افزایش دادن سود مالکان واحدهای تولید پراکنده همراه با کاهش تلفات سیستم توزیع با استفاده از الگوریتم گرگ خاکستری بهبودیافته
محورهای موضوعی : مهندسی برق قدرت
سید امیر محمد لاحقی
1
(دانشکده مهندسي برق و کامپیوتر، دانشگاه شیراز، شیراز، فارس، ايران)
بهروز ذاکر
2
(دانشکده مهندسي برق و کامپیوتر، دانشگاه شیراز، شیراز، فارس، ايران)
کلید واژه: تولید پراکنده, قیمت‌گذاری بهینه, بهینه‌سازی گرگ خاکستری, درخت تصمیم,
چکیده مقاله :
این مقاله یک راهکار جامع برای بهینه‌سازی عملکرد واحدهای تولید پراکنده در سیستم‌های توزیع ارائه می‌دهد. با تمرکز بر کاهش تلفات شبکه توزیع، راهکار پیشنهادی از قیمت‌گذاری نقطه به نقطه استفاده می‌کند تا قیمت‌ها را در سراسر سیستم توزیع تعیین کند. هدف بهینه‌سازی بر کمینه کردن تلفات شبکه تمرکز دارد و از قیمت‌های مشارکتی اعلام‌شده توسط مالکان واحدهای تولید پراکنده استفاده می‌کند. هم‌چنین بهینه‌سازی قیمت‌ها با استفاده از الگوریتم بهینه‌سازی گرگ خاکستری بهبودیافته انجام می‌شود که برای بهبود آن از یک مدل درخت تصمیم استفاده شده است که اجازه تشخیص راهکارهای بهینه در هر تکرار را فراهم می‌کند که این اقدام باعث افزایش سرعت و دقت در هر مرحله از آموزش الگوریتم می‌شود. کارایی روش پیشنهادی بر روی دو سیستم توزیع آزمایشی 33شینه و 69شینه IEEE در نرم‌افزار MATLAB ارزیابی می‌شود که نتایج آن حاکی از بهبودی چشمگیر در سرعت و دقت راهکار ارائه‌شده نسبت به روش‌های قبلی است. به طور کلی، این مطالعه می‌تواند به پیشرفت استراتژی‌های کارآمد برای مدیریت واحدهای تولید پراکنده در سیستم‌های توزیع، با تاکید بر سودآوری و حل چالش‌های بهینه‌سازی شبکه، کمک شایانی کند.
This paper presents a comprehensive solution for optimizing the performance of distributed generation units in distribution systems. Focusing on reducing distribution network losses, the proposed solution utilizes point-to-point pricing method to determine prices across the distribution system. The optimization objective is to minimize network losses, utilizing participatory prices declared by the owners of distributed generation units. Furthermore, price optimization is carried out using an improved grey wolf optimization algorithm, which employs a decision tree model to identify optimal solutions in each iteration, enhancing speed and accuracy at each stage of the algorithm training. The efficacy of the proposed method is evaluated on two IEEE 33-bus and 69-bus test distribution systems in MATLAB software, showing significant improvement in the speed and accuracy of the proposed solution compared to previous methods. Overall, this study can contribute to the advancement of efficient strategies for managing distributed generation units in distribution systems, emphasizing profitability and addressing network optimization challenges.
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