A graph-theory-based dynamic programming planning method for distributed energy system planning: Campus area as a case study. (1st January 2023)

• Record Type: Journal Article
• Authors: Ding, Yan; Wang, Qiaochu; Tian, Zhe; Lyu, Yacong; Li, Feng; Yan, Zhe; Xia, Xi
• Is Part Of: Applied energy. Volume 329(2023)
• Publication Date: 2023-01-01
• Subjects: Distributed energy system -- Energy station site selection -- Pipeline network layout deployment -- Graph theory -- Dynamic programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120258
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A graph theory based planning method is proposed for distributed energy systems. Site selection of energy stations can be determined by kernel density analysis. Load fluctuation rate of each energy station is minimized by dynamic programming. Improved Prim algorithm proposed reduce the total pressure loss of pipeline network. Abstract: Distributed energy systems are widely used in current regional energy planning because of their flexibility in terms of energy supply. However, the differentiated demand for various building loads increases the uncertainty of the energy supply. To reduce load fluctuation and hydraulic imbalance, a dynamic programming method based on graph theory was proposed in this study for energy station site selection and pipeline network layout deployment. The kernel density method was applied to distribute the regional building load for the site selection of energy stations. With the minimum load fluctuation rate as the goal, a 0–1 dynamic programming method was proposed to optimize the energy supply range of the energy station. Based on graph theory, an improved Prim algorithm was developed to determine the pipeline network layout. Taking a campus area as a case study, the proposed planning method was shown to reduce the initial investment, annual operating cost, and equivalent annual cost by 1.23%, 6.52%, and 5.04%, respectively. The optimized planning scheme not only balanced the load fluctuation in each energy station but also reduced the total pressure loss of the pipeline network by 19.86%.