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Planning and operation of parking lots considering system, traffic, and drivers behavioral model

Rahmani Andebili, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/TSMC.2018.2824122
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. The planning and operation problems of parking lots of plug-in electric vehicles (PEVs) are studied in this paper. Herein, each distribution company (DISCO) allocates the parking lots to the electrical feeders to minimize the power loss and expected energy not supplied of the system, and consequently minimize the total cost of the planning problem over the given time horizon. In addition, the generation company (GENCO) manages the charging time of PEVs parked in the parking lots to defer the more expensive and pollutant generation units, and as a result maximize its daily profit. In both planning and operation problems, the behavioral model of PEVs' drivers are modeled with respect to the value of incentive and their distance from the parking lots. To achieve the realistic results in the planning problem of each DISCO, several economic and technical factors including yearly inflation and interest rates, hourly and daily variations of the load demand, yearly load growth of the system, and yearly growth rate of PEVs' application are considered. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing algorithm and genetic algorithm, respectively. It is demonstrated that the behavioral model of drivers, their driving patterns, and even the type of PEVs can remarkably affect the outcomes of planning and operation problems. It is shown that the optimal allocation of parking lots can minimize every DISCO's planning cost and optimal charging management of PEVs can increase the GENCO's daily profit. © 2018 IEEE
  6. Keywords:
  7. Charging management ; Drivers' behavioral model ; Driving patterns ; Plug-in electric vehicle (PEV) ; Traffic and system-based parking lot allocation ; Behavioral research ; Electric power generation ; Genetic algorithms ; Plug-in electric vehicles ; Profitability ; Simulated annealing ; Traffic surveys ; Behavioral model ; Charging managements ; Driving pattern ; Parking lots ; Plug in Electric Vehicle (PEV) ; Electric power system planning
  8. Source: IEEE Transactions on Systems, Man, and Cybernetics: Systems ; Volume 49, Issue 9 , 2019 , Pages 1879-1892 ; 21682216 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8345201