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Big Data Analytics Strategies for the Smart Grid —Amit Narayan, Phd, CEO, Autogrid Analytics Strategies

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Big Data Analytics Strategies for the Smart Grid —Amit Narayan, Phd, CEO, Autogrid Analytics Strategies Stimmel Power Engineering / Data Mining and Knowledge Discovery This book provides an in-depth analysis that will help utility executives, as well as regu- Big Data lators, investors, large power users, and entrepreneurs, understand some of the tectonic 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. Grid Smart the for Strategies Analytics Data Big —Amit Narayan, PhD, CEO, AutoGrid Analytics Strategies After more than a century providing a mission-critical resource to consumers around the world, traditional energy providers are realizing the power of big data and predic- for the Smart Grid tive analytics ... In her exceptional book, Carol examines these trends and breaks down very complex topics into prose that is easy to understand. I highly recommend this book to anyone in the energy industry looking to grow and evolve their business. Carol L. Stimmel —Adrian Tuck, CEO, Tendril Carol Stimmel defines utility data analytics as the application of techniques within the digital energy ecosystem that are designed to reveal insights that help explain, predict, and expose hidden opportunities to improve operational and business efficiency and to deliver real-world situational awareness. ... Volume, velocity, variety, and value— the characteristics ascribed to ‘big data’—will aptly characterize the reader’s and practitioner’s view of Ms. Stimmel’s book. —Ivo Steklac, GM Residential & Commercial Energy Solutions, SunPower Corporation The author has done an excellent job of leveraging her experience in the industry and her strong technical background to create a book that is a very easy-to-read, useful tool for anyone trying to get started in applying big data analytics to the utility industry. She not only provides the reader with a solid base knowledge and background but provides solid examples of how data analytics can be applied within a utility environment and the advantages that can be gained by doing so. —Ron Gerrans, CEO, Genus Zero and former CEO, E Source Carol Stimmel is also the author of the forthcoming book from CRC Press: Building Smart Cities: Analytics, ICT, and Design Thinking K22140 6000 Broken Sound Parkway, NW Suite 300, Boca Raton, FL 33487 ISBN: 978-1-4822-1828-2 711 Third Avenue New York, NY 10017 90000 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. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, includ- ing photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. 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 x Big Data Analytics Strategies for the Smart Grid 6.6 Grid Management 100 6.6.1 The Relationship Between Standards and Analytics 102 6.7 Resiliency Analytics 102 6.8 Extracting Value from Operational Data Analytics 104 Chapter Seven: Customer Operations and Engagement Analytics 107 7.1 Chapter Goal 107 7.2 Increasing Customer Value 108 7.2.1 Customer Service 108 7.2.2 Advanced Customer Segmentation 109 7.2.3 Sentiment Analysis 110 7.2.4 Revenue Collections 112 7.2.5 Call Center Operations 113 7.2.6 Utility Communications 114 7.3 What’s in It for the Customer? 117 7.3.1 Enhanced Billing and Customer-Facing Web Portals 118 7.3.2 Home Energy Management 120 7.3.3 Strategic Value 121 Chapter Eight: Analytics for Cybersecurity 123 8.1 Chapter Goal 123 8.2 Cybersecurity in the Utility Industry 124 8.2.1 The Threat Against Critical Infrastructure 124 8.2.2 How the Smart Grid Increases Risk 127 8.2.3 The Smart Grid as Opportunity for Dark Mischief 128 8.3 The Role of Big Data Cybersecurity Analytics 129 8.3.1 Predict and Protect 131 8.3.2 Cybersecurity Applications 133 8.3.3 Proactive Approaches 134 8.3.4 Global Action for Coordinated Cybersecurity 134 8.3.5 The Changing Landscape of Risk 136 Contents xi Section Three Implementing Data Analytics Programs for Sustained Change Chapter Nine: Sourcing Data 141 9.1 Chapter Goal 141 9.2 Sourcing the Data 142 9.2.1 Smart Meters 143 9.2.2 Sensors 145 9.2.3 Control Devices 146 9.2.4 Intelligent Electronic Devices 147 9.2.5 Distributed Energy Resources 148 9.2.6 Consumer Devices 149 9.2.7 Historical Data 150 9.2.8 Third-Party Data 151 9.3 Working with a Variety of Data Sources 152 9.3.1 Data Fusion 152 Chapter Ten: Big Data Integration, Frameworks, and Databases
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