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Modeling hybrid energy systems integrating heat pumps and district heating: A systematic review
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Sustainable Digitalisation Research Centre (SDRC).ORCID iD: 0000-0002-6887-2142
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Sustainable Digitalisation Research Centre (SDRC).ORCID iD: 0000-0003-0998-6585
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Sustainable Digitalisation Research Centre (SDRC).ORCID iD: 0000-0002-2763-8085
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Sustainable Digitalisation Research Centre (SDRC).ORCID iD: 0000-0003-0326-0556
2025 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 329, article id 115253Article, review/survey (Refereed) Published
Abstract [en]

Given the environmental impact and cost-efficiency challenges of the conventional central District Heating (DH) systems, there is a shift towards hybrid solutions. The demand for small-scale Heat Pumps (HPs), integral components of these systems, has surged due to their electrically driven, cost-effective operation, and potential to meet environmental goals. This paper conducts a systematic literature review by investigating and highlighting hybrid heating solutions and their role in decarbonizing the built environment. It compares and discusses the potential benefits and challenges of various hybrid HP-DH systems against conventional DH-only heating approaches. The study evaluates these systems based on economic, environmental, and energy efficiency aspects, and it explores the use of intelligent and AI-based algorithms. The results indicate that, from an economic perspective, the hybrid approach can potentially offer cost savings over the long term, considering factors such as initial investment and operating expenses. The findings of the reviewed works suggest that in a DH-HP configuration, an operational cost saving between 5% and 27%, and a CO2 reduction of up to 32.3% can be achieved without additional resources. Additionally, the environmental impact analysis indicates a significant decrease in greenhouse gas emissions, aligning with global efforts to mitigate global warming.

Place, publisher, year, edition, pages
Elsevier, 2025. Vol. 329, article id 115253
Keywords [en]
District heating, Heat pump, Hybrid energy system, Systematic literature review, Optimization, Building integrated, Artificial intelligence
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mau:diva-73328DOI: 10.1016/j.enbuild.2024.115253ISI: 001399280600001Scopus ID: 2-s2.0-85214089839OAI: oai:DiVA.org:mau-73328DiVA, id: diva2:1931733
Available from: 2025-01-27 Created: 2025-01-27 Last updated: 2025-12-17Bibliographically approved
In thesis
1. Hybrid energy system optimization: towards intelligent and sustainable heating control
Open this publication in new window or tab >>Hybrid energy system optimization: towards intelligent and sustainable heating control
2025 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As the on-site heating systems (such as heat pumps, renewable energy sources) are receiving more attention in terms of practical installation and academic research, the future of heating systems is shifting towards hybrid solutions. This doctoral research explores intelligent control strategies for integrating mainly district heating (DH) and heat pumps (HPs) in residential and commercial buildings. The study focuses on enhancing energy management through Model Predictive Control (MPC), a robust closed-loop optimization method, augmented with artificial intelligence (AI) and data-driven algorithms. While MPC requires accurate building models, AI integration enables adaptive learning from historical data, improving decision-making under uncertainty. A key innovation of this work is the multi-criteria optimization framework, which considers building occupant thermal comfort, environmental impact, and cost-efficiency. Despite growing interest in hybrid systems, the optimal integration of DH and HPs remains underexplored. This research aims to fill that gap by developing a trustworthy and intelligent control system validated using open datasets and real-world data. The outcome will support endusers and building managers in making informed energy decisions, contributing to sustainable and efficient urban energy systems.

Place, publisher, year, edition, pages
Malmö University Press, 2025. p. 27
Series
Studies in Computer Science ; 39
Keywords
Heat pump, District heating, Model predictive control, Multi-objective optimization, Gray-box modeling, Hybrid heating
National Category
Energy Engineering
Identifiers
urn:nbn:se:mau:diva-81082 (URN)10.24834/isbn.9789178777075 (DOI)978-91-7877-706-8 (ISBN)978-91-7877-707-5 (ISBN)
Presentation
2025-12-15, A0607, Niagara, Malmö University, Malmö, 13:15 (English)
Opponent
Supervisors
Note

Paper IV in dissertation as manuscript and not included in the fulltext online.

Available from: 2025-12-09 Created: 2025-12-09 Last updated: 2025-12-17Bibliographically approved

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Soleimani, AliDavidsson, PaulMalekian, RezaSpalazzese, Romina

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