Probabilistic planning of electric vehicles charging stations in an integrated electricity-transport system

•An integrated planning model for optimal placement of fast-charging stations.•Developing EV arrival rate profile by considering different charging tariffs.•A stochastic modeling by using point estimation method and Gram-Charlier expansion.•Evaluating risk of single-objective function by probabilist...

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Veröffentlicht in:Electric power systems research Jg. 189; S. 106698
Hauptverfasser: Aghapour, Raziye, Sepasian, Mohammad Sadegh, Arasteh, Hamidreza, Vahidinasab, Vahid, Catalão, João P.S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.12.2020
Elsevier Science Ltd
Schlagworte:
Batteries
Chance-constrained programming
Constraints
Electric power systems
Electric vehicle charging
Electric vehicles
Electricity
Energy management
Fast charging station
Point estimation
Probabilistic dominance criteria
Probability theory
Queuing theory
Sizing
Stations
Stochastic models
Traffic accidents & safety
Traffic assignment
Traffic flow
Traffic models
Transportation models
Transportation networks
Transportation systems
Chance-constrained programming
Fast charging station
Queuing theory
Probabilistic dominance criteria
Traffic assignment
Point estimation
ISSN:0378-7796, 1873-2046
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Zusammenfassung:•An integrated planning model for optimal placement of fast-charging stations.•Developing EV arrival rate profile by considering different charging tariffs.•A stochastic modeling by using point estimation method and Gram-Charlier expansion.•Evaluating risk of single-objective function by probabilistic dominance criteria.•The objective function is to minimize annual investment cost and energy losses. One of the most important aspects of the development of Electric Vehicles (EVs) is the optimal sizing and allocation of charging stations. Due to the interactions between the electricity and transportation systems, the key features of these systems (such as traffic network characteristics, charging demands and power system constraints) should be taken into account for the optimal planning. This paper addressed the optimal sizing and allocation of the fast-charging stations in a distribution network. The traffic flow of EVs is modeled using the User Equilibrium-based Traffic Assignment Model (UETAM). Moreover, a stochastic framework is developed based on the Queuing Theory (QT) to model the load levels (EVs’ charging demand). The objective function of the problem is to minimize the annual investment cost, as well as the energy losses that are optimized through chance-constrained programming. The probabilistic aspects of the proposed problem are modeled by using the point estimation method and Gram-Charlier expansion. Furthermore, the probabilistic dominance criteria are employed in order to compare the uncertain alternatives. Finally, the simulation results are provided for both the distribution and traffic systems to illustrate the performance of the proposed problem.
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ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106698