Big Data Analytics for Dynamic Energy Management in Smart Grids

TL;DR

This paper discusses how big data analytics can enhance dynamic energy management in smart grids by leveraging real-time data, forecasting, and intelligent solutions to improve efficiency, reliability, and sustainability.

Contribution

It highlights the challenges of big data in smart grid energy management and reviews current data processing methods, proposing future research directions.

Findings

Big data analytics is critical for real-time energy management.

Forecasting load and renewable production is essential for efficiency.

Future research should focus on robust data processing techniques.

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 the large volumes of data generated by a 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.