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Multi-objective optimization of combined cooling, heating and power system considering the collaboration of thermal energy storage with load uncertainties
•Build the multi-objective optimization model of CCHP system.•Optimize collocation of energy storage with CCHP system under uncertain loads.•Combine the design space and chance-constrained method to get sizing curves.•Obtain the comprehensive performance of economy, emission and reliability.•Present...
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Published in: | Journal of energy storage 2021-08, Vol.40, p.102819, Article 102819 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Build the multi-objective optimization model of CCHP system.•Optimize collocation of energy storage with CCHP system under uncertain loads.•Combine the design space and chance-constrained method to get sizing curves.•Obtain the comprehensive performance of economy, emission and reliability.•Present sensitive analysis on parameters affecting system performance.
A thermoelectric coupling limitation imposes the combined cooling, heating, and power (CCHP) systems low performance under dynamic uncertainty. Energy storage is an effective way to address the problem. This paper integrates an energy storage device into the CCHP system to decouple the limitation and proposes a multi-objective optimization model considering load uncertainty for the optimal capacity of energy storage. The design space is a set of all feasible configuration schemes that meet the requirements of the system’s confidence level. A chance-constrained method is employed to address the load uncertainty and transform the uncertain optimization into deterministic optimization. Load uncertainty is closely related to the given confidence level. Therefore, the indicator loss of load expectation is used to evaluate the reliability of the CCHP system. The economic and environmental performance of the CCHP system is assessed by using the annual total cost and carbon dioxide emission, respectively. A hotel example validates the feasibility of the proposed methodology. The results show that increasing the confidence level from 0.5 to 0.99 results in the capacity increase of the waste heat boiler from 326.8 kW to 408.2 kW in the heating mode. The increased change reduces the loss of load expectation by 108.4% and increases the annual operation cost by 110.14%. |
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ISSN: | 2352-152X 2352-1538 |
DOI: | 10.1016/j.est.2021.102819 |