مدل‌سازی احتمالی تقاضای بار ایستگاه شارژ سریع برای خودروهای الکتریکی عمومی
محورهای موضوعی : مهندسی برق و کامپیوترحسین یوسفی 1 , مقداد تورانداز کناری 2 * , محمدصادق سپاسیان 3
1 - دانشگاه شهید بهشتی
2 - دانشگاه شهید بهشتی
3 - دانشگاه شهید بهشتی
کلید واژه: ایستگاه شارژ سریعبار شارژتئوری صفخودروهای الکتریکیشبیه‌سازی مونت‌کارلومدل‌سازی احتمالی,
چکیده مقاله :
با توجه به عوامل اقتصادی و زیست‌محیطی، انتظار می‌رود در آینده‌ای نزدیک استفاده از خودروهای الکتریکی به‌خصوص با قابلیت اتصال به شبکه، افزایش قابل توجهی پیدا کند. نفوذ بالای خودروهای الکتریکی می‌تواند شبکه را تحت تأثیر قرار دهد و از این رو در سال‌های اخیر مطالعات زیادی به اثرات شارژ خودروهای الکتریکی بر روی شبکه پرداخته‌اند. در این مقاله، مدلی احتمالی بر پایه تئوری صف و همچنین شبیه‌سازی مونت‌کارلو با نرم‌افزار ED برای استخراج تقاضای بار ایستگاه شارژ سریع ارائه شده است، با این فرض که خودروهای مورد بررسی، تاکسی‌های شهرستان آمل در استان مازندران باشند. داده‌های مورد نیاز مانند زمان مراجعه و وضعیت شارژ باتری قبل از شارژگیری، با سه روش از تاکسی‌های درون‌شهری شهرستان آمل جمع‌آوری و استخراج شده است. به دست آوردن منحنی تقاضای بار شارژ خودروهای الکتریکی نیازمند داده‌هایی می‌باشد که وابسته به رفتار ترافیکی رانندگان است. از آنجایی که رفتار صاحبان خودرو غیر قطعی می‌باشد، در نتیجه این داده‌ها به‌صورت متغیرهای غیر قطعی تعریف شده و توسط روش‌های احتمالی مورد ارزیابی قرار گرفته‌اند.
Considering economic and environmental factors, it is expected that the number of plug-in electric vehicles (PEVs) will be increased, rapidly. The high penetration of EVs, can affect the power system. Therefore, in recent years, various studies have paid their attention to the impacts of PEVs charging on the network. In this paper, a probabilistic model based on the queueing theory is extracted using Monte Carlo simulation for modeling EV charging station load. It is assumed that the vehicles are the taxis of Amol city in Mazandaran province. Required data such as the time of arrival and the state of charge of the battery before charging, were collected and extracted using three methods from intra-city taxis in the city of Amol. To obtain the demand load of EV charging, the traffic-based behavior of drivers is needed. This behavior is stochastic. Therefore, its related variables will not be deterministic and must be evaluated using probabilistic methods.
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