Big Data Application in Power Systems

Reza Arghandeh, Yuxun Zhou

• Publisher: Elsevier Science
• Year: 2017
• eBook ISBN: 9780128119693
• Topic: Physical Sciences
• Subtopic: Energy

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

Section 1

Harness the Big Data From Power Systems

Chapter 1

A Holistic Approach to Becoming a Data-Driven Utility

John D. McDonald GE Energy Connections-Grid Solutions, Atlanta, GA, United States

Chapter Overview

The ultimate goal of harnessing big data is to improve customer service and achieve enterprise business goals while increasing the reliability, resiliency, and efficiency of operations. Thus, business drivers should dictate data needs and the technology roadmap to achieve ongoing improvements in these areas. A data-driven utility should first identify its fundamental business drivers to understand precisely what intelligence is needed for operations and the enterprise and what specific technology supports the creation of intelligence and value, both for current business challenges and for future business needs and technology functionalities. Intelligence, and automation, relies on a two-way, integrated communication system based on standards; thus a utility must first develop a “strong” grid by establishing an information and communications technology foundation based on an open architecture and standards. This first step requires that information technology and communications groups work together to understand and support the functional requirements such as network response requirements, bandwidth, and latency, of each disparate data path—from sensor to end user—for current and future systems and applications. Then a data-driven utility should develop a “smart” grid, which requires the convergence of information technology and operations technology and their respective staffs—the beginning of an operations- and enterprise-wide cultural shift to holistic utility management that focuses on value creation and eliminates organizational silos. On the technology side, integration of data-producing devices and systems precedes automation. Determining substation automation applications relies on observing the behavior of data over time (daily, seasonally) and diverse conditions (weather patterns). On the organization side, all operations and enterprise groups should cooperate to identify their data needs to create a data requirements matrix. Information and operations technology personnel can then determine the least number of platforms and the most efficient paths to route data from device to end user, taking security into account. Access and authentication rules ensure that only the right person gets the right data at the right time. A key concept in a data-driven utility is that every internal stakeholder who can create value from data should have secure access to that data. Operational data is routed to the control center in real time, while nonoperational data is extracted from intelligent electronic devices, concentrated and sent across the operations firewall to be stored and processed in a data mart for on-demand access by enterprise groups and their applications. Three case studies illustrate the value of a data-driven utility in terms of asset management and safety, the fundamentals of standards and interoperability, and the enterprise value, in dollars, of increased visibility into the transmission and distribution network.

Keywords

Data-driven utility; Holistic approach; Operational data; Nonoperational data; Business drivers; Information and communication technology (ICT); Distribution automation (DA); Substation automation (SA); Advanced metering infrastructure (AMI); Intelligent electronic device (IED); IED template; Data map; Data requirements matrix; Virtual data mart; Functional data paths; SCADA; Network response requirements; Dashboard

1 Introduction

In this digital age, power utilities must harness data to achieve the operational and enterprise efficiencies, insights, and flexibility to thrive amid emerging technologies and disruptive market forces. The question is not whether to become a data-driven utility, but how to do so. The opportunities and challenges are many. In the simplest terms, harnessing data in a comprehensive manner will require a transformational journey that will remake every power utility that undertakes the challenge. The process of becoming a data-driven utility requires a fundamental shift in organizational culture and business processes as well as data-related technology and practices. The desired result is not limited to the creation of a more reliable, resilient, and efficient grid. This transformation should also enable enterprise flexibility that supports new utility business models. Becoming a data-driven utility is an endeavor in which philosophy and technology go hand in hand.

The philosophy piece is simple and three-fold. First, data should drive improvements in a power utility's raison d’être. The ultimate, traditional goal of a power utility is to serve customers by delivering power safely, efficiently, and affordably. We are likely to see this fundamental mandate broaden to include customer service options, enabled by data. Harnessing data can support improvements in customer service, enhance customer and stakeholder value and increase the reliability, resiliency, and efficiency of operations. This is true whether a utility is cooperatively owned, municipally owned, or investor owned. Second, the organizational and technological transformations required to become a data-driven utility are so far-reaching that only a holistic approach will serve. Third, and most broadly, current and near-term societal and market trends pose a challenge to utilities’ historic, regulated monopoly business and regulatory model. If a utility wants to determine its own fate, it must be proactive. Data is the new enabler of value and its opportunities and challenges must be actively embraced with a sense of urgency.

