نوع مقاله : پژوهشی

نویسندگان

گروه برق و کامپیوتر- واحد کاشان، دانشگاه آزاد اسلامی، کاشان، ایران

چکیده

امروزه افزایش قیمت سوخت­های فسیلی و کاهش منابع از یک سو و آلودگی­های زیست محیطی از سوی دیگر باعث افزایش استفاده از منابع تجدیدپذیر گردیده است. در این مقاله ریزشبکه مورد مطالعه از منابع بادی و خورشیدی، ذخیره­ساز (باتری و فلایویل) خودرو الکتریکی، دیزل ژنراتور و حضور سیستم­های انرژی چند حاملی MCH)) به­عنوان انرژی ترکیبی برق و حرارت (CHP) تشکیل شده است. فرکانس ریزشبکه باتوجه به شبکه گاز و پیک مصرف کنترل می­شود. در ریزشبکه چند حاملی پخش بار شبکه گاز همزمان با پخش بار الکتریکی منظور می­گردد. همچنین فرکانس به­صورت غیرخطی کنترل می‌شود. از طرف دیگر روند رو به­ رشد تولید و به­کارگیری خودروهای برقی (V2G) بارهای جدیدی برای شبکه برق ایجاد کرده است که در صورتی که مدیریت صحیحی بر روی نحوه شارژ آنها صورت نگیرد انحراف فرکانس شبکه افزایش یافته و می­تواند سبب فروپاشی شبکه گردد. لذا از خودروهای برقی به­ منظور مشارکت در عملیات تنظیم فرکانس ریزشبکه با روش سیستم استنتاج فازی عصبی تطبیقی (ANFIS) استفاده می­شود. به­منظور مقایسه روش پیشنهادی در شبیه­سازی‌ها از کنترل­کننده فازی استفاده شده است. نتایج حاصل از شبیه­سازی‌ها در پنج مطالعه مورد بررسی قرار می‌گیرند که بیان­گر عملکرد مطلوب روش پیشنهادی در کاهش انحراف فرکانس، استحکام در برابر اغتشاشات و مقاوم بودن در برابر عدم قطعیت‌‌های موجود در سیستم است. همچنین روش پیشنهادی توان خروجی پایدارتری در منابع تولید ریزشبکه دارد. 

چکیده تصویری

کنترل فرکانس در ریزشبکه‌‌های چند-حاملی با حضور خودروهای الکتریکی مبتنی بر کنترل‌کننده سیستم استنتاج فازی عصبی تطبیقی

تازه های تحقیق

- عملکرد کنترل کننده فازی و انفیس مقایسه شده است.

- کنترل کننده پیشنهادی انفیس عملکرد سیستم کنترل را بهبود می بخشد.

- کنترل کننده پیشنهادی انفیس عملکرد میرایی بسیار بهتری نسبت به کنترل کننده فازی دارد.

- کنترل کننده پیشنهادی ماهیت سازگار و قوی دارد.

- کنترل کننده پیشنهادی انحراف فرکانس شبکه را در زمان کمتری برای اختلالات حذف می کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Frequency Control in Multi-Carrier Microgrids with the Presence of Electric Vehicles Based on Adaptive Neuro Fuzzy Inference System Controller

نویسندگان [English]

  • Seyed Ali Seyed Beheshti Fini
  • Seyed Mohammad Shariatmadar
  • Vahid Amir

Department of Electrical and Computer- Kashan Branch, Islamic Azad University, Kashan, Iran

چکیده [English]

Nowadays, the use of renewable resources has increased because of fossil fuel price growth, resource shortage, and environmental pollution. This study investigates a microgrid composed of wind and solar systems with battery storage sources and flywheel, diesel generator, and multi-carrier energy systems (MCH) as combined electricity and heat (CHP). The microgrid frequency is controlled based on the gas network and its consumption peak. In a multi-carrier network, the load distribution in the gas network is simultaneously considered with the electric charge distribution. Besides, the frequency is controlled nonlinearly. On the other hand, the growing trend of producing and using electric vehicles has generated new loads on the electricity network. In this regard, if these loads are not properly managed to charge them, the network’s frequency deviations will increase and cause the collapse of the electricity network.
Therefore, electric vehicles (V2G) are considered in microgrid frequency tuning operations through ANFIS adaptive fuzzy control method. In order to compare the proposed method in the simulations, a fuzzy controller is used. The results of the simulations are examined in five studies that express the optimal performance of the proposed method in reducing frequency deviations, strength against disturbances and resistance Uncertainties in the system. The proposed method also has a more stable output power in microgrid production resources.

کلیدواژه‌ها [English]

  • Adaptive Neuro Fuzzy Inference System
  • Electric Vehicle
  • Frequency control
  • fuzzy control
  • Microgrid
  • secondary frequency

Citation: S.A. Seyed-Beheshti-Fini, S.M. Shariatmadar, V. Amir, "Frequency control in multi-carrier micr­og­r­i­ds with the presence of electric vehicles based on adaptive neuro fuzzy inference system controller", Journal of Intelligent Procedures in Electrical Technology, vol. 14, no. 55, pp. 27-42, December 2023 (in Persian).

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