Optimization of multi-energy storage in urban building clusters - Sameer Algburi; Aymen Mohammed; Hassan Falah Fakhruldeen; Ibrahim Abdullah; Israa Alhani; Ali Khudhair; Qusay Hassan; Michael Ssebunya; Feryal Ibrahim Jabbar (Results in Engineering)
A data-driven optimization framework was developed to enhance energy performance in building clusters through multi-energy storage systems, combining electrical and thermal solutions. The approach used surrogate modeling, symbolic regression, and genetic programming to simulate energy consumption, integrate weather and tariff data, and refine storage strategies across varied building types. Applied to a cluster in Bismayah city, Iraq, the methodology evaluated HVAC configurations, façade designs, and thermal mass levels to tailor storage capacity recommendations. Results revealed a 38 % reduction in peak grid import and a 24.6 % drop in overall energy consumption when hybrid energy storage was implemented. A thermal energy storage tank capacity of 4651 kWh, coupled with a battery storage unit of 342 kWh, demonstrated a 35 % decrease in energy costs. Demand response participation increased by 45 % through strategic use of pre-cooling routines and temperature reset controls. TES-only configurations achieved energy usage of 16.3 kWh/m², while hybrid configurations further reduced to 8.7 kWh/m². Budget analyses showed that investments ranging from $2.6 million to $10.4 million proportionally enhanced system performance without over-sizing. The integration of Battery and Thermal Energy Storage with Phase Change Materials further supported passive thermal control, reducing HVAC reliance during peak hours.