The disparate nature of data for electric power utilities complicates the emergency recovery and response process.The reduced efficiency of response to natural hazards and disasters can extend the time that electrical...The disparate nature of data for electric power utilities complicates the emergency recovery and response process.The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads,and in extreme events,leave the public without power for extended periods.This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology,population,and electric distribution line reliability indices into a unified,cloud-based,serverless framework that supports power system operations in response to extreme events.An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency,which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events.This technology was developed initially for Puerto Rico,following extreme hurricane and earthquake events in 2017 and 2020,but is applicable to utilities around the world.Given the highly abstract and modular design approach,this technology is equally applicable to any geographic region and similar natural hazard events.In addition to a review of the requirements,development,and deployment of this framework,technical aspects related to application performance and response time are highlighted.展开更多
基金supported by the United States Department of Energy,Office of Energy Efficiency and Renewable Energy,Solar Energy Technology Program。
文摘The disparate nature of data for electric power utilities complicates the emergency recovery and response process.The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads,and in extreme events,leave the public without power for extended periods.This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology,population,and electric distribution line reliability indices into a unified,cloud-based,serverless framework that supports power system operations in response to extreme events.An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency,which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events.This technology was developed initially for Puerto Rico,following extreme hurricane and earthquake events in 2017 and 2020,but is applicable to utilities around the world.Given the highly abstract and modular design approach,this technology is equally applicable to any geographic region and similar natural hazard events.In addition to a review of the requirements,development,and deployment of this framework,technical aspects related to application performance and response time are highlighted.
