PhD position: Near real-time flexibility needs assessment and dynamic hosting capacity for distribution networks

KU Leuven boasts a rich tradition of education and research that dates back six centuries. The university’s basic research orientation has always been and will remain fundamental research. At the same time, the university remains vigilantly open to contemporary cultural, economic and industrial realities, as well as to the community’s needs and expectations. KU Leuven is currently by far the largest university in Belgium in terms of research funding and expenditure (EUR 455.1 million in 2015). KU Leuven conducts fundamental and applied research in all academic disciplines with a clear international orientation. In the Times Higher Education ranking KU Leuven is ranked as the 12th European university, while in the Reuters Top 100 of the World’s most innovative institutions, KU Leuven is listed as the first European university.
The ELECTA research group of the Department of Electrical Engineering (ESAT) of KU Leuven performs research on aspects of generation, transmission, distribution and rational use of electrical energy. Topics covered are smart grids, reliable power systems with high penetration of renewable energy sources, HVDC and LVDC, energy markets, integration of renewable energy sources on transmission and distribution. Six professors lead the group. They manage the research assisted by teams of senior researchers, post-doctoral researchers and about 50 PhD students.
EnergyVille is an association of the Flemish research institutes KU Leuven, VITO, imec and UHasselt in the field of sustainable energy and intelligent energy systems. EnergyVille brings research, development, training and industrial innovation together under one name, in close cooperation with local, regional and international partners. EnergyVille strives to become one of the top five European institutes in innovative energy research. For that reason, the center was embedded in big national and international networks from the start.

The evolution of distribution networks with greater amount of load and distributed generation uncertainty makes it challenging for the distribution system operators (DSOs) to operate the grid reliably. This energy transition demands developing decision support tools for the DSOs to use flexible resources while ensuring probable network congestion incidents and deteriorating power quality (power factor, voltage unbalance, etc.) events to be minimized. The traditional fit-and-forget approach for the distribution networks needs a major revamping.

This PhD will deal with short term congestion forecasting and flexibility needs quantification for the distribution networks of the future. The key direction of exploration during the PhD are:

(i) Flexibility needs assessment: Distribution system operators (DSO) need to plan flexible resources in order to avoid probable distribution network incidents (DNI). DNI encapsulates the thermal congestion in the line’s voltage violations at the nodes, and not complying with power quality standards detailed in EN 50160. From DSO's perspective, power factor, voltage, and current imbalance are of additional interest.

(ii) Dynamic hosting capacity: DSOs need to quantify the amount of headroom available for utilizing the grid infrastructure to the best possible manner. Stochastic framework would be developed for computationally efficient calculation of dynamic hosting capacity.

(iii) Dynamic operating envelopes (DOE): Risk-aware evaluation tools coupled with stochastic models would be utilized for DOEs using centralized optimal power flow or through decentralized local measurements. These DOEs are subsequently utilized for fulfilling local objectives of prosumers in the unbalanced distribution network.

The PhD candidate is expected to go beyond the state of the art of flexibility operation, congestion mitigation and headroom calculation for near real-time intervals, developing methods and workflows that compare centralized and decentralized techniques under different levels of distribution network observability. The result should be a workable representation of the networks, that can effectively be used by other engineers for operational purposes.

As such, we arelooking for a candidate with interest, and preferably background, inmathematical optimization (linear/convex/mixed integer), electricalpower system, stochastic modeling, statistical analysis and data science.

Genk

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