Collaborative planning of spatial layouts of distributed energy stations and networks: A case study. (1st November 2021)

• Record Type: Journal Article
• Authors: Zhou, Yuan; Ma, Yanpeng; Wang, Jiangjiang; Lu, Shuaikang
• Is Part Of: Energy. Volume 234(2021)
• Publication Date: 2021-11-01
• Subjects: Distributed energy system -- Energy station -- Energy networks layout -- Economic optimization -- Collaborative optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.121205
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The spatial layout of energy stations and networks is important for the implementation of regional distributed energy systems (RDES). The existing literatures mainly employed the shortest path algorithm to find the optimal layouts, which cannot fully consider the difference and complementarity between energy users. This paper employs graph theory to transform the spatial optimization into an optimization problem of searching the optimal sub-graph from the abstract graph in the studied region and solved by genetic algorithm (GA), in which the uncertainties of users loads are integrated to the optimization model. Besides, hybrid coding is introduced in GA to simultaneously optimizes the siting of energy stations and network distributions. Therefore, collaborative optimization is realized to transform such traditional two-stage optimization to single-stage optimization. The characteristics of energy consumers, geography, and network distance between consumers and stations are appropriately handled by combining collaborative optimizing and graphic theory, leading this work to high practicability and helping investors put RDES into practice. Different constraints in the case study are compared to verify the feasibility and reliability of the proposed method. The influences of load budget uncertainty on the optimization results are discussed. The results demonstrate that the proposed optimization method reduces the total annual cost and network cost by 12.3% and 4.3% compared to the shortest path algorithm, respectively. Highlights: A collaborative plan method of distributed energy stations and networks is proposed. The economic model of stations and networks considering investment and operation cost is constructed. Uncertainties of loads are considered in optimization model to indicate their influences. The hybrid coding method in genetic algorithm is developed to realize the integrated optimization. The proposed method achieves the cost saving of 12.3% in comparison to the shortest path algorithm.