Power Industry Risk Assessment: Current Practices

Author(s):  
Stanislav Eroshenko ◽  
Andrey Bramm ◽  
Elena L. Zinovieva ◽  
Olga S. Vozisova
2014 ◽  
Author(s):  
Bernardo Brazao Rego Mello ◽  
Sergio Augusto Novis Filho ◽  
Luiz Flavio Autran Monteiro Gomes

2021 ◽  
pp. 1-10
Author(s):  
Sukran Seker

The glass manufacturing includes operations, such as batch forming using raw materials, melting, forming, annealing, quality check and package. Due to risky processes in glass manufacturing, significant health hazards for workers are present in the glass industry. Risk assessment is effective way to prevent accidents and protect workers from serious accidents during glass manufacturing. To assess health hazards associated with glass manufacturing, in this study Risk Matrix and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are integrated under Interval-Valued Intuitionistic Fuzzy (IVIF) environment to prioritize risk factors and suggest required preventive and protective measures. Suggested preventive and protective measures provide technical, economic and environmental challenges for glass manufacturing firms. Once the importance weight of risk parameters in Risk Matrix’ are determined, the risk factors are assessed performing IVIF-TOPSIS method during glass manufacturing. In order to verify the validity and stability of the proposed risk assessment model, sensitivity and comparative analysis are accomplished at the end of the study.


2018 ◽  
Vol 141 ◽  
pp. 289-304 ◽  
Author(s):  
Richard J. Wenning ◽  
Hilary Robinson ◽  
Michael Bock ◽  
Mary Ann Rempel-Hester ◽  
William Gardiner

2013 ◽  
Vol 345 ◽  
pp. 368-371
Author(s):  
Rui Qing Wang ◽  
Zi Qian Xiao

The restructuring/deregulation in electric power industry has heightened the importance of risk assessment. A model to estimate value-at-risk via GARCHSK specification is proposed, in which the seasonalities, heteroscedasticities, skewnesses, kurtosises and relationship to system loads are jointly addressed. The impacts of probability distribution assumption for innovations on value-at-risk estimate validation are analyzed for three distributions: normal, student-t and Gram-Charlier series expansion of the normal density function. The numerical example shows that the proposed model performs better in predicting one-period-ahead VaR.


Sign in / Sign up

Export Citation Format

Share Document