This thesis explores how accurate state estimation can support Distribution System Operators (DSO) in managing MV grids. As the energy transition drives increasing complexity in electricity networks, more precise and reliable data is necessary for planning and operational decisions. Although state estimation offers a way to enhance data, DSOs struggle to implement it. The main objective of this thesis is to develop a general strategy for placing measurements to achieve accurate state estimation using as few measurements as possible.

Multiple cases were designed to assess the impact of specific measurement placement. A reference ”perfect case” network load scenario was created, and the values were corrupted using normally distributed noise, with standard deviations reflecting the expected accuracy of each measurement type. These noisy measurements were fed into the PandaPower Weighted Least Squares (WLS) state estimation algorithm, implemented in Python. The resulting state estimates were compared to the perfect case values to evaluate the state estimator’s accuracy and the placement strategy’s effect.

The results show that Power Injection Measurements (PIMs) primarily improve accuracy at the node where they are placed. In contrast, Medium Voltage Measurement Units (MVMUs) offer broader improvements across the entire feeder where they are installed. One strategic measurement location was identified based on financial grounds. Results also indicate that improving the accuracy of an inaccurate node is possible without improving its measurement, but requires widespread deployment elsewhere, which is rarely justifiable economically. Retrofitting stations solely for measurement purposes is generally not considered worthwhile.

The main focus should be on identifying the substations with the poorest measurement accuracy, typically pseudo-measurements. As a consequence, overall pseudo-measurement accuracy will also improve. This makes it more likely that the state estimates will fall within predetermined limits. The number of measurements that need to be placed depends on this predetermined limit.