Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities (2303.03006v1)

Abstract: 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 and smart-city energy networks. This paper intends on bridging these disjointed scales to secure both resilient and more energy-efficient urban planning thanks to a holistic approach. The intent is to assess the aggregated impact of user behavior stochasticities on optimal urban energy planning. To this end, 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. To secure the scalability of our approach, an organic spatial problem distribution suitable for field deployment is put forth, validated by a proof of concept. Uncertainty factors affecting urban energy planning are particularly examined through a local sensitivity analysis, namely, economic, climate, and occupant-behavior uncertainties. Founded on historical measurements a typical Dutch energy community composed of 41 residential buildings is designed. Results disclose a fast-converging distributed stochastic problem, where boilers are showcased as the preferred heating utility, and distributed renewable energy and storage systems were identified as unprofitable for the community. Occupant behavior was 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.