مهندسی صنایع و مدیریت

مهندسی صنایع و مدیریت

‌به‌کارگیری الگوریتم یادگیری ماشین برای بهینه‌سازی تأمین انرژی در نیروگاه‌‌های مجازی

نوع مقاله : پژوهشی

نویسندگان
بنت الهدی دهقانی 1
محمدمهدی لطفی 1
علیرضا صدیقی 2
1 گروه مهندسی صنایع، دانشگاه یزد، یزد، ایران
2 دانشکده‌ی مهندسی برق، دانشگاه یزد، یزد، ایران.
10.24200/j65.2025.66704.2433
چکیده
در پژوهش حاضر، مسئله‌ی مشارکت بهینه‌ی واحدها در یک نیروگاه مجازی با ذخیره­ساز سمت تقاضا، جهت کاهش شکاف بین عرضه و تقاضای انرژی با رویکردهای MILP و یادگیری ماشین مطالعه شده است. نتایج برای سیستم 24 - باسه استاندارد IEEE  نشان داده‌اند که گرچه استفاده از ذخیره­ساز سمت تقاضا باعث افزایش هزینه و نوسان تولید نیروگاه مجازی می­شود، اما هزینه­ها در نیروگاه مجازی با ذخیره­ساز سمت تقاضا حدود 5/1 برابر کمتر از هزینه­های شبکه‌ی سنتی بدون ذخیره­ساز است. برای حل مدل در ابعاد واقعی، از الگوریتم ترکیبی ELM-GA استفاده شده است، که پیشنهادی نزدیک به بهینه، اما با سرعت بسیار بالاتر از روش MILP ارائه می­کند؛ زیرا آموزش و یادگیری همگرایی به نقطه‌ی بهینه را سرعت می­‌بخشد. نتایج نشان می­دهند که با افزایش ابعاد مسئله به 60 واحد نیروگاهی، شکاف بهینگی به 4/4٪ رسیده است، در حالی که سرعت حل یادگیری ماشین 16 برابر بهتر از MILP بوده است.
کلیدواژه‌ها
نیروگاه مجازی
مدیریت سمت تقاضا
مسئله‌ی مشارکت واحدها
برنامه‌ریزی خطی عدد صحیح
یادگیری ماشین
موضوعات

عنوان مقاله English

Developing Machine Learning Algorithm for Energy Supply Optimization in Virtual Power Plants

نویسندگان English

Bentolhoda Dehghani 1
Mohammad Mehdi Lotfi 1
Alireza Sedighi 2
1 Department of Industrial Engineering, Yazd University, Yazd, Iran.
2 Department of Electrical Engineering, Yazd University, Yazd, Iran.
چکیده English

This research addresses the challenge of optimizing energy dispatch in restructured power markets by evaluating a Virtual Power Plant (VPP) model that integrates demand-side storage. The study aims to close the gap between energy supply and demand by employing two optimization methodologies: a precise Mixed-Integer Linear Programming (MILP) model and a hybrid algorithm based on an Extreme Learning Machine and a Genetic Algorithm (ELM-GA), which is computationally efficient. The VPP model incorporates a diverse portfolio of generation units (diesel, hydroelectric, solar, wind), supply-side storage, and a prosumer, with demand-side management actualized through a water storage system. The objectives are to maximize VPP profitability while minimizing hydroelectric energy losses and pump operational costs. Tested on a standard IEEE 24-bus system with real-world data, the results show that a VPP with demand-side storage is 1.5 times less costly than a traditional grid without storage. Furthermore, the VPP framework is 1.7 times more cost-effective than a traditional grid structure. For large-scale problems, the ELM-GA algorithm delivers near-optimal solutions (4.4% optimality gap) 16 times faster than MILP, highlighting its potential for rapid, reliable energy management and policy-making.

کلیدواژه‌ها English

Virtual power plant
demand side management
unit commitment
MILP
machine learning
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