<![CDATA[Determining customer connection locations on a Low-Voltage Distribution Network is a critical stage in electricity distribution planning because it requires simultaneous consideration of spatial conditions and network technical capability. Manual evaluation processes are often time-consuming, depend heavily on the experience of field personnel, and may produce inconsistent recommendations. This study aims to develop a Spatial Decision Support System (SDSS) based on a Python Toolbox in ArcGIS Pro by integrating the Euclidean Distance method and multicriteria analysis to identify the most suitable candidate locations for customer connections on a Low-Voltage Distribution Network. This study employed a Research and Development (R&D) approach consisting of requirements analysis, system design, toolbox development, implementation, and testing. The Euclidean Distance method was applied to identify the five nearest distribution pole candidates from multiple distribution transformers as an initial screening stage. Each candidate was subsequently evaluated using multicriteria analysis based on transformer capacity, network reliability indices comprising the System Average Interruption Duration Index (SAIDI) and the System Average Interruption Frequency Index (SAIFI), transformer voltage conditions, and connection distance. The evaluated parameters were normalized, weighted according to their relative importance, and aggregated into a feasibility score to rank the candidate connection locations. The results demonstrate that the developed toolbox successfully automates candidate identification, technical evaluation, score calculation, and recommendation generation in a rapid, consistent, and objective manner. The integration of spatial analysis and multicriteria analysis within ArcGIS Pro improves evaluation efficiency compared with conventional manual procedures while providing technically reliable recommendations for selecting customer connection locations on Low-Voltage Distribution Networks.]]>
