A Scalable Geospatial Data-Driven Localization Approach for Modeling of Low Voltage Distribution Networks and Low Carbon Technology Impact Assessment

The electrification of heat and transport through the uptake of low carbon technologies (LCTs) is expected to pose significant planning and management challenges for distribution network operators (DNOs) in the coming decades. Therefore, to support investment decision making there is a requirement to understand the impact LCTs will have on low voltage (LV) distribution network infrastructure across diverse geographical areas. However, LV networks are not only radically different in terms of topology and physical asset characteristics, but also in terms of the demand they serve which is sensitive to the diversity of local conditions such as climate, consumer demographic and building stock. As such, there is an increasing requirement to capture elements of this diversity in the development of LV network and LCT modeling approaches to better quantify place-based LCT impact and to inform the quantification of local area flexibility. In turn, using Python and OpenDSS, this work presents a novel scalable approach to localized LV network and LCT impact modeling by coupling two methodologies; a LV network model development methodology and a LCT impact assessment methodology which accounts for both the electrification of heat and transport with consideration for the diversity of residential heat demand. The methodology is demonstrated on LV networks in Scotland through quantification of LCT network impact against key network assessment metrics. The findings demonstrate the value in spatial and temporal high-resolution modeling at scale, emphasizing a need to consider the combined impact of electrified heat and transport in future network investment planning.