Big Data Application In Power Systems

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids. Provide a general description and definition of big data, which has been gaining significant attention in the research community. Introduces a comprehensive overview of big data optimization methods in power system. Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks. Presents applications in power systems, such as power flow and protection. Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection.

Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids. Provide a general description and definition of big data, which has been gaining significant attention in the research community. Introduces a comprehensive overview of big data optimization methods in power system. Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks. Presents applications in power systems, such as power flow and protection. Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection.

It has been a little over a century since the inception of interconnected networks and little has changed in the way that they are operated. Demand-supply balance methods, protection schemes, business models for electric power companies, and future development considerations have remained the same until very recently. Distributed generators, storage devices, and electric vehicles have become widespread and disrupted century-old bulk generation - bulk transmission operation. Distribution networks are no longer passive networks and now contribute to power generation. Old billing and energy trading schemes cannot accommodate this change and need revision. Furthermore, bidirectional power flow is an unprecedented phenomenon in distribution networks and traditional protection schemes require a thorough fix for proper operation. This book aims to cover new technologies, methods, and approaches developed to meet the needs of this changing field.

Energy Systems Transition: Digitalization, Decarbonization, Decentralization, and Democratization provides a thorough multidisciplinary overview of the operation of modern green energy systems and examines the role of 4D energy transition in global decarbonization mitigation efforts for meeting long-term climate goals. Contributions present practical aspects and approaches with evidence from applications to real-world energy systems, offering in-depth technical discussions, case studies, and examples to help readers understand the methods, current challenges, and future directions. A hands-on reference to energy distribution systems, it is suitable for researchers and industry practitioners from different branches of engineering, energy, data science, economics, and operation research.

The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Data warehouses were developed for many good reasons, such as providing quick query and reporting for business operations, and business performance. However, over the years, due to the explosion of applications and data volume, many existing data warehouses have become difficult to manage. Extract, Transform, and Load (ETL) processes are taking longer, missing their allocated batch windows. In addition, data types that are required for business analysis have expanded from structured data to unstructured data. The Apache open source Hadoop platform provides a great alternative for solving these problems. IBM® has committed to open source since the early years of open Linux. IBM and Hortonworks together are committed to Apache open source software more than any other company. IBM Power SystemsTM servers are built with open technologies and are designed for mission-critical data applications. Power Systems servers use technology from the OpenPOWER Foundation, an open technology infrastructure that uses the IBM POWER® architecture to help meet the evolving needs of big data applications. The combination of Power Systems with Hortonworks Data Platform (HDP) provides users with a highly efficient platform that provides leadership performance for big data workloads such as Hadoop and Spark. This IBM RedpaperTM publication provides details about Enterprise Data Warehouse (EDW) optimization with Hadoop on Power Systems. Many people know Power Systems from the IBM AIX® platform, but might not be familiar with IBM PowerLinuxTM, so part of this paper provides a Power Systems overview. A quick introduction to Hadoop is provided for those not familiar with the topic. Details of HDP on Power Reference architecture are included that will help both software architects and infrastructure architects understand the design. In the optimization chapter, we describe various topics: traditional EDW offload, sizing guidelines, performance tuning, IBM Elastic StorageTM Server (ESS) for data-intensive workload, IBM Big SQL as the common structured query language (SQL) engine for Hadoop platform, and tools that are available on Power Systems that are related to EDW optimization. We also dedicate some pages to the analytics components (IBM Data Science Experience (IBM DSX) and IBM SpectrumTM Conductor for Spark workload) for the Hadoop infrastructure.

Design and Optimization of Biogas Energy Systems presents an overview on planning, implementing, assessing and optimizing biogas systems, from fuel conversion to power generation. The book introduces the fundamental elements of bioenergy systems, highlighting the specificities of biogas systems. It discusses the current state of their adoption at a global level and the challenges faced by designers and operators. Methods for sizing, simulating and modeling are discussed, including prefeasibility analysis, available production processes, integration into hybrid energy systems, and the application of Big Data analysis and game theory concepts. All chapters include real-life examples and exercises to illustrate the topics being covered. The book goes beyond theory to offer practical knowledge of methods to reach solutions to key challenges in the field. This is a valuable resource for researchers, practitioners and graduate students interested in developing smart, reliable and sustainable biogas technologies. Provides an applied approach to biogas systems, from technology fundamentals, to economic and environmental assessment Explores control methods and reliability prediction of each system component, including modeling and simulation with HOMER and MATLAB Discusses the use of Big Data analysis, numerical methods, and Game Theory for plant assessment