The growing use of renewable energy sources and sustainable technologies has increased the attractiveness of low-voltage DC distribution grids. Despite initial research and standardization work, the definition of power quality (PQ) issues and their related compatibility levels in DC grids and the related measurement techniques remain in their infancy. The lack of available measurement results obtained in real-world situations is a major shortcoming. In this paper, the results of a measurement campaign obtained in a configurable bipolar 350 V/700 V DC microgrid in an urban environment are presented. Voltage and current signals were registered continuously at different positions in the microgrid with a sampling rate of 500 kSa/s while changing the configuration of the microgrid; the resulting data were analyzed afterwards, with a focus on DC voltage and current variation, ripple, and spectral analysis. The measurements were taken with custom-designed metrology-sound measurement equipment. The measurement results provide input to the development of DC PQ analysis tools and the standardization of DC PQ measurement methods. Furthermore, the experience obtained will be beneficial for utility companies and regulatory authorities for reliable planning-level and compatibility-level surveys.

Many electric vehicles (EVs) are using today’s distribution grids, and their flexibility can be highly beneficial for the grid operators. This flexibility can be best exploited by DC power networks, as they allow charging and discharging without extra power electronics and transformation losses. From the grid control perspective, algorithms for planning EV charging are necessary. This paper studies the problem of EV charging planning under limited grid capacity and extends it to the partially observable case. We demonstrate how limited information about the EV locations in a grid may disrupt the operation planning in DC grids with tight constraints. We introduce two methods to change the grid topology such that partial observability of the EV locations is resolved. The suggested models are evaluated on the IEEE 16 bus system and multiple randomly generated grids with varying capacities. The experiments show that these methods efficiently solve the partially observable EV charging planning problem and offer a trade-off between computational time and performance.