الگوریتمی ترکیبی بر پایه روش‌های هوش محاسباتی جهت مدیریت مصرف برق خانگی با حضور خودروی برقی

نویسندگان

سبزوار - دانشگاه حکیم سبزواری - دانشکده مهندسی برق و کامپیوتر

چکیده

در این مقاله با استفاده از یک الگوریتم ترکیبی هوشمند روشی برای مدیریت مصرف برق خانگی مناسب جهت پیاده‌سازی در بستر شبکه برق هوشمند برق مطرح گردیده است. هدف اصلی این مقاله ارائه روشی جهت مدیریت مصرف برق بخش خانگی با حضور خودروی برقی می‌باشد. برای رسیدن به این هدف در گام اول، یک مدل برای مدیریت مصرف برق وسایل خانگی با هدف حداقل‌سازی هزینه خانوار و با در نظر گرفتن پارامتر رفاه ارائه گردیده است. در گام دوم به مدل‌سازی برنامه پاسخ‌گویی بار اضطراری (EDRP) و بررسی امکان تبادل انرژی بین خودروی برقی و شبکه برق تحت این برنامه پرداخته شده است. در گام سوم، مساله برنامه‌ریزی شارژ خودروی برقی در برنامه مدیریت مصرف برق به‌صورت تطبیقی و احتمالاتی بررسی شده است. شبیه‌سازی‌های انجام شده با سناریوی مشخص نشان‌دهنده کاهش 60.96 درصدی مصرف انرژی در مدیریت مصرف برق وسایل خانگی و نیز زمان‌بندی بهینه شارژ خودروی الکتریکی می‌باشد. همچنین در قسمت پاسخ‌گویی بار اضطراری در مقایسه دو سناریوی متفاوت مقدار 66.77% هزینه مشتری و پیک بار مقدار 1 کیلووات به ازای هر خانه کاهش یافته است.

کلیدواژه‌ها

عنوان مقاله [English]

A Hybrid Algorithm based on Computational Intelligence Methods for House Energy Management in Presence of Electric Vehicle

نویسندگان [English]

  • M. Rajabimendi
  • M. E. Hajiabadi
  • M. Baghaei Nejad
Electronics and Computer Engineering Department, Hakim Sabzevari University, Sabzevar, Iran
چکیده [English]

This paper has proposed an energy management system for domestic house applications considering electric vehicle. The first step is linked to optimal and adaptive scheduling of appliances and is defined as a multi objective function model with some fuzzy coefficient and owner comfort factors. The important aspect of this tool is to help customers respond to real time electricity price. In the second step, emergency demand response program (EDRP) modeling and the possibility of energy exchange has been investigated. In the third step, the electric vehicle charging planning has been investigated as adaptive and uncertainty. In energy management part, specific scenarios is simulated which reflect 60.96% of energy consumption reduction and the optimal scheduling of electric vehicle charging too. In the emergency demand response part two specific scenarios are simulated which reflect 66.77% of energy consumption reduction and peak shaving of 1 kW per house.

کلیدواژه‌ها [English]

  • Energy management system (EMS)
  • electric vehicle
  • emergency demand response program
  • energy exchange
  • computational intelligence methods

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