Asset Details
Do you wish to reserve the book?
Inherent Risk Analysis of Power Supply Management: Case of Belize’s System Operator and Third-Party Actors
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Inherent Risk Analysis of Power Supply Management: Case of Belize’s System Operator and Third-Party Actors
Do you wish to request the book?
Inherent Risk Analysis of Power Supply Management: Case of Belize’s System Operator and Third-Party Actors
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Inherent Risk Analysis of Power Supply Management: Case of Belize’s System Operator and Third-Party Actors
2025
Overview
System operators (SOs) manage power supply, focusing on risk management. In small emerging economies, proactive risk management is crucial as major disruptions require SOs to redirect resources into recovery efforts. Therefore, SOs prioritize risk reduction, proactively minimizing the possibility of major disruption to ensure the industry’s long-term advancement. However, SOs frequently focus on residual risk mitigation while ignoring their exposure to inherent risk. This study investigated the inherent risks associated with power supply management using the SO’s operations and pertinent third parties. It used a seasonal multivariate strategy to identify risk factors, create univariate distribution models, and generate multivariate distributions using the copula method. Joint risk exposure was calculated using different percentile metrics for each scenario, allowing for a comparison of exposure levels. The study found that risk variables can sometimes reinforce or offset each other, impacting exposure behaviour. Exposure levels indicate periods of increased or decreased exposure to risk variables. Copula-modelled interdependencies captured larger exposure levels but had lower unit likelihoods, presenting less conservative exposure forecasts for SO managers. Case 1 exhibited the highest exposure levels in the early dry season (0.237 and 0.179), while case 2 showed peak exposure levels in the late wet season (1.009 and 0.948), along with cases 3 (0.977 and 0.908) and 4 (0.950 and 0.879).
Publisher
MDPI AG
Subject
/ Methods
