یک چارچوب بهینه احتمالی جهت بهره‌برداری از سپهر‌های انرژی ادغام‌شده با بارهای پاسخگوی سرمایشی، حرارتی و الکتریکی و سیستم ذخیره‌ساز یخ توسط الگوریتم بهینه‌سازی خود-تطبیق کپک مخاطی بهبودیافته

محورهای موضوعی : مهندسی برق قدرت

محمد عمادی ¹ , حمید رضا مسرور ² , اسمعیل رک رک ³ , امین سامان فر ⁴

1 - گروه مهندسی برق، دانشکده مهندسی برق، واحد خرم آباد، دانشگاه آزاد اسلامی، خرم آباد، لرستان، ایران
2 - گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران، ایران
3 - گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه لرستان، خرم آباد، لرستان، ایران
4 - گروه مهندسی برق، دانشکده مهندسی برق، واحد خرم آباد، دانشگاه آزاد اسلامی، خرم آباد، لرستان، ایران

تاریخ دریافت : 1401/07/20 تاریخ پذیرش : 1401/09/28 تاریخ انتشار : 1402/03/01

کلید واژه: الگوریتم کپک مخاطی, روش تخمین نقطه‌ای, سپهر انرژی یکپارچه, تولید چند انرژی, مدیریت تصادفی سپهر انرژی,

چکیده مقاله :

به دنبال گسترش استفاده از سپهرهای چند حاملی انرژی در صنایع، این مقاله یک چارچوب تصادفی جامع جهت مدیریت بهینه و برنامه‌ریزی روزانه یک سپهر انرژی ادغام‌شده با منابع انرژی تجدید پذیر و بارهای پاسخگوی سرمایشی، حرارتی و الکتریکی و سیستم ذخیره‌ساز یخ ارائه می‌دهد. برای حل این چالش، از روش تخمین نقطه‌ای2m+1 جهت ارزیابی دقیق عدم قطعیت‌های سیستم با پیچیدگی محاسباتی کم استفاده می‌شود. روش تخمین نقطه‌ای2m+1 یک روش تحلیل عدم قطعیت سریع بر اساس سری تیلور است. در این روش عدم قطعیت منابع انرژی تجدید پذیر و بارهای الکتریکی و حرارتی سپهر انرژی و همچنین قیمت مبادله با شبکه‌های مختلف توزیع انرژی بالادستی در نظر گرفته‌شده است. این مقاله همچنین یک روش بهینه‌سازی خود-تطبیق جدید به نام الگوریتم بهینه‌سازی خود-تطبیق بهبودیافته کپک مخاطی (SMSMA) را جهت حل مسئله پیچیده غیرخطی برنامه‌ریزی بهینه روزانه یک سپهر انرژی ارائه می‌دهد. روش خود-تطبیق بهبودیافته شده بر مبنای تئوری موجک است که قابلیت و توانایی الگوریتم اصلی کپک مخاطی را جهت حل مسئله برنامه‌ریزی بهینه روزانه یک سپهر انرژی یکپارچه بهبود می‌بخشد. نتایج عددی نشان می‌دهد که چارچوب برنامه‌ریزی تصادفی روزانه پیشنهادی، همراه با الگوریتم بهینه‌سازی SMSMA پیشنهادی، هزینه‌های بهره‌برداری سپهر انرژی را کاهش می‌دهد.

چکیده انگلیسی:

Following the expansion of the use of multi-carrier energy hubs in industries, this paper presents a comprehensive stochastic framework for optimal management and daily scheduling of an energy hub integrated with renewable energy sources and responsive cooling, thermal and electrical loads, and ice storage system. To solve this challenge, the 2m+1 Point Estimation Method (PEM) is used to accurately evaluate the system's uncertainties with low computational complexity. The 2m+1 PEM is a fast uncertainty analysis method based on the Taylor series. This method considers the uncertainty of renewable energy sources, the cooling, electrical and thermal loads, and the exchange price with different upstream energy distribution networks. This paper also presents a new self-adaptive optimization method called the Self-adaptive Modified Slime Mold optimization Algorithm (SMSMA) to solve the complex nonlinear problem of optimal daily scheduling of an energy hub. The improved self-adaptive method is based on the wavelet theory, which improves the capability and ability of the original slime mold algorithm to solve the daily optimal scheduling problem of an integrated energy hub. Numerical results show that the proposed daily stochastic scheduling framework, together with the proposed SMSMA algorithm, effectively reduces the operating costs of energy hubs.

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