Impact of typical demand day selection on CCHP operational optimization

Due to high variability of energy demand in a whole year, optimizing the configuration and operation of a CCHP system will take very high and unfeasible computational time expenses. To overcome this problem, this paper presents a new and creative method to get few representative days that adequately...

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Bibliographic Details
Published in:Energy procedia 2018-01, Vol.152, p.39-44
Main Authors: Gao, Yuan, Liu, Qianying, Wang, Shuxia, Ruan, Yingjun
Format: Article
Language:English
Subjects:
CCHP system
k-means clustering algorithm
MILP
typical demand days
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Summary:Due to high variability of energy demand in a whole year, optimizing the configuration and operation of a CCHP system will take very high and unfeasible computational time expenses. To overcome this problem, this paper presents a new and creative method to get few representative days that adequately preserve significant demand characteristics. Typical demand days are selected based on the use of k-means clustering algorithm and average method. A hypothetical CCHP system is optimized with mixed-integer linear programming algorithm (MILP) in order to confirm the selection of typical days in this paper. This paper also imitates traditional method. A case study of a Qingdao office building is discussed to demonstrate the proposed method. The results illustrate that the magnitude of Mean Absolute Percentage Errors (MAPE) between actual demand load and typical days load can affect actual operational effect. In conclusion, optimal number of typical days for actual CCHP operation can obtain very low MAPE and lowest annual total cost.
ISSN:1876-6102
1876-6102
DOI:10.1016/j.egypro.2018.09.056