PhD position for distribution system state estimation to unlock flexible resources

As a direct consequence of multiple (inter)national and regional initiatives (e.g., EU Green Deal, RE Power EU), an increasing penetration of renewable energy and electrification is expected in the upcoming years. Mass integration of technologies such as electric vehicles (EVs), heat pumps (HPs), photovoltaics (PV) and energy storage (ES) is a driving factor in this transition and will impose severe challenges with respect to the power quality of the low voltage (LV) distribution grids. To guarantee a maximal rollout of these resources, it is of primary importance for all stakeholders to (i) model and understand their impact on the grid, (ii) estimate flexibility potential in building energy management systems, and (iii) develop real-time monitoring and control strategies for distribution grids. In this PhD project, work will be focused on the formulation of novel distribution system state estimation (DSSE) models, specifically for (real-time) flexibility allocation.

State estimation (SE) is a well-known monitoring technique that infers the most likely operating condition of a power network, using as input 1) noisy measurements and 2) a digital model of the network itself. SE is deployed in real time in transmission system control rooms, but its adoption in distribution networks is currently quite limited. Two of the bottlenecks that hinder the adoption of SE in distribution networks (which are not present in transmission networks) are (1) the scarcity of available measurements, and (2) the lack of digital network models of sufficient quality. As such, distribution system state estimation examples often require extensions to the “standard” transmission system approach, e.g., complementing the lack of measurements with “pseudo-measurements”, i.e. forecasts of non-monitored quantities.

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 lines 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. With new converter-based loads and distributed generation, a high level of harmonics is also crucial for DNI.

Furthermore, there are no one-size-fits-all DSSE solutions, as the success of different DSSE techniques are strongly dependent on the features of the network, such as topology network impedances, and the degree of unbalance. This PhD will address different aspects of improving the performance and applicability of DSSE keeping in mind the unlocking of the flexibility services opportunity from the PV, EV, HP and ES.