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System-Wide Impacts of Distributed Generation on Power System Operation

Data-Driven Approaches to Addressing the Challenges of Integrating Distributed Generation

Time: Fri 2022-02-11 10.00

Location: Sten Veler, Teknikringen 33

Video link:

Language: English

Subject area: Electrical Engineering

Doctoral student: Tin Rabuzin , Elkraftteknik

Opponent: Associate Professor Marjan Popov, Delft University of Technology

Supervisor: Lars Nordström, Elkraftteknik

QC 20220117


Deployment of distributed generation (DG) is happening at an increasing rate driven by both the need for the reduction of humanity's carbon footprint and economic opportunities. The trend of increasing penetration levels of DG has also introduced interdependence between the responsibilities of system operators and the owners of the generating units. To fully accommodate distributed generation in the system, these interdependencies and the accompanying operational challenges need to be addressed. At the same time, the development of information and communication technologies within power systems has enabled access to immense information collected from the grids, such as the wide-area synchrophasor measurements. To utilize the increasing amounts of collected information and address the challenges of DG integration, novel applications can be developed by using data-driven methodologies.  This thesis aims to investigate if such data-driven approaches are sufficiently reliable, timely and accurate to address the DG integration challenges appearing across the responsibility areas. 

To explore the capabilities of data-driven approaches, two impacts of DG were studied in this thesis. First, different types of islanding detection methods were proposed. The data-driven methods for computing sensitivity parameters as seen from a distribution-level generator were developed and integrated within a proposed local islanding detection method. It was shown that such estimation methods enable a reliable and timely islanding detection by the use of field measurements. Next, synchrophasor-based remote islanding detection methods were developed as a tool for the situational awareness of system operators. It was shown that the dimensionality of the voltage angle measurements can be utilized to distinguish between islanding and other types of events using a data-driven approach. 

The other impact of DG that was addressed is the modelling of distribution networks in dynamic studies of a transmission network. A large number of existing approaches to dynamic modelling were identified pointing to the need for a model structure selection procedure. Thus, a data-driven modelling approach that selects an optimal model structure and evaluates the uncertainty of the models' outputs was proposed. The models' uncertainty was then used to select only informative events for parameter identification and to thereby reduce its computational burden. The performance of the method was demonstrated by using it to derive an optimal equivalent model of a modified CIGRE network.

Through the aforementioned contributions, the thesis shows that the increasing observability of the systems enables data-driven methodologies to address the challenges of DG integration.