Data driven methods for DSO smart grid operation in the context of flexible energy systems

The pathways to decarbonize the energy sector is not only changing the ways in which we consume and produce electricity, but the entire power system at large. As generation is moved to the edge of the grid, while, among other things, electrification of transport and heating leads to changes in load behaviour, new situations arise in the power distribution systems. The increasing levels of intermittent distributed generation (DG) and the changing demand might stress the distribution grids, which highlights the need for new methods to operate power distribution systems.

For low voltage (LV) grids in the distribution systems, the observability is typically zero, thus implying no ability to respond to grid issues that might arise from the more volatile and stochastic generation and demand. Meanwhile, grid equipment, such as transformers, are more often loaded above their rated capacities. Rated limits are conventionally static, while the actual loading capability is dynamic due to seasonal (daily, yearly, etc.) variations in the operating environment. Thereby, static ratings leave unused capacity in the grid.

In this thesis, focus is given to developing new methods for online monitoring and forecasting of both grid and grid equipment states during operation. More specifically, a per phase node voltage estimation method is developed for unbalanced radial LV grids, proven to have reasonable accuracy with root mean squared errors ranging from 0.002 – 0.0004 p.u. depending on the node. A transformer thermal model is further developed for the application of dynamic transformer rating, proven to be capable of providing 6-hour forecasts.

While the mentioned models address real-time operation tools to gain information about the operating conditions, the thesis further develops an operational framework to solve emerging grid issues. The Smart Energy-Operating System (SE-OS) offers a platform to request ancillary services provided through flexible resources, and the distribution system operator (DSO) framework is derived from these principles.

The suggested operational framework involves ancillary services provided by an aggregator, energy communities and battery energy storage systems. To support the coordination of flexibility based ancillary services, a price elasticity model for aggregators is developed. The method enables analysis and evaluation of the capability of flexible resources to provide the ancillary services needed by the DSO.

The operational framework further incorporates data-driven online monitoring and forecasting tools, for which the developed methods are intended. This enables a shift in DSO operation strategy from the traditionally passive to adaptive operation.