دانلود مقاله یک سیستم مدیریت انرژی آنلاین برای ریزشبکه‌های مسکونی
چکیده
مقدمه
معماری AC/DC MG مسکونی
سیستم مدیریت انرژی ارائه شده
مطالعه موردی
نتایج تجربی
نتیجه گیری
منابع
Abstract
Introduction
The architecture of the residential AC/DC MG
Proposed energy management system
Case study
Experimental results
Conclusion
References
چکیده
سیستم های انرژی الکتریکی سنتی در حال تجربه یک انقلاب بزرگ هستند و محرک های اصلی این انقلاب انتقال سبز و دیجیتالی شدن هستند. در این مقاله، یک EMS پیشرفته در سطح سیستم برای ریزشبکه‌های AC/DC مسکونی (MGs) با بهره‌گیری از نوآوری‌های ارائه شده توسط دیجیتالی‌سازی پیشنهاد شده است. EMS پیشنهادی از انتقال سبز پشتیبانی می‌کند، زیرا برای MG طراحی شده است که شامل منابع انرژی تجدیدپذیر (RES)، باتری‌ها و وسایل نقلیه الکتریکی است. علاوه بر این، رفتارهای مصرف برق کاربران مسکونی به طور خودکار استخراج شده است تا MG انعطاف پذیرتری ایجاد کند. الگوریتم نظارت بر بار غیر نفوذی با پشتیبانی از یادگیری عمیق (NILM) برای تجزیه و تحلیل و تفکیک سیگنال مصرف انبوه هر خانوار در MG به کار گرفته شده است. یک EMS دو سطحی طراحی شده است که هر دو خانواده و اجزای MG را با استفاده از بهینه‌سازی، پیش‌بینی و ماژول‌های NILM هماهنگ می‌کند. EMS در سطح سیستم پیشنهادی در یک محیط آزمایشگاهی در زمان واقعی آزمایش شده است. آزمایش‌ها با در نظر گرفتن دوره‌های بهینه‌سازی مختلف انجام می‌شوند و اثربخشی EMS پیشنهادی برای افق‌های بهینه‌سازی مختلف نشان داده شده است. در مقایسه با یک استراتژی حداکثر اصلاح به عنوان معیار، EMS پیشنهادی برای افق 24 ساعته کاهش 12.36٪ در هزینه عملیات روزانه MG مسکونی را فراهم می کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Traditional electric energy systems are experiencing a major revolution and the main drivers of this revolution are green transition and digitalization. In this paper, an advanced system-level EMS is proposed for residential AC/DC microgrids (MGs) by taking advantage of the innovations offered by digitalization. The proposed EMS supports green transition as it is designed for an MG that includes renewable energy sources (RESs), batteries, and electric vehicles. In addition, the electricity consumption behaviors of residential users have been automatically extracted to create a more flexible MG. Deep learning-supported Non-intrusive load monitoring (NILM) algorithm is deployed to analyze and disaggregate the aggregated consumption signal of each household in the MG. A two-level EMS is designed that coordinates both households and MG components using optimization, forecasting, and NILM modules. The proposed system-level EMS has been tested in a laboratory environment in real-time. Experiments are performed considering different optimization periods and the effectiveness of the proposed EMS has been shown for different optimization horizons. Compared to a peak shaving strategy as a benchmark, the proposed EMS for 24-hour horizon provides a 12.36% reduction in the residential MG daily operation cost.
Introduction
During the last decade, with the increasing integration of residential wind turbines (WTs) and photovoltaic panels (PVs) as well as electric vehicles (EVs), electricity consumers have found a new role as prosumers. The possibility of locally generating and storing power along with the introduction of smart home appliances (washing machines, dishwashers, cloth dryers, electric water heaters, air conditioners, etc.) has considerably increased the flexibility on the consumer side being able to manage their power consumption pattern and participate in demand response programs. This active participation benefits both the consumers and the electricity grid in several ways. From the consumers' point of view, a lower energy cost and higher utilization of renewable energy can be expected while electricity utilities can flatten the grid load curve and reduce the stress on the system equipment, thereby increasing the system efficiency, reliability, and lifetime.
Conclusion
Energy management strategies are of great importance for the optimal operation of MGs. In this paper, an advanced system-level EMS designed for a hybrid AC/DC residential MG has been proposed and experimentally validated to efficiently operate a residential MG. Since the analyzed MG has individual households, the smart meter signals of each household have been analyzed with NILM algorithm to extract the consumption profiles of the customers. By using this information, consumers' daily energy costs were minimized at the first level of optimization, by considering their consumption habits. In this way, it was ensured that both consumers' bills were reduced and their comfort levels were not affected. In the second level of optimization, optimum operation of MG was ensured by considering the generation and consumption units of MG. Real-time test results have proven that a 24-hour optimization horizon provides more optimal operation than 6 and 12-hour horizons. Experiments have shown that the battery cannot be fully charged when using a 6-hour horizon, thus increasing the operating cost. In the 24-hour horizon, the battery could be fully charged, thus minimizing the operating cost of the MG. In addition, thanks to the PAR function included in the multi-objective optimization problem, the power drawn from the utility grid is smoothed.