Coordinated planning of multi-area multi-energy systems by a novel routing algorithm based on random scenarios. (October 2021)

• Record Type: Journal Article
• Authors: Zhou, Nan; Xie, Da; Gu, Chenghong; Li, Guojie; Zhang, Luqing; Lou, Ziyang; Chen, Yang; Zhang, Yanchi; Chen, Aikang
• Is Part Of: International journal of electrical power & energy systems. Volume 131(2021)
• Publication Date: 2021-10
• Subjects: Multi-energy router -- Virtual multi-energy router -- Routing algorithm -- Multi-area multi-energy system -- MAMES planning; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107028
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Latin hypercube sampling builds operation scenarios for planning evenly. K-means clustering algorithm reduces the operation scenarios set for planning. A novel routing algorithm generates candidate set of integrated energy network. The proposed planning determines the energy network and energy components. Abstract: As an important Energy Internet technology, energy router (ER) can implement energy routing and information interconnection for the electrical grid within a certain geography range. However, multi-energy supply allocation between multi-energy routers (MERs) has to be resolved, which can connect isolated area networks and expand the scale of the energy Internet. In this paper, equipment capacity and network routing are planned for multi-area multi-energy system (MAMES), which integrates multiple MERs with a virtual multi-energy router (VMER). Firstly, based on the VMER, the concept of MAMES is proposed. Then, the correlation model of multi-energy networks in VMER is constructed, based on MER energy transmission conversion extension model and hybrid power flow model. And the novel routing algorithm is proposed to determine the energy candidate set of the integrated energy networks. During planning, the random scenario generation based on Latin hypercube sampling (LHS) and scenario reduction based on k-means clustering are applied. Finally, the simulation shows that the planning method based on the routing algorithm can effectively obtain the capacity of energy equipment inside MERs and the routing layout between MERs in the MAMES. Proposed routing algorithm and operation scenarios reduction greatly improve the efficiency of planning. Correlated planning helps improve energy efficiency, environment protection, and economic benefits of MAMES.