Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Along with the growing inclusion of smart technologies into the electrical power grids, benefits, which can bex000D
originated from advanced metering infrastructure (AMI), have grabbed noticeable attention from distributionx000D
utilities. AMI is an architecture for automated, two-way communication between customers' meters and thex000D
utility; from the utility company point of view, AMI can provide real-time data of power consumption whichx000D
can be efficiently used for several tasks such as network monitoring. Saskatoon Light & Power (SL&P) whichx000D
is a distribution utility in Saskatchewan has been operating the AMI system since July 2016. As the number ofx000D
meters are severely ample in practical systems, SL&P, similar to other utilities, creates virtual meter data byx000D
aggregating loads served by distribution transformers. Although this process helps the operators to analyze thex000D
grid with ease, it sacrifices valuable information provided by the AMI and confines their applications to thex000D
billing process. Such an important deficiency can be considered as the main factor which motivates thisx000D
research program to delve more into the residential load profile modeling and find approaches which can yieldx000D
to the enhanced development of virtual meters. The long-term goal of collaboration between University ofx000D
Saskatchewan and SL&P is to find a novel framework that combines customer segmentation and virtual meterx000D
development processes together so that the end result not only can meet the operators expectations, but also canx000D
form a cutting-edge knowledge motivating other researchers to focus on this problem. To achieve this ultimatex000D
goal, the short-term goals which triggers starting a collaboration in terms of an engage grant are: (i) to assessx000D
current probabilistic methods for modeling residential load profiles and select a method which can address thex000D
issue of highly volatile characteristics with respect to the practical needs; and (ii) to develop a framework forx000D
customer segmentation and virtual meter development based on both machine learning techniques and thex000D
probabilistic modeling conducted in (i). The outcomes of this research are expected to constitute milestones inx000D
power system monitoring, operation and control, and contribute to the development of a more reliable grid.