Integrating Socio-Spatial Factors into Battery Placement Decision-Making

By Friso Schutten

Abstract
Neighborhood-based Battery Storage Systems (BESS) enhance grid resilience and support the integration of renewable energy in urban environments. Their physical location is crucial for forming efficient microgrids, yet current decision-making tools primarily focus on technical parameters, the social factors remain underexposed. This thesis investigates how integrating social criteria into spatial decision-making can improve BESS placement, leading to more equitable and efficient urban energy systems. A literature review assessed key technical parameters, while a second review examined social factors in energy planning. Based on these insights, a spatial clustering model using median-based normalization was developed in R to group neighborhoods, balancing social diversity with technical grid constraints. Results show that income, age, and ethnicity play a critical role in optimal BESS placement. Incorporating social factors enhances decision-making, complementing traditional models that focus on energy demand, grid capacity, and renewable generation potential. However, improvements in usability and statistical clustering methodology are needed. Future research should refine clustering techniques and develop an interactive tool to support policymakers in integrating social and technical factors more effectively.