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A pricing-based location model for deploying a hydrogen fueling station network
Affiliation:1. Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan 4111, QLD, Australia;2. Dept. of Botany & Microbiology College of Science King Saud University, P.O Box 2455, Riyadh, 11451, Saudi Arabia;3. Escola Técnica Superior dÉnginyeria Industrial de Barcelona (ETSEIB), Universitat Politécnica de Catalunya (UPC), Av. Diagonal, 647, 08028, Barcelonaa, Spain;4. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61519, Egypt;5. Department of Electrical Engineering, Fuzhou University, Fuzhou, 350116, China;1. Chemical Engineering Department, Universitas Indonesia, Depok, 16424, Indonesia;2. Fraunhofer Institute for Systems and Innovation Research ISI, Breslauer Strasse 48, 76139, Karlsruhe, Germany;3. Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA, 94720, United States;4. PwC Strategy & (Germany) GmbH, Kapelle-Ufer 4, 10117 Berlin, Germany;1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing, 102206, China;2. School of Economics and Management, North China Electric Power University, Beijing, 102206, China;3. School of Electrical & Electronic Engineering, North China Electric Power University, Beijing, 102206, China;4. Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing, 102206, China;1. Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1Kagamiyama, Higashi-Hiroshima, 739-8527, Japan;2. Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501, Japan
Abstract:We propose an innovative two-step Pricing-Based Location strategy for the rollout of new hydrogen fueling stations. A first model maximizes the profit of a new station with a price p1 which corresponds to a design capacity supplying a given market share (n1 customers). According to these findings and with the objective of deploying an extensive network, a second model searches for a suitable location as remote as possible from existing competitors, but as close as possible to just n1 demand locations. This problem is solved by an agent-based model integrating the Particle Swarm Optimization metaheuristic and a Geographic Information System representing the geospatial distribution of customer demand. We apply this model to the city of Paris by locating additional stations across the city one by one to supply a growing captive fleet of taxis and other transport operators in the future.
Keywords:Hydrogen car  Filling station  Location problem  Price optimization  Agent-based model  Particle Swarm Optimization
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