An improved affine arithmetic‐based optimization method for district energy systems with uncertainties. Issue 3 (9th December 2022)

• Record Type: Journal Article
• Authors: Liu, Tianjie; Jiao, Wenling; Tian, Xinghao; Zhang, Xin
• Is Part Of: Energy science & engineering. Volume 11:Issue 3(2023)
• Page Start: 1080
• Page End: 1091
• Publication Date: 2022-12-09
• Subjects: affine arithmetic -- district energy system -- interval linear programming -- uncertain parameters; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.1369
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: In the process of the district energy system (DES) operation and dispatching, there are various uncertainties brought by the strong coupling of equipment and the fluctuation of internal and external surroundings, and these uncertain parameters are often correlated. In this paper, an improved affine arithmetic‐based method is proposed to solve the economic dispatching problem of DES with correlated uncertainties. First, an optimal dispatching model considering economic and environmental factors is constructed. An affine algorithm is introduced and interval arithmetic is used to improve the problem of interval extension and high‐order noise element. Taking a classical DES system as an example, the optimal dispatching of the system is analyzed using deterministic method, interval method, and proposed method. The application effect of the proposed method under different uncertainties scale and amplitude is also discussed. The results show that the proposed method can effectively reduce the width of target value interval compared with the traditional interval optimization method, which can further contribute to the optimal operation decision. Abstract : The paper represents an improved affine arithmetic‐based method to solve the economic dispatching problem of district energy systems. The proposed method considers the uncertainty overall process and the correlation of variables simultaneously