یک روش ترکیبی پیش بینی احتمالاتی بلندمدت بار خالص شبکه با در نظر گرفتن اثر توان تولید‌شده توسط منابع انرژی تجدیدپذیر در شبكه‌هاي هوشمند
محورهای موضوعی : مهندسی برق و کامپیوتر
محسن جهان تیغ
1
(دانشگاه آزاد اسلامی واحد نجف آباد)
مجيد معظمي
2
(دانشگاه آزاد اسلامي واحد نجف آباد)
کلید واژه: پیش بینی احتمالاتی بلندمدت بار, تحلیل اجزای همسایگی, سیستم استنتاج عصبی- فازی, شبکه هوشمند, تولید بادی, تولید فوتوولتائیک,
چکیده مقاله :
امروزه با توجه به رشد گسترده و نفوذ استفاده از منابع توليد پراكنده در شبكه‌هاي هوشمند، پيش‌بيني بار خالص شبكه با در نظر گرفتن اثر توليدات پراكنده اهميت قابل توجهي پيدا كرده است. در اين مقاله يك روش بهينه‌سازي تركيبي به منظور پیش‌بینی احتمالاتي بلندمدت بار خالص شبكه با استفاده از روش تحلیل اجزای همسایگی و حل مسأله رگرسیون به روش mini-batch-LBFGS و ترکیب پیش‌بینی‌های به دست آمده با استفاده از سیستم استنتاج عصبی- فازی تطبیقی ارائه شده است. اين ساختار شامل تركيب چندين پيش‌بيني بلندمدت از جمله پیش‌بینی بار، توان يك ايستگاه خورشيدي و توان یک مزرعه بادی با توربینهای بادی مجهز به ژنراتور القایی دوسوتغذیه است. پیش‌بینی بار خالص و بررسی وابستگی موجود بین خطاهای پیش‌بینی بار و توانهای خورشیدی و بادی نیز در این مقاله مورد مطالعه قرار گرفته است. نتايج شبيهسازي روش پيشنهادي و مقایسه آن با مدلهای تائو و رگرسیون چندکی نشان می‌دهد که درصد میانگین مطلق خطا برای پیش‌بینی‌های بار و توانهای خروجی ایستگاه خورشیدی و مزرعه بادی به ترتیب به میزان 947/0%، 3079/0% و 0042/0% بهبود یافته است که کاهش خطای کلی پیش‌بینی را سبب می‌شود.
With the growth and integration of distributed generation resources in smart grids, net load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for probabilistic net load forecasting using neighborhood component analysis and solving regression problem with the aid of mini-batch LBFGS method. Net load forecasting is suggested in this paper trough forecast combination via adaptive network-based fuzzy inference system. The structure includes a combination of several long-term forecasts, including forecasts of load, the generation of a solar station, and the generation of a wind farm with wind turbines equipped with doubly-fed induction generator. Also, the net load forecasting and the relationship between errors of load, wind and solar predictions are studied in this paper. The simulation results of the proposed method and its comparison with Tao and quantile regression models show that mean absolute percentage error of load forecasting, and the forecasts of solar and wind generations improved by 0.947%, 0.3079% and 0042%, respectively which result to a decrease in net load forecasting error.
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