Variance-Based Global Sensitivity Analysis for Power Systems

Ni, Fei; Nijhuis, Michiel; Nguyen, Phuong H.; Cobben, J.F.G.

doi:10.1109/tpwrs.2017.2719046

Cited by 61 publications

(24 citation statements)

References 33 publications

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“…Variance-based SA [ 33 ] is the most commonly used method. In addition, there are other SA methods, such as Kullback–Liebler divergence methods, screening methods, entropy-based SA, etc.…”

Section: Risk Control Considering Uncertaintymentioning

confidence: 99%

Entropy-Based Risk Control of Geological Disasters in Mountain Tunnels under Uncertain Environments

Xia

Xiong

Lü

et al. 2018

Entropy

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Uncertainty is one of the main sources of risk of geological hazards in tunnel engineering. Uncertainty information not only affects the accuracy of evaluation results, but also affects the reliability of decision-making schemes. Therefore, it is necessary to evaluate and control the impact of uncertainty on risk. In this study, the problems in the existing entropy-hazard model such as inefficient decision-making and failure of decision-making are analysed, and an improved uncertainty evaluation and control process are proposed. Then the tolerance cost, the key factor in the decision-making model, is also discussed. It is considered that the amount of change in risk value (R 1) can better reflect the psychological behaviour of decision-makers. Thirdly, common multi-attribute decision-making models, such as the expected utility-entropy model, are analysed, and then the viewpoint of different types of decision-making issues that require different decision methods is proposed. The well-known Allais paradox is explained by the proposed methods. Finally, the engineering application results show that the uncertainty control idea proposed here is accurate and effective. This research indicates a direction for further research into uncertainty, and risk control, issues affecting underground engineering works.

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Section: Risk Control Considering Uncertaintymentioning

confidence: 99%

Entropy-Based Risk Control of Geological Disasters in Mountain Tunnels under Uncertain Environments

Xia

Xiong

Lü

et al. 2018

Entropy

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“…An alternative could be a global sensitivity analysis (GSA) that can determine the impact of input uncertainties on the output quantity of interest in a certain physical or mathematical model. This methodology is increasingly discussed for power systems simulations, as [25] shows, and it is useful to establish global relationships between inputs in order to generate stochastic models that are specific to a power system. Our approach is based on an econometric estimation that looks for the relationship between contract prices and the before mentioned variables, using historical data.…”

Section: Average Contract Price Projectionmentioning

confidence: 99%

Electricity Interconnection in Chile: Prices versus Costs

Bustos-Salvagno

Fuentes

2017

Energies

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Having a well-adapted transmission network is key for reaching a sustainable energy system, where generators and consumers participate in an efficient market. The purpose of the present research is to estimate the impact on the final prices of the interconnection of the two largest electrical systems in Chile. To this end, an econometric model is used in which prices, among other variables, are explained by the level of marginal costs in the system (opportunity cost), the degree of concentration in the market (market power), and the variability of marginal costs projected by the agents (risk). The data corresponds to bilateral contracts between generators and major clients, from 2006 to 2014. The basic results indicate that the interconnection would benefit final customers by reducing equilibrium contract prices, as a result of the decreased variability of expected marginal costs and a lower concentration of existing generating companies. This study's main contribution is to focus on the benefits of electricity interconnection under conditions of competition and risk, instead of using the simple estimation of direct costs, as has traditionally been done.

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“…GSA has been used by scholars in power systems [13]- [15]. GSA is divided into 4 types: non-parametric method, regression method, analysis of variance method and density method.…”

Section: Introductionmentioning

confidence: 99%

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Tian¹,

Lii²,

Zhao³

et al. 2020

IEEE Access

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Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (π model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (FSI)and Total Sensitivity Indices (TSI). Secondly, the FSI and TSI of each parameter are analyzed, and the effect on the output of model of each parameter is determined by FSI. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of TSI. The parameter whose TSI equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models.

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Variance-Based Global Sensitivity Analysis for Power Systems

Cited by 61 publications

References 33 publications

Entropy-Based Risk Control of Geological Disasters in Mountain Tunnels under Uncertain Environments

Entropy-Based Risk Control of Geological Disasters in Mountain Tunnels under Uncertain Environments

Electricity Interconnection in Chile: Prices versus Costs

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

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