Optimized sampling strategy for load scenario generation in partially observable distribution grids

Due to the increased penetration of low-carbon technologies, low voltage (LV) distribution networks are expected to face frequent congestion and voltage violation issues in the near future. In order to identify network limitations and make grid reinforcement decisions, comprehensive visibility of the LV network is needed. Inherently, LV grids are not well monitored, and most of the time the load profiles of the customers are not known. In this context, we present a two-stage novel scenario generation technique that combines a weight coefficients learning strategy with advanced clustering techniques to accurately sample optimal load profiles from the historical data for all the LV customers. Clustering based on Gaussian mixture models is used to extrapolate a limited number of measured yearlong load profiles to all customers without load profiles. A weighted dynamic time warping distance metric is then employed to group identical daylong load profiles into similar sub-clusters. Because different meteorological factors such as temperature, humidity, cloud cover, etc. affect consumption patterns differently under different conditions, a continuous genetic algorithm was developed to model the individual impact of different weather attributes on customer demand using weight coefficients. In addition, an unsupervised nearest neighbor search is incorporated to remove outliers and to ensure a certain degree of consistency among the selected scenarios. The proposed technique is evaluated using proper scoring metrics and it performs 40% better than the widely used random sampling and produces 15% more accurate results than the cluster sampling approach.

Automated summary

Due to the increased penetration of low-carbon technologies, low voltage distribution networks are expected to face congestion and voltage violation issues. This paper proposes a novel scenario generation technique to accurately sample optimal load profiles for LV customers. The technique combines clustering and weight learning strategies, and is evaluated using proper scoring metrics, outperforming random sampling and cluster sampling approaches.