مدیریت انرژی و برنامه ریزی عملیاتی میکروگریدهای شبکه شده در یک محیط تصادفی

محورهای موضوعی : مهندسی برق قدرت

گیلدا حسینی ¹ (دانشکده مهندسی برق، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
سیدبابک مظفری ² (دانشکده مهندسی برق- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
سودابه سلیمانی ³ (دانشکده مهندسی برق- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)

تاریخ دریافت : 1402/08/23 تاریخ پذیرش : 1402/10/26 تاریخ انتشار : 1403/03/01

کلید واژه: مدیریت انرژی, برنامه پاسخگویی بار, ریزشبکه,

چکیده مقاله :

در این مقاله جهت مدیریت بهینه انرژی و افزایش سود حاصله، یک مدل خطی دو مرحله ای برای هماهنگی میکروگرید های شبکه شده به صورت پیش اقدامانه و اصلاحی ارایه شده است. در مرحله اول، یک برنامه ریزی یک روز جلوتر، بدون در نظر گرفتن عدم قطعیت و در یک محیط قطعی برای میکروگرید ها صورت می گیرد. در مرحله دوم با بهره گیری از یک مدل تصادفی، عدم قطعیت هر میکروگرید در بهره برداری شبکه در زمان واقعی در نظر گرفته می شود. در مدلسازی تابع هدف مسئله، اختلاف ناشی از مرحله های پیش اقدامانه و اصلاحی محاسبه و لحاظ می گردد. سناریوهای عدم قطعیت در تولید انرژی باد، خورشید و تقاضا با استفاده از توابع توزیع احتمال از شبیه سازی مونت کارلو بدست می آیند که سناریوهای نماینده با یک روش کاهش سناریو انتخاب می شوند. در این مقاله از الگوریتم K-means برای کاهش سناریوها و از شاخص DB برای خوشه بندی اتوماتیک استفاده شده است. همچنین مدیریت و کنترل بار با استفاده از برنامه پاسخگویی بار می باشد. راهکار پاسخ به تقاضای پیشنهاد شده برای میکروگرید ها با سه سطح از بار مفروض می باشد که فقط سطح غیر بحرانی بار میکروگرید ها براساس سود اقتصادی شبکه قابلیت کنترل پذیری دارد. مدل ارایه شده بهینه سازی، برنامه ریزی عدد صحیح مختلط می باشد که در محیط نرم افزار گمز شبیه سازی و حل شده است. هدف اصلی مدل دو مرحله ای پیشنهاد شده مدیریت بهینه انرژی مبتنی بر اثر بخشی اقتصادی و با عملکرد مناسب شبکه می باشد که نتایج حاصله کارایی مدل را نشان می دهد.

چکیده انگلیسی:

This article introduces a two-stage linear model designed for the coordination of networked microgrids, aimed at optimizing energy management and enhancing profitability through proactive and corrective strategies. Initially, the first stage involves day-ahead hourly planning for microgrids, executed in a deterministic environment without accounting for uncertainties. Subsequently, the second stage addresses these uncertainties in real-time network operation through a stochastic programming approach. The model's objective function quantifies and incorporates the variations resulting from both the proactive and corrective phases. To handle uncertainties in wind and solar energy production as well as load demand, probability distribution functions derived from Monte Carlo simulations are utilized. From these, representative scenarios are chosen using a scenario reduction technique. Specifically, the K-means algorithm is employed for scenario clustering, with the Davies-Bouldin (DB) index facilitating automatic clustering. Additionally, load management is conducted via a demand response program. The proposed model stipulates that, within microgrids, only non-critical load levels can be modulated based on the network's economic benefit. This optimization model, formulated as mixed integer programming, is simulated and resolved in the GAMS software environment. The primary goal of this two-stage model is to achieve optimal energy management by balancing economic efficiency with robust network performance. The results obtained validate the model's effectiveness.

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