Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities.

To meet carbon emission reduction goals in line with the Paris agreement, planning resilient and sustainable energy systems has never been more important. In the building sector, particularly, strategic urban energy planning engenders large optimization problems across multiple spatiotemporal scales leading to necessary system scope simplifications. This has resulted in disconnected system scales, namely, building occupants (bottom layer) and smart-city energy networks (top layer). This paper intends on bridging these disjointed scales to secure both resilient and more energy-efficient urban planning. To assess the aggregated impact of user behavior stochasticities on optimal urban energy planning, a stochastic energy community sizing and operation problem is designed, encompassing multi-level utilities founded on energy hub concepts for improved energy and carbon emission efficiencies. The problem is solved through an organic spatial problem distribution suitable for field deployment, validated by a proof of concept. We examine uncertainty factors affecting urban energy planning through a local sensitivity analysis, namely, economic, climate, and occupant-behavior uncertainties. Founded on this modeling setup, an energy community of 41 Dutch residential buildings is optimally designed using historical measurements. Results disclose a fast-converging distributed stochastic problem, showcasing boilers as the preferred heating utility. Distributed renewable energy and storage systems are identified as unprofitable for the community. Occupant behavior is particularly exposed as the leading uncertainty factor impacting energy community planning. This demonstrates the relevance and value of our approach in connecting occupants to cities for improved, and more resilient, urban energy planning strategies. • Stochastic optimization for resilient planning of energy communities. • Uncertainty factor impact assessment on optimal system design through local sensitivity analysis. • Scalable urban planning, and control-oriented real-world deployment readiness through a distributed optimization approach. • Case study investigation encompassing 41 Dutch residential buildings. • Results reveal occupant behavior to be the leading uncertainty factor affecting the system design, behind climate and economic conditions. [ABSTRACT FROM AUTHOR]