دانلود مقاله بررسی تاثیر تصادفی در مدیریت انرژی ریزشبکه
چکیده
مقدمه
مدل
نتایج محاسباتی
نتیجه گیری
منابع
Abstract
Introduction
Model
Computational results
Conclusion
References
چکیده
روش‌های برنامه‌ریزی تصادفی مختلفی برای توضیح نفوذ تولید انرژی تجدیدپذیر نامشخص در ریزشبکه‌ها مورد استفاده قرار گرفته‌اند. با این حال، این روش های تصادفی ممکن است غیر ضروری باشند. ذخیره انرژی همراه با زمان‌بندی مجدد بر اساس یک افق زمانی چرخشی، ابزار قدرتمندی را برای انطباق با هر رویداد غیرمنتظره به ریزشبکه می‌دهد. گرایش طبیعی به مهندسی بیش از حد سیستم‌های جدید را به آن اضافه کنید و انسان شروع به تعجب می‌کند که چقدر می‌توان با بهینه‌سازی تصادفی نسبت به روش‌های قطعی به دست آورد. ما این سوال را با نگاه کردن به یک ریزشبکه مسکونی موجود در هوور، AL بررسی کردیم. ما روش‌های مختلف تصادفی برای زمان‌بندی را با رویکردهای قطعی مقایسه می‌کنیم و نشان می‌دهیم که استفاده از برنامه‌نویسی تصادفی ارزش کمی دارد. در عوض، متوجه می‌شویم که در نظر گرفتن افق‌های زمانی طولانی‌تر، استفاده بهتر از منابع محاسباتی است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Various stochastic programming methods have been used to account for penetration of uncertain renewable energy generation in microgrids. However, these stochastic methods may be unnecessary. Energy storage combined with rescheduling based on a rolling time horizon gives a microgrid powerful tools to adapt to any unexpected events. Add to that the natural tendency to over-engineer new systems and one begins to wonder how much value can be gained by stochastic optimization over deterministic methods. We investigated this question by looking at an existing residential microgrid in Hoover, AL. We compare various stochastic approaches for scheduling with deterministic approaches and show that there is little value of using stochastic programming. Instead, we find that considering longer time horizons is a better use of computational resources.
Introduction
Due to deregulation of electricity markets and increasing renewable energy adoption, distributed electricity generation is gaining traction. Microgrids have been introduced as a way to facilitate distributed energy and incorporate renewables while improving reliability [1]. Microgrids have already shown promise in delivering power to remote areas, as well as the ability to coexist with existing electrical infrastructure [2, 3].
because of their distributed nature, microgrids require local control and optimization infrastructure to ensure successful operation, especially when isolated from any other electrical grid [4]. Many deterministic and stochastic optimization architectures have been developed to handle this need for local optimization of assets [5, 6]. The stochastic methods have the advantage of accounting for uncertain quantities in their operation such as wind turbines, photovoltaic (PV) power, and load. On the other hand, stochastic optimization often requires decomposition algorithms due to the large size of the models.
Conclusion
This work investigated the impact of stochastic approaches to microgrid scheduling. We use real data from a residential microgrid in Hoover, AL. to demonstrate that stochastic approaches are not likely to produce signifcant cost savings over deterministic methods, even when the error associated with solar forecasts is high. This is in part due to the rolling time horizon approach’s ability to adjust to sudden changes as well as the natural robustness of the microgrid design. Given that solutions must be obtained quickly in a (near) real-time use, we argue that using longer time horizons in a derministic model are likely to be more impactful than incorporating uncertainty.