‌به‌کارگیری الگوریتم یادگیری ماشین برای بهینه‌سازی تأمین انرژی در نیروگاه‌‌های مجازی

نوع مقاله : پژوهشی

نویسندگان

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

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

مراجع

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