Cost-based siting and sizing of energy stations and pipeline networks in integrated energy system. (1st May 2021)

• Record Type: Journal Article
• Authors: Wang, Yongli; Wang, Jingyan; Gao, Mingchen; Zhang, Danyang; Liu, Yang; Tan, Zhongfu; Zhu, Jinrong
• Is Part Of: Energy conversion and management. Volume 235(2021)
• Publication Date: 2021-05-01
• Subjects: Regional integrated energy system (RIES) -- Energy station (ES) -- Pipeline network (PN) -- Siting and sizing -- Algorithms -- Lowest annual cost (LAC); Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2021.113958
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A synergy planning methodology of energy stations and networks is proposed. The cost-based synergy planning model consists of power losses, siting and sizing, multiple constraints. The model uses a hybrid approach including Genetic Algorithm, Dijkstra Algorithm and Simulated Annealing. Four scenarios analyze the potential benefits of the methodology. The proposed hybrid approach is an efficient tool for siting and sizing energy stations and networks. Abstract: Siting and sizing of energy stations (ESs) and pipeline networks (PNs) significantly influence views on the economy of regional integrated energy system (RIES). In this paper, we present a synergy planning method for siting and sizing ESs and PNs. This study describes the basic structure of ESs and PNs in RIES, clearly points out influencing factors that should be considered in the planning model. Then, with flow velocity, pipe diameter, equipment capacity, and the location of ESs and PNs as variables, the cost-based function model is built based on many constraints. The Genetic Algorithm (GA) based heuristic algorithm and the PNs planning algorithm based on "Dijkstra Algorithm (DA) + Simulated Annealing (SA)" are used to solve the problem proposed in this paper. In the results and discussion, the proposed model and method are verified by a hypothetical region from OpenStreetMap. The economic changes are studied in a basic scenario and three comparison scenarios. The results show that synergy planning can save 293, 320 $ and reduce the load peak-valley difference rate by 3.24%. Moreover, optimizing the flow velocity can reduce 0–32.04% lowest annual cost (LAC).