کنفرانس بین المللی فناوری و مدیریت انرژی

صفحه اصلی / هشتمین کنفرانس بین المللی فناوری و مدیریت انرژی

Reliable Operation of V2G-Equipped Parking Lots Based on Probabilistic Mobility Patterns of Plug-in Hybrid Electric Vehicles

نویسندگان :

Hossein Shahinzadeh¹ Jalal Moradi² Ahmad Hafezimagham³ Gevork B. Gharehpetian⁴ Mohamed Benbouzid⁵ S.M. Muyeen⁶

1- دانشگاه صنعتی امیرکبیر تهران 2- دانشگاه صنعتی امیرکبیر تهران 3- دانشگاه صنعتی امیرکبیر تهران 4- دانشگاه صنعتی امیرکبیر تهران 5- University of Brest 6- Qatar University

کلمات کلیدی :

Parking Lots،Plug-in Hybrid Electric Vehicles (PHEV)،Smart grids،Vehicle-to-Grid (V2G)،Mobility Patterns،Electricity Markets،Data Analytics،Probabilistic

چکیده :

In the near future, Plug-in Hybrid Electric Vehicles (PHEV) will play a substantial role in the energy markets due to their growing manufacturing rate of them in the vehicle market. To supply the cars with vehicle-to-grid (V2G) capability, corporate parking lots are needed to ensure reliable operation. Hence, to effectively estimate the optimal capacity of each parking lot, multiple aspects must be considered subject to satisfy prevailing constraints. On the one hand, the distributed charge/discharge of PHEVs by residential equipment or parking lot facilities must be steadily monitored and predicted by the electricity grid operator at the distribution level. On the other hand, the distributed capacity of PHEV batteries is an available option for system operators that can be used for various ancillary service purposes such as peak shaving or reserve provision. Moreover, in a P2P model, it is possible to meet some needed energy from the distribution level through parking lots or via PHEV aggregators instead of absorbing from the upstream network, which can provide immense benefits for both sides. In this study, an innovative approach based on the charge/discharge pattern of PHEVs is suggested in order to find the optimal capacity and placement of parking lots in a distribution network. Hence, in order to find the classify the behavior of PHEV owners, a rich historical dataset including routine and non-routine trip distances, the number of routine and non-routine trips, the average duration of trips, the typical arrival and departure times, the type of PHEVs and the technologies of batteries. It is also needed to estimate the typical destination of PHEVs. It is evident that the model contains lots of uncertainty, which is why the probabilistic and stochastic data mining models can help to extract the estimated pattern of PHEVs effectively. Consequently, it is possible to operate better the distribution network aiming to get the most advantages from the capacity of parking lots.