Energy storage enabling renewable energy communities: An urban context-aware approach and case study using agent-based modeling and optimization

Studies on energy storage as an enabler of renewable energy communities have largely ignored the influence of urban built context on its performance improvement potential. This paper thus presents a systematic approach that incorporates features of built form and function, using an agent-based model of urban energy demand and supply, in the performance analysis of urban energy communities integrating energy storage. It employs rule-based simulation and cost optimization models for storage (of single or hybrid type) sizing and operations, and analyzes the resulting performance through techno-economic metrics. A case study evaluated energy storage and performance outcomes for three urban built types (i.e., large low-rise, compact low-rise, and compact mid-rise areas) with different proportions of commercial and residential buildings in a warm climate, and considered two popular energy storage technologies, namely Li-ion batteries and reversible solid-oxide fuel cells (SOFC-RFC). Large low-rise and residential low-rise areas, harnessing their solar energy potential, increased their self-sufficiency to above 90 % and 65 % respectively through Li-ion batteries while retaining annual energy cost saving margins above 15 %. Mid-rise areas attained 100 % self-consumption through Li-ion batteries or SOFC-RFC but with minimal change in self-sufficiency. The findings are critical for urban context-specific policies and plans for energy storage deployment.