2 Aligning Internal and External Stakeholders

One fundamental concept in becoming a data-driven utility is that every internal stakeholder who can create value from data should have secure and timely access to that data. The very process of identifying useful data, collecting, processing, and presenting it or making it accessible on-demand will drive cultural and business process change throughout a utility. Creating a data-driven utility requires cooperation and coordination across all operational and enterprise units and the recognition that silos are obsolete legacies of past practices. One should not underestimate the fundamental transformation unleashed by pursuing the goal of becoming a data-driven utility.

This observation holds true for external stakeholders as well. On the customer side, data has also become a valuable commodity. Customers are no longer passive ratepayers. Their energy use data belongs to them and, increasingly, they expect value for it. Public utility commissions recognize that customers own their energy use data, that utilities must secure it, and that the individual customer has the prerogative to say how that customer-specific data is used or shared. Whether utilities use data to create service options with value to both utility and customer may well determine their future success as an enterprise. Today, emerging technologies, third parties, and disruptive market forces abound, seeking to provide utility customers with value and service options based on their energy use data. For utilities, data has become not only the means to thrive but also the means to survive.

3 Taking a Holistic Approach

A holistic, methodical approach to becoming a data-driven utility has several common, recognizable steps, though the outcome for any individual utility will likely be unique, due to its existing customer base, business model, and legacy infrastructure. In this introductory chapter and overview of the topic, we will examine the implications of a holistic approach, the technology-related phases it requires, and connect the dots between data-producing sensor and data-reliant end user. A brief synopsis of three case studies will illustrate many of these points.

A holistic approach to becoming a data-driven utility literally takes everything into account. It views transmission and distribution as a single integrated entity. It encompasses the operations and business of delivering power to customers in a manner that achieves customer engagement and satisfaction based on increased system reliability, resiliency, and efficiency. Built on a foundation of open architecture and standards, a holistic approach ensures interoperability between devices, systems, and databases. It enables value creation at operational and enterprise levels. It enables forward and backward compatibility to derive full value from current and future investments in technology while maintaining the value of legacy equipment. In terms of an end-to-end system, a holistic approach provides a means by which all data-producing devices—increasingly, nearly every device in a T&D system—can be mapped to communication channels and networks with the appropriate response requirements, routed to both operations and enterprise sides of the organization, and presented and/or made accessible on-demand to the right people in the right time and place for value creation.

A holistic approach aligns customer needs and expectations with utility business drivers and depends on a technology roadmap for grid modernization that supports this alignment. In terms of utility culture and organization, a holistic approach eliminates silos and demands utility-wide cooperation and coordination to avoid redundant systems and costs. Thus it provides the basis for prudent, well-vetted investments that will create customer and stakeholder value and benefits that increase over time, meet future needs, and are likely to win regulatory approval.

In an era in which the utility business model requires review and transformation and digital technology produces an increasing granularity, quality, and quantity of data, a holistic approach to becoming a data-driven utility offers the richest opportunity for success.

4 “Strong” First, Then “Smart”

Aligning customer needs and expectations with utility operational and business drivers should dictate how data is generated, collected, stored, processed, presented, or accessed, and how actionable intelligence is applied. A data-driven utility should review its current and mid-term operational and business models and identify its customer needs and fundamental business drivers. This will help in understanding precisely what actionable intelligence—and, thus, data—is needed for both operations and the enterprise to meet its self-determined goals of improving customer service and pursuing value creation.

To optimize current practices and enable future flexibility in reaching operations and enterprise goals, a utility must first develop a “strong” grid before pursuing a “smart” grid. This can only be achieved by establishing an information and communications technology (ICT) foundation based on open architecture and industry standards. The development of operational intelligence (and automation) and enterprise value relies on a two-way, standards-based, integrated communication system [1].

This first step requires that information technology (IT) and communications groups work together to understand and support the functional requirements (response requirements, bandwidth, latency) of each disparate data path—from sensor to end user—for current and future systems and applications. This approach requires organization-wide cooperation, which is no small feat. Enabling this fundamental cultural shift requires executive leadership, potentially third-party facilitation, and incentives that reward personnel for organization-wide and customer value creation rather than for individual staff and bailiwick-level achievements.

A foundational ICT platform that links all operational and enterprise aspects of a utility is a prerequisi...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• About the Editors
• Preface: Objective and Overview of the Book
• Acknowledgments
• Section 1: Harness the Big Data From Power Systems
• Section 2: Harness the Power of Big data
• Section 3: Put the Power of Big Data into Power Systems
• Index