Compact Model of Latent Heat Thermal Storage for Its Integration in Multi-Energy Systems
Featured Application Dynamic simulation of Latent Heat Thermal Storage at system level; optimization of control strategies in multi-energy systems; investigation of Demand Side Management strategies. Nowadays, flexibility through energy storage constitutes a key feature for the optimal management of...
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Veröffentlicht in: | Applied sciences 2020-12, Vol.10 (24), p.8970, Article 8970 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Featured Application
Dynamic simulation of Latent Heat Thermal Storage at system level; optimization of control strategies in multi-energy systems; investigation of Demand Side Management strategies.
Nowadays, flexibility through energy storage constitutes a key feature for the optimal management of energy systems. Concerning thermal energy, Latent Heat Thermal Storage (LHTS) units are characterized by a significantly higher energy density with respect to sensible storage systems. For this reason, they represent an interesting solution where limited space is available. Nevertheless, their market development is limited by engineering issues and, most importantly, by scarce knowledge about LHTS integration in existing energy systems. This study presents a new modeling approach to quickly characterize the dynamic behavior of an LHTS unit. The thermal power released or absorbed by a LHTS module is expressed only as a function of the current and the initial state of charge. The proposed model allows simulating even partial charge and discharge processes. Results are fairly accurate when compared to a 2D finite volume model, although the computational effort is considerably lower. Summarizing, the proposed model could be used to investigate optimal LHTS control strategies at the system level. In this paper, two relevant case studies are presented: (a) the reduction of the morning thermal power peak in District Heating systems; and (b) the optimal energy supply schedule in multi-energy systems. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app10248970 |