دانلود رایگان مقاله تجزیه و تحلیل داده بزرگ برای مدیریت انرژی پویا در شبکه هوشمند

Abstract

The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of large volumes of data generated by the vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.

6. Conclusions

In this paper, we have summarized the state-of-the-art in the exploitation of big data tools for dynamic energy management in smart grid platforms. We have first highlighted that, in order to deal with the extreme size of data, the smart grid requires the adoption of advanced data analytics, big data management, and powerful monitoring techniques. Next, we elaborated on the utilization of the most commonly used smart grid data mining and predictive analytics methods, focusing on the smart meter data that are necessary for the accurate and efficient power consumption/supply forecasting. We proceeded with a brief survey on the works dealing with high performance computing, insisting on cost efficiency and security issues in the context of SG control. Finally, we discussed several interesting techniques and methods that have to be further explored into the framework of a real-time monitoring and forecasting system, and we provided promising research directions for future research in the field.