Optimization of multi-energy storage in urban building clusters
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, i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025014975 |
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| author | Sameer Algburi Aymen Mohammed Hassan Falah Fakhruldeen Ibrahim Abdullah Israa Alhani Ali Khudhair Qusay Hassan Michael Ssebunya Feryal Ibrahim Jabbar |
| author_facet | Sameer Algburi Aymen Mohammed Hassan Falah Fakhruldeen Ibrahim Abdullah Israa Alhani Ali Khudhair Qusay Hassan Michael Ssebunya Feryal Ibrahim Jabbar |
| author_sort | Sameer Algburi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc3bd43a998b405aa3fbabf65b0e0ca4 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-bc3bd43a998b405aa3fbabf65b0e0ca42025-08-20T03:25:08ZengElsevierResults in Engineering2590-12302025-06-012610542710.1016/j.rineng.2025.105427Optimization of multi-energy storage in urban building clustersSameer Algburi0Aymen Mohammed1Hassan Falah Fakhruldeen2Ibrahim Abdullah3Israa Alhani4Ali Khudhair5Qusay Hassan6Michael Ssebunya7Feryal Ibrahim Jabbar8College of Engineering, Al-Kitab University, Kirkuk 36015, Iraq; Corresponding authors.Electrical Technical College, Al-Farahidi University, Baghdad, IraqComputer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, 10011, IraqComputer Science Department, Al-Turath University, Baghdad, IraqMazaya University College, IraqDepartment of Mechanical Engineering, University of Deyala, Deyala, IraqDepartment of Mechanical Engineering, University of Deyala, Deyala, IraqDepartment of Mechanical Engineering, University of Deyala, Deyala, Iraq; Corresponding authors.Medical Physics Department, College of Sciences, Al-Mustaqbal University, 51001, Babil, IraqA 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.http://www.sciencedirect.com/science/article/pii/S2590123025014975Energy managementStorage systemsOptimizationDemand responseEfficiency |
| spellingShingle | Sameer Algburi Aymen Mohammed Hassan Falah Fakhruldeen Ibrahim Abdullah Israa Alhani Ali Khudhair Qusay Hassan Michael Ssebunya Feryal Ibrahim Jabbar Optimization of multi-energy storage in urban building clusters Results in Engineering Energy management Storage systems Optimization Demand response Efficiency |
| title | Optimization of multi-energy storage in urban building clusters |
| title_full | Optimization of multi-energy storage in urban building clusters |
| title_fullStr | Optimization of multi-energy storage in urban building clusters |
| title_full_unstemmed | Optimization of multi-energy storage in urban building clusters |
| title_short | Optimization of multi-energy storage in urban building clusters |
| title_sort | optimization of multi energy storage in urban building clusters |
| topic | Energy management Storage systems Optimization Demand response Efficiency |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025014975 |
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