Data Analytics in Smart Grid for Optimal Energy Management for Green Cities

Abstract

In the modern era of rapid urbanization and pressing environmental concerns, the concept of smart grids has emerged as a pivotal solution towards sustainable and energyefficient cities. To fully leverage the capabilities of smart grids and make them integral to green cities, it is imperative to effectively harness the data generated by these intelligent power systems. Data analytics facilitates in an efficient and swift analysis of the huge volume of data generated by the diverse components and devices of a smart grid. The existing literature on the application of data analytics for smart grids primarily focuses on preliminary techniques that do not fully exploit the value of the available data. This thesis explores the application of advanced data analytics approaches to enhance the efficiency, reliability, and sustainability in urban energy systems. We propose a graph theory-based predictive data analytics algorithm designed to enhance electricity transmission reliability within microgrids. By accurately predicting power transmission losses and identifying optimal routes, the algorithm outperforms existing methods, demonstrating improved power loss reduction, congestion management, accuracy, reliability, and adaptability to different power flow scenarios. Additionally, the study focuses on optimizing load management within microgrids and proposes a prescriptive data analytics algorithm to efficiently cover power deficiencies using various energy sources like battery storage, main grid and on-site generators. This approach improves power supply reliability and overall microgrid efficiency. Transitioning to the smart home level, the research employs prescriptive data analytics to propose an optimal energy trading system in smart grids, potentially increasing energy trading profitability while reducing reliance on the main grid. Lastly, building upon advanced data analytics approaches, we define smart grid software problems and propose an automated solution development framework for smart grids. Our framework outperforms current approaches in scalability, concurrency, efficiency, and support for various revenue-generating business models, presenting promising prospects for innovation and fostering self-sufficiency in smart grids. Together, these research findings have the potential to contribute to the transformation of urban energy systems, fostering sustainability, reliability, and economic viability amid rising energy demands and environmental challenges.