Reference number : GC-2017-Q2-04266, amendment number : , agreement type : G, recipient legal name : Chen, Yu (Christine) (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Across North America, phasor measurement units (PMUs) and smart meters have been extensively deployed_x000D_
across the electric grid. The volume and variety of the data collected from these devices have the potential to_x000D_
enable a broad array of power system monitoring and operations tasks. Specifically, this project will leverage_x000D_
measurement data collected from PMUs and smart meters to improve [T1] real-time transient stability_x000D_
assessment, and [T2] online learning for demand response._x000D_
Transient stability will be an important concern in the future power system, primarily due to rapidly varying_x000D_
renewable generation, which is expected to gradually but widely displace fossil-fuel-based technologies._x000D_
Data-centric approaches based on machine learning have been shown to predict whether or not the system will_x000D_
be stable with high accuracy. In [T1], along with our industry partner, we will extend existing tools to also_x000D_
identify the root cause of instability, so that electric utilities can repair damages and restore normal system_x000D_
operations in a timely manner._x000D_
Demand response programs help to improve power system reliability and market efficiency, which can be_x000D_
achieved by incentivizing customers via real-time pricing to shift their electricity usage away from periods of_x000D_
peak demand. Here, a major challenge is in predicting human behaviours, which can be solved via online_x000D_
learning algorithms, but they generally neglect electrical network effects. In [T2], we will investigate the_x000D_
impact of the electric network, which imposes nontrivial physical and operational constraints, on real-time_x000D_
implementations of demand response strategies._x000D_
In close collaboration with our industry partner, the proposed tools will be prototyped and implemented into_x000D_
their commercial-grade software to extend its applicability to energy data analytics. By leveraging data_x000D_
collected across the electric grid, the project outcomes help to ensure power availability and quality in the face_x000D_
of growing uncertainty arising from high levels of renewable penetration and customer participation., program purpose french : Across North America, phasor measurement units (PMUs) and smart meters have been extensively deployed_x000D_
across the electric grid. The volume and variety of the data collected from these devices have the potential to_x000D_
enable a broad array of power system monitoring and operations tasks. Specifically, this project will leverage_x000D_
measurement data collected from PMUs and smart meters to improve [T1] real-time transient stability_x000D_
assessment, and [T2] online learning for demand response._x000D_
Transient stability will be an important concern in the future power system, primarily due to rapidly varying_x000D_
renewable generation, which is expected to gradually but widely displace fossil-fuel-based technologies._x000D_
Data-centric approaches based on machine learning have been shown to predict whether or not the system will_x000D_
be stable with high accuracy. In [T1], along with our industry partner, we will extend existing tools to also_x000D_
identify the root cause of instability, so that electric utilities can repair damages and restore normal system_x000D_
operations in a timely manner._x000D_
Demand response programs help to improve power system reliability and market efficiency, which can be_x000D_
achieved by incentivizing customers via real-time pricing to shift their electricity usage away from periods of_x000D_
peak demand. Here, a major challenge is in predicting human behaviours, which can be solved via online_x000D_
learning algorithms, but they generally neglect electrical network effects. In [T2], we will investigate the_x000D_
impact of the electric network, which imposes nontrivial physical and operational constraints, on real-time_x000D_
implementations of demand response strategies._x000D_
In close collaboration with our industry partner, the proposed tools will be prototyped and implemented into_x000D_
their commercial-grade software to extend its applicability to energy data analytics. By leveraging data_x000D_
collected across the electric grid, the project outcomes help to ensure power availability and quality in the face_x000D_
of growing uncertainty arising from high levels of renewable penetration and customer participation., agreement title english : Energy Data Analytics for Reliable and Efficient Electric Grid Operations, agreement title french : Energy Data Analytics for Reliable and Efficient Electric Grid Operations, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-09-20, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada