Table Of Content

Power Engineering / Data Mining and Knowledge Discovery S Big Data t i m This book provides an in-depth analysis that will help utility executives, as well as regu- m lators, investors, large power users, and entrepreneurs, understand some of the tectonic e l Analytics Strategies changes coming to an industry that from the outside can seem impervious to change. Making sense of a chaotic future, Carol charts a path where everyone can benefit. B —Amit Narayan, PhD, CEO, AutoGrid i g for the Smart Grid After more than a century providing a mission-critical resource to consumers around D the world, traditional energy providers are realizing the power of big data and predic- a tive analytics ... In her exceptional book, Carol examines these trends and breaks down t very complex topics into prose that is easy to understand. I highly recommend this book a Carol L. Stimmel to anyone in the energy industry looking to grow and evolve their business. A —Adrian Tuck, CEO, Tendril n a Carol Stimmel defines utility data analytics as the application of techniques within the l digital energy ecosystem that are designed to reveal insights that help explain, predict, y and expose hidden opportunities to improve operational and business efficiency and t i to deliver real-world situational awareness. ... Volume, velocity, variety, and value— c s the characteristics ascribed to ‘big data’—will aptly characterize the reader’s and S practitioner’s view of Ms. Stimmel’s book. t —Ivo Steklac, GM Residential & Commercial Energy Solutions, r a SunPower Corporation t e The author has done an excellent job of leveraging her experience in the industry and g her strong technical background to create a book that is a very easy-to-read, useful tool i e for anyone trying to get started in applying big data analytics to the utility industry. She s not only provides the reader with a solid base knowledge and background but provides f o solid examples of how data analytics can be applied within a utility environment and the r advantages that can be gained by doing so. t —Ron Gerrans, CEO, Genus Zero and former CEO, E Source h e Carol Stimmel is also the author of the forthcoming book from CRC Press: S Building Smart Cities: Analytics, ICT, and Design Thinking m a r t G K22140 r 6000 Broken Sound Parkway, NW Suite 300, Boca Raton, FL 33487 ISBN: 978-1-4822-1828-2 i d 711 Third Avenue 90000 New York, NY 10017 an informa business 2 Park Square, Milton Park www.crcpress.com Abingdon, Oxon OX14 4RN, UK 9 781482 218282 www.auerbach-publications.com K22140 cvr mech.indd 1 6/5/14 12:34 PM Big Data Analytics Strategies for the Smart Grid Big Data Analytics Strategies for the Smart Grid Carol L. Stimmel CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2015 by Carol L. Stimmel CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20140611 International Standard Book Number-13: 978-1-4822-1829-9 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. 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Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Dedication To my beloved son Jake—for your infectious enthusiasm, pursuit of new ideas, persistent distrust of the status quo, and brilliant sense of humor. I love you, honey. v Contents Dedication v Contents vii Foreword xv Preface xxi About the Author xxiii Acknowledgments xxv Section One The Transformative Power of Data Analytics Chapter One: Putting the Smarts in the Smart Grid 3 1.1 Chapter Goal 3 1.2 The Imperative for the Data-Driven Utility 4 1.3 Big Data: We’ll Know It When We See It 7 1.4 What Are Data Analytics? 8 1.4.1 The Data Analytics Infrastructure 9 1.5 Starting from Scratch 11 1.5.1 Mind the Gap 12 1.5.2 Culture Shift 12 vii viii Big Data Analytics Strategies for the Smart Grid 1.5.3 A Personal Case Study 13 1.5.4 Ouija Board Economics 15 1.5.5 Business as Usual Is Fatal to the Utility 17 1.5.6 To Be or Not to Be 18 1.6 Finding Opportunity with Smart Grid Data Analytics 19 Chapter Two: Building the Foundation for Data Analytics 21 2.1 Chapter Goal 21 2.2 Perseverance Is the Most Important Tool 22 2.2.1 “It’s Too Hard” Is Not an Answer 23 2.3 Building the Analytical Architecture 23 2.3.1 The Art of Data Management 25 2.3.2 Managing Big Data Is a Big Problem 25 2.3.3 The Truth Won’t Set You Free 25 2.3.4 One Size Doesn’t Fit All 28 2.3.5 Solving the “Situation-Specific” Dilemma 29 2.3.6 The Build-Versus-Buy War Rages On 30 2.3.7 When the Cloud Makes Sense 32 2.3.8 Change Is Danger and Opportunity 35 Chapter Three: Transforming Big Data for High-Value Action 37 3.1 Chapter Goal 37 3.2 The Utility as a Data Company 38 3.2.1 Creating Results with the Pareto Principle 39 3.3 Algorithms 40 3.3.1 The Business of Algorithms 41 3.3.2 Data Classes 41 3.3.3 Just in Time 43 3.4 Seeing Intelligence 44 3.4.1 Remember the Human Being 46 3.4.2 The Problem with Customers 46 3.4.3 The Transformation of the Utility 49 3.4.4 Bigger Is Not Always Better 49 3.5 Assessing the Business Issues 51 3.5.1 Start with a Framework 51 Contents ix Section Two The Benefits of Smart Grid Data Analytics Chapter Four: Applying Analytical Models in the Utility 57 4.1 Chapter Goal 57 4.2 Understanding Analytical Models 58 4.2.1 What Exactly Are Models? 61 4.2.2 Warning: Correlation Still Does Not Imply Causation 62 4.3 Using Descriptive Models for Analytics 63 4.4 Using Diagnostic Models for Analytics 64 4.4.1 How Diagnostic Tools Help Utilities 65 4.5 Predictive Analytics 65 4.6 Prescriptive Analytics 67 4.7 An Optimization Model for the Utility 69 4.8 Toward Situational Intelligence 69 Chapter Five: Enterprise Analytics 73 5.1 Chapter Goal 73 5.2 Moving Beyond Business Intelligence 74 5.2.1 Energy Forecasting 75 5.2.2 Asset Management 75 5.2.3 Demand Response and Energy Analytics 77 5.2.4 Dynamic-Pricing Analytics 84 5.2.5 Revenue-Protection Analytics 87 5.2.6 Breaking Down Functional Barriers 88 Chapter Six: Operational Analytics 91 6.1 Chapter Goal 91 6.2 Aligning the Forces for Improved Decision-Making 92 6.3 The Opportunity for Insight 93 6.3.1 Adaptive Models 94 6.4 Focus on Effectiveness 94 6.4.1 Visualizing the Grid 96 6.5 Distributed Generation Operations: Managing the Mix-Up 98