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Design optimization of a small-scale polygeneration energy system in different climate zones in Iran
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Heat and Power Technology.
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0002-4479-344X
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0001-9556-552X
2018 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 5, article id 1115Article in journal (Refereed) Published
Abstract [en]

Design and performance of polygeneration energy systems are highly influenced by several variables, including the climate zone, which can affect the load profile as well as the availability of renewable energy sources. To investigate the effects, in this study, the design of a polygeneration system for identical residential buildings that are located in three different climate zones in Iran has been investigated. To perform the study, a model has previously developed by the author is used. The performance of the polygeneration system in terms of energy, economy and environment were compared to each other. The results show significant energetic and environmental benefits of the implementation of polygeneration systems in Iran, especially in the building that is located in a hot climate, with a high cooling demand and a low heating demand. Optimal polygeneration system for an identical building has achieved a 27% carbon dioxide emission reduction in the cold climate, while this value is around 41% in the hot climate. However, when considering the price of electricity and gas in the current energy market in Iran, none of the systems are feasible and financial support mechanisms or other incentives are required to promote the application of decentralized polygeneration energy systems.

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 11, no 5, article id 1115
Keywords [en]
polygeneration system, climate zone, optimization, combined cooling, heating, and power generation (CCHP), renewable energy, particle swarm optimization (PSO) algorithm, Iran
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227689DOI: 10.3390/en11051115Scopus ID: 2-s2.0-85047081323OAI: oai:DiVA.org:kth-227689DiVA, id: diva2:1205192
Note

QC 20180530

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-30Bibliographically approved
In thesis
1. Small-Scale Decentralized Energy Systems: optimization and performance analysis
Open this publication in new window or tab >>Small-Scale Decentralized Energy Systems: optimization and performance analysis
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Small-scale polygeneration energy systems, providing multiple energy services, such as heating, electricity, cooling, and clean water, using multiple energy sources (renewable and non-renewable) are considered an important component in the energy transition movement. Exploiting locally available energy sources and providing energy services close to the end users have potential environmental, economic, and societal benefits. Furthermore, integration of thermal and electro-chemical storages in the system can decrease fossil fuel consumption, particularly when applying a long-term perspective.

Despite their promising potential, the global share of power generation by these systems, including the combined heat and power (CHP) systems, is relatively low in the current energy market. To investigate the applicability of these systems, their competitiveness in comparison with conventional energy solutions should be carefully analyzed in terms of energy, economy, and the environment. However, determining whether the implementation of a polygeneration system fulfills economic, energetic, and environmental criteria is a challenging process. Additionally, the design of such systems is a complex task, due to a system design with various generation and storage modules, and the continuous interaction between the modules, load demand fluctuations, and the intermittent nature of renewable energy sources.

In this research study, a method to identify the optimal size for small-scale polygeneration systems and suitable operating strategies is proposed. Based on this method, a mathematical model is developed that can optimize the design in terms of energy, economy, and the environment relative to a reference system for a given application. Moreover, the developed model is used to investigate the effects of various parameters on the performance of the system, including, among others, the selected operating strategy and load characteristics as well the climate zones through a number of case studies. It is concluded that the application of a small-scale polygeneration energy system potentially has considerable energetic and environmental benefits. However, its economic feasibility varies from case to case. The concluding remarks are primarily intended to provide a general perception of the potential application of a polygeneration system as an alternative solution. It also provides a general understanding of the effects of various parameters on the design and performance of a complex polygeneration system.

The results from various case studies demonstrate that the developed model can efficiently identify the optimal size of a polygeneration system and its performance relative to a reference system. This can support engineers and researchers as well as investors and other decision makers to realize whether a polygeneration system is a good choice for a specific case.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 137
Series
TRITA-ITM-AVL ; 2018:20
Keywords
Small-scale polygeneration energy systems, techno-economic optimization, renewable energy, operating strategy, particle swarm optimization, optimization algorithm, decentralized energy system
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-228078 (URN)978-91-7729-808-3 (ISBN)
Public defence
2018-06-07, Kollegiesalen, Brinellvägen 8, Stockholm, 14:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-05-17 Last updated: 2018-05-18Bibliographically approved

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