4.6 Article

Coupled Multinetwork Constrained Planning of Energy Supplying Facilities for Hybrid Hydrogen-Electric Vehicles

Journal

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Volume 58, Issue 2, Pages 2848-2862

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2021.3106875

Keywords

Planning; Hydrogen; Electric vehicle charging; Optimization; Renewable energy sources; Nickel; Batteries; Coupled multinetwork flow; energy-supplying facility; hybrid hydrogen-electric vehicle; renewable energy; many-objective optimization

Funding

  1. National Natural Science Foundation of China [62073148, U2066202]
  2. State Grid Corporation of China [U2066202]
  3. National Natural Science Foundation of Hubei Province [2019CFA005]

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This paper proposes a multi-network framework to consider the coupled flow constraints in HVSEF planning. By using a modified maximum covering location method and a multi-objective optimization model, the problem of HVSEF planning is successfully addressed.
Hybrid hydrogen-electric vehicles (H2EVs) are developing rapidly in recent years. Evidently, the planning of H2EVs energy-supplying facilities (HVESFs) is important. It involves the coupled power flow of power network (PN), mass flow of hydrogen network (HN), and traffic flow of traffic network (TN), simultaneously. These coupled flows would indeed challenge economic and secure performances of HVESFs planning, especially when uncertain renewable energy is introduced. Therefore, a multinetwork framework considering coupled flow constraints is proposed for the planning issue of HVESFs, involving all the PN, HN, and TN. Therein, we first propose a modified maximum covering location method, in order to satisfy the energy demand of H2EVs. On this basis, a many-objective optimization-based bi-level HVESFs planning model is developed. Then, we use a hybrid differential evolution-interior point algorithm to solve this model, and obtain the planning solutions. Simulation results verify the feasibility and effectiveness of the proposed planning model, which has better performances compared to the existing approach.

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