ایجاد یک جهش در الگوریتم گرگ خاکستری برای حل مسئله توزیع اقتصادی-زیست‌محیطی نیروگاه‌های ادغام شامل حرارتی و بادی
محورهای موضوعی : انرژی های تجدیدپذیر
مهدی افروزه
1
,
حمیدرضا عبدالمحمدی
2
,
محمداسماعیل نظری
3
1 - گروه مهندسی برق- واحد خمین، دانشگاه آزاد اسلامی، خمین، ایران
2 - گروه مهندسی برق و کا مپیوتر- دانشکده فنی مهندسی گلپایگان، دانشگاه صنعتی اصفهان، گلپایگان
3 - گروه مهندسی برق و کا مپیوتر- دانشکده فنی مهندسی گلپایگان، دانشگاه صنعتی اصفهان، گلپایگان
کلید واژه: اثر شیر بخار, الگوریتم گرگ خاکستری جهش یافته, توزیع اقتصادی زیست‌محیطی, مزرعه‌های بادی,
چکیده مقاله :
در این مقاله نسخه جهش یافته دینامیکی الگوریتم بهینه سازی گرگ خاکستری برای حل مسئله پخش بار اقتصادی- زیست محیطی سیستم قدرت استاندارد 40 واحدی به همراه دو مزرعه بادی پیشنهاد شده است. لذا تابع هدفی جامع از هزینه های بهره برداری که ترکیبی از هزینه های مستقیم انرژی باد، هزینه جریمه تخمین بیش از حد، هزینه جریمه تخمین کمتر از حد، هزینه واحد حرارتی و هزینه آلایندگی، ارائه شده است. با توجه به ماهیت تصادفی سرعت باد توان تولیدی توسط توربین های بادی غیرقابل پیش بینی است، بنابراین از تابع توزیع احتمال ویبول برای مدل سازی توان مزرعه های باد در این مقاله استفاده شده است. هزینه بهره برداری مزرعه بادی به صورت احتمالاتی در نظر گرفته شده است تا سناریوهای باد با احتمال پایین تاثیر کمتری در هزینه نهایی داشته باشند. شبیه سازی ها در قالب سه بخش انجام شده است و به منظور اعتبارسنجی با مرجع های دیگر مورد مقایسه واقع شده است. نتایج حاصل شده از بهینه سازی ها در هر سه سناریو و مقایسه آن با الگوریتم های هوشمند تائیدی بر عملکرد بهتر و دقت بالاتر الگوریتم پیشنهادی نسبت به نسخه اصلی الگوریتم گرگ خاکستری و همچنین سایر الگوریتم ها دارد.
In this paper, a dynamic mutant version of the gray wolf optimization algorithm (MGWO) is proposed to solve the economic-environmental dispatch (E-ED) problem of a standard 40-unit power system with two wind farms. Thus, a comprehensive objective function of operating costs is presented, which is a combination of wind energy costs, over-estimated penalty costs, under-estimated penalty costs, thermal unit costs and emission costs. Due to the random nature of wind speed, the power generated by wind turbines is unpredictable. Therefore, the Weibull probability distribution function has been used to model the wind farm power in this paper. The cost of operating a wind farm is considered probabilistic so that low-probability wind scenarios have less effect on the total operation cost. The simulations are performed in the form of three section and the optimization results are compared with several meta-heuristic algorithm results for validation. The results of the optimizations in all three scenarios and its comparison with other algorithms confirm the better performance and higher accuracy of the proposed MGWO algorithm than the original version of the gray wolf algorithm (GWO) as well as other algorithms.
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