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Optimum design of a hybrid PV-CSP-LPG microgrid with Particle Swarm Optimization technique
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0001-8510-2783
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0002-4479-344X
2016 (English)In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 109, p. 1031-1036Article in journal (Refereed) Published
Abstract [en]

Designing an energy system using multiple energy sources including renewables and providing multiple energy services (e.g. electricity, heating) can enhance the reliability and efficiency of the system while mitigating the environmental footprint. However, interaction among various components, variation of the energy demand profile, and local ambient conditions make design optimization a complex task, and suggesting that efficient simulation tools and optimization techniques can help designers to determine the best solutions within a reasonable timeframe and budget. Previous work on a dynamic microgrid simulation tool called "u-Grid" used an exhaustive search technique to find optimum configurations. However, the high computational cost of the exhaustive search was a motivation to explore alternative optimization methods to improve the optimization process and also to enhance search speed. In this paper Particle Swarm Optimization (PSO) has been presented as a global optimizer and incorporated within the problem context. Results from the exhaustive search have been used as a benchmark for testing and validation of the newly introduced optimization technique. The result shows that the PSO method is an efficient technique which has the ability to determine a high quality design solution for an optimized microgrid with a relatively low computational cost. Applying this PSO-based algorithm to the case study has reduced the total computation time a factor of about 6 in a significantly smaller computational platform.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 109, p. 1031-1036
Keywords [en]
Hybrid energy system, Polygeneration, Optimization, Particle Swarm Optimization, Microgrid
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-199979DOI: 10.1016/j.applthermaleng.2016.05.119ISI: 000386738600023Scopus ID: 2-s2.0-84992197068OAI: oai:DiVA.org:kth-199979DiVA, id: diva2:1071880
Funder
StandUp
Note

QC 20170206

Available from: 2017-02-06 Created: 2017-01-20 Last updated: 2018-05-17Bibliographically 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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Ghaem Sigarchian, SaraMalmquist, Anders

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