Rapid Identification of Economic Indicators of Integrated Energy Systems Based on Data Analysis. (4th February 2022)

• Record Type: Journal Article
• Authors: He, Wei; Lu, Haicheng; Liu, Yingfang; Chen, Guang; Huang, Zishuo
• Other Names: Li Yang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-02-04
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/9180774
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : The construction of integrated energy systems has received widespread attention. In addition to system design and operating optimization, clarifying the internal relationship between the project's own characteristics and its economic indicators is also important for high-value project selection and implementation. Based on various energy prices and the climatic characteristics of typical cities, 60 integrated energy supply projects in 4 typical cities were used as a case study, and the cold and heat source system solutions with the shortest investment payback period for each project were calculated through a system optimization model. We calculated the correlation coefficient between the initial investment of system equipment, annual energy supply, annual energy sales revenue, annual equivalent full-load energy supply hours, initial equipment investment indicators for annual unit energy supply, and the project's static investment payback period. The key factors affecting the static investment payback period of the project were identified. The results show that, under the optimal system scheme, even for buildings of the same type in the same area, the economic indicators of different integrated energy projects are still quite different. Under the current design, operation, and operation conditions, load distribution is the decisive factor for the economic feasibility of the project. The static investment payback period of the project can be quickly calculated based on the equivalent full-load energy supply hours of the project throughout the year. Compared with the current numerical and system dynamics model for point-to-point single system optimizing modeling, the proposed statistical regression model can help reveal the main factors of integrated energy system's economically feasible.