Layout Methods for Integrated Energy Supply Service Stations from the Perspective of Combination Optimization

Integrated energy supply service stations (IES) are a new type of transportation energy infrastructure offering the advantages of comprehensive functions and intensive land use while providing more convenient and efficient energy supply services. Through the analysis of service station characteristics, this study regards the IES as a spatially superimposed combination of various energy supply services, proposes a layout method from the perspective of combination optimization, and establishes a station optimization model for energy supply stations. This method aims to further coordinate and optimize the combination of various energy supply stations to achieve global optimization of the energy supply service system. Finally, this study uses a hypothetical situation for example analysis to verify the validity and rationality of the method. The layout plan proposed in this study has important theoretical and practical significance for how to achieve the optimal layout of an IES.

[1]  David Dallinger,et al.  New business models for electric cars: A holistic approach , 2011 .

[2]  Jyrki Wallenius,et al.  Bibliometric Analysis of Multiple Criteria Decision Making/Multiattribute Utility Theory , 2008, MCDM.

[3]  Michal Pluhacek,et al.  Evolutionary algorithms dynamics and its hidden complex network structures , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[4]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Fan Zhang,et al.  Understanding temporal and spatial travel patterns of individual passengers by mining smart card data , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[6]  Saïd Hanafi,et al.  New convergent heuristics for 0-1 mixed integer programming , 2009, Eur. J. Oper. Res..

[7]  Mohsen Ramezani,et al.  Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach , 2017 .

[8]  Michael Kuby,et al.  An efficient formulation of the flow refueling location model for alternative-fuel stations , 2012 .

[9]  Nadarajah Mithulananthan,et al.  A comprehensive planning framework for electric vehicle charging infrastructure deployment in the power grid with enhanced voltage stability , 2015 .

[10]  Zhenbo Wang,et al.  The built environment and trip chaining behaviour revisited: The joint effects of the modifiable areal unit problem and tour purpose , 2019 .

[11]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[12]  Nathalie Bostel,et al.  A dynamic model for facility location in the design of complex supply chains , 2008 .

[13]  Liang Guan-min Based on Fuzzy Analytic Hierarchy Process of CNG Fueling Station Location Research , 2011 .

[14]  Michael Kuby,et al.  On the Way or Around the Corner? Observed Refueling Choices of Alternative-Fuel Drivers in Southern California , 2013 .

[15]  Kin K. Leung,et al.  Optimization-based resource allocation in communication networks , 2014, Comput. Networks.

[16]  F. Martina,et al.  Design for Additive Manufacturing , 2019 .

[17]  Florian Heiss,et al.  Discrete Choice Methods with Simulation , 2016 .

[18]  Anil Namdeo,et al.  Spatial planning of public charging points using multi-dimensional analysis of early adopters of electric vehicles for a city region , 2014 .

[19]  Tian Lan,et al.  Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies , 2014 .

[20]  Pandian Vasant,et al.  Optimisation of PHEV/EV charging infrastructures: a review , 2014 .

[21]  Rex K. Kincaid,et al.  P-median problems with edge reduction , 2014, 2014 Systems and Information Engineering Design Symposium (SIEDS).

[22]  Barrett W. Thomas,et al.  The stochastic p-hub center problem with service-level constraints , 2009, Comput. Oper. Res..

[23]  Khandker Nurul Habib,et al.  Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary , 2012 .

[24]  Mark Goh,et al.  Covering problems in facility location: A review , 2012, Comput. Ind. Eng..

[25]  Dusan Ramljak,et al.  Bee colony optimization for the p-center problem , 2011, Comput. Oper. Res..

[26]  Reza Ghodsi,et al.  Optimal Location of Compressed Natural Gas (CNG) Refueling Station Using the Arc Demand Coverage Model , 2010, 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation.

[27]  Changhyun Kwon,et al.  Multi-period planning for electric car charging station locations: A case of Korean Expressways , 2015, Eur. J. Oper. Res..

[28]  Nenad Mladenovic,et al.  Hybrid Variable Neighbourhood Decomposition Search for 0-1 Mixed Integer Programming Problem , 2010, Electron. Notes Discret. Math..

[29]  Martine Labbé,et al.  Solving Large p-Median Problems with a Radius Formulation , 2011, INFORMS J. Comput..

[30]  Pandian Vasant,et al.  Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures , 2016 .

[31]  Mazin Abed Mohammed,et al.  Solving vehicle routing problem by using improved genetic algorithm for optimal solution , 2017, J. Comput. Sci..

[32]  Michael Kuby,et al.  Optimization of hydrogen stations in Florida using the Flow-Refueling Location Model , 2009 .

[33]  Matteo Fischetti,et al.  Proximity search for 0-1 mixed-integer convex programming , 2014, J. Heuristics.