Distributed Optimization of Joint Seaport-All-Electric-Ships System under Polymorphic Network

Wenjia Xia, Qihe Shan (), Geyang Xiao (), Yonggang Tu and Yuan Liang
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Wenjia Xia: Maritime Big Data and Artificial Intelligent Application Centre, Navigation College, Dalian Maritime University, Dalian 116026, China
Qihe Shan: Maritime Big Data and Artificial Intelligent Application Centre, Navigation College, Dalian Maritime University, Dalian 116026, China
Geyang Xiao: Research Institute of Intelligent Networks, Zhejiang Lab, Hangzhou 311121, China
Yonggang Tu: Jiaxing Big Data Center, Jiaxing Municipal People’s Government, Jiaxing 314000, China
Yuan Liang: Research Institute of Intelligent Networks, Zhejiang Lab, Hangzhou 311121, China

Sustainability, 2022, vol. 14, issue 16, 1-14

Abstract: As a result of the trend towards auto intelligence and greening of vehicles and with the concept of polymorphic network being put forward, the power transmission mode between seaports and all-electric ships (AESs) is likely to be converted to “peer-to-peer” transmission. According to practical shore power systems and carbon trade mechanisms, an advanced peer-to-peer power dispatching model-joint seaport-AESs microgrid(MG) system has been proposed in the paper. The joint seaport–AES system model is proposed to minimize the total operational cost of power production and marketing, including distributed generation (DG) cost, electricity trading cost, and carbon emissions, and the boundary conditions are given as well. A parameter projection distributed optimization (PPDO) algorithm is utilized to solve the distributed optimization power operation planning of the proposed joint seaport–AES MG system under a polymorphic network and to guarantee the precision of power dispatching, which compensates for the insufficiency of the computing power. Finally, a case study of a five-node polymorphic joint seaport-AESs system is conducted. The feasibility of the parameter projection approach and the peer-to-peer power dispatching model are verified via the convergence of all the agents within the constraint sets.

Keywords: distributed optimization; power dispatching; port microgrid; AES; parameter projection; polymorphic network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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