2030. Wind Generating Resources (WGRs) are affected by meteorological variables such as temperature, wind speed and wind direction. Specifically, WGRs have uncertainty and variability issues depending on temporal and spatial characteristics. In this paper, we propose the probabilistic estimation of wind generating resources based on the spatiotemporal penetration scenarios for power grid expansion. The data of WGRs are analyzed based on clustering method considering the spatiotemporal penetration scenarios, and the potential scenarios are estimated using Monte Carlo simulation by selecting a representative power distribution probability for each cluster. The proposed estimation model of WGRs will play a key role to develop the hedging strategies of investment decision on power grid expansion planning with high wind power penetrations. INDEX TERMS Probabilistic model and estimation, wind generating resources, spatiotemporal penetration scenarios, Monte Carlo simulation, power grid expansion.

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Section: B Monte Carlo Simulationmentioning

confidence: 99%

Probabilistic Estimation of Wind Generating Resources Based on the Spatio-Temporal Penetration Scenarios for Power Grid Expansions

2021

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“…The following papers evaluated power system security using a probabilistic approach. In [8], the assessment method of power system contingency security using MCS with the developed software is proposed. In [9], the self-organizing map (SOM) and MCS-based power system security evaluation method were developed and applied to IEEE RTS.…”

Section: Literature Reviewmentioning

confidence: 99%

A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis

Kim

Hur

2020

Energies

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As renewable energy resources such as wind and solar power are developing and the penetration of electric vehicles (EVs) is increasingly integrated into existing systems, uncertainty and variability in power systems have become important issues. The charging demands for EVs and wind power output are recognized as highly variable generation resources (VGRs) with uncertainty, which can cause unexpected disturbances such as short circuits. This can deteriorate the reliability of existing power systems. In response, research is required to identify the uncertainties presented by VGRs and is required to examine the ability of power system models to reflect those uncertainties. The deterministic method, which is the most basic method that is currently in use, does not reflect the uncertainty of system components. Therefore, this paper proposes a probabilistic method to assess the steady-state security of power systems, reflecting the uncertainty of VGRs using Monte Carlo simulation (MCS). In the proposed method, the empirical EVs charging demand and wind power output data are modeled as a probability distribution, and then MCS is performed, integrating the power system operation to represent the steady-state security as a probability index. To verify the method proposed in this paper, a security analysis was performed based on the systems in Jeju Island, South Korea, where the penetration of wind power and EVs is expanding rapidly.

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“…(11). Larger value N i causes larger fault detection time, because the algorithm does not use the latest signal samples.…”

Section: An Optimal Polygonal Operating Characteristic In the Time-domentioning

confidence: 99%

“…The second problem is that security and dependability are not exactly assessed despite being the most important features of distance protections. The papers where the dependability and security are assessed are rare [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of new time-domain distance relay algorithms

Zubic

Djurić

Zeljković

2015

Electric Power Systems Research

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Probabilistic Steady-State Security Assessment of an Electric Power System Using A Monte Carlo Approach

Cited by 8 publications

References 4 publications

Probabilistic Estimation of Wind Generating Resources Based on the Spatio-Temporal Penetration Scenarios for Power Grid Expansions

Probabilistic Estimation of Wind Generating Resources Based on the Spatio-Temporal Penetration Scenarios for Power Grid Expansions

A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis

Probabilistic assessment of new time-domain distance relay algorithms

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