{"id":8046,"date":"2018-04-09T17:58:04","date_gmt":"2018-04-09T21:58:04","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/data-science-for-your-local-utility-company\/"},"modified":"2018-04-09T17:58:04","modified_gmt":"2018-04-09T21:58:04","slug":"data-science-and-your-local-utility-company","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/data-science-and-your-local-utility-company\/","title":{"rendered":"Data Science and Your Local Utility Company"},"content":{"rendered":"

Primer.<\/strong><\/p>\n

Please watch this 2-minute video for an overview:\u00a0 https:\/\/www.youtube.com\/watch?v=pdrQDOhjF24<\/a><\/p>\n

Introduction.<\/strong><\/p>\n

Duke Energy is a North Carolina based energy holding company that services 7.2 million customers across the United States and operates more than 250,000+ miles of distribution lines across 104,000 square miles.[i]<\/a>\u00a0 \u00a0Despite being part of \u201can inherently slow-moving sector,\u201d Duke Energy is among many utility companies reaping the benefits of data science.[ii]<\/a><\/p>\n

Journey.<\/strong><\/p>\n

Since 1904, Duke Energy has operated hydroelectric, nuclear, coal-fired, and natural gas turbine electric plants.[iii]<\/a>\u00a0 In recent history, the rising cost of maintaining the United States\u2019 aging grid system and the threat of distributed generation and more energy efficient customers has threatened utility company profits, beginning what some experts call the \u201cutility death spiral.\u201d\u00a0 The death spiral theory predicts that as more and more customers move to solar and other forms of distributed power generation, fewer people will be on the grid, which will decrease the number of people that utilities can distribute their costs to, which increases the price of energy to consumers on the grid, which then incentivizes more customers to move off the grid, continuing the downward spiral.\u00a0 As a result, utility companies, like Duke Energy, are looking to cut costs and increase revenue.<\/p>\n

It was in this context, that Duke Energy sponsored the Data Modeling and Analytics Initiative (DMAI) in 2012.\u00a0 In this forum , vendors were invited to analyze a sample data set and provide \u201copportunities and insights\u201d for inclusion in Duke Energy\u2019s \u201cbig data analytics strategy.\u201d[iv]<\/a>\u00a0 Insights from the DMAI included meter and customer analytics, distribution grid analysis, and security, as well as estimated values for reducing operational expenses, increasing revenues, and customer satisfaction (See Exhibit A).[v]<\/a><\/p>\n

Exhibit A. Summary of Use Cases From DMAI<\/strong>[vi]<\/a><\/p>\n

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After DMAI, Duke Energy identified the following: (1) there was significant value potential in applying data science across its business, (2) internal data needed to be cleaned better, (3) specifications for overall data collection architecture were needed, and (4) Duke needed data science resource and skills.\u00a0 Duke Energy now had the confidence to pursue a \u201cbig data\u201d strategy (See Exhibits B and C).<\/p>\n

Exhibit B. Data Modeling and Analytics Framework [vii]<\/a><\/strong><\/p>\n

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\u00a0Exhibit C. Data Flow Architecture [viii]<\/a><\/strong><\/p>\n

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Value Creation.<\/strong><\/p>\n

Duke Energy creates value by integrating traditionally siloed datasets and streaming data live from field sensors.[ix]<\/a>\u00a0 Using a team of data scientists at its Analytics Competency Center, Duke Energy is able to not only look at what had happened or what is happening, but is also able to make predictions about the future.\u00a0 Duke Energy is able to translate insights from meter to distribution to generation into the following improvements (See Exhibit D): [x]<\/a><\/p>\n

    \n
  1. Reduce power outages<\/li>\n
  2. Reducing power outage time<\/li>\n
  3. Predictive maintenance<\/li>\n
  4. Equipment replacement timing<\/li>\n
  5. Energy utilization modeling<\/li>\n
  6. Risk identification<\/li>\n
  7. Theft identification<\/li>\n
  8. Sever weather impact predictions<\/li>\n
  9. Failing meter identification<\/li>\n
  10. Automated service dispatch<\/li>\n
  11. Improved billing<\/li>\n
  12. Lower call center volumes<\/li>\n
  13. Customer segmentation and profiling<\/li>\n
  14. Improved customer satisfaction<\/li>\n
  15. Improved customer engagement<\/li>\n<\/ol>\n

    Exhibit D. Value Creation Example: Improving Power Outage Prediction by Using Additional Variables<\/strong>[xi]<\/a><\/p>\n

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    Value Capture.<\/strong><\/p>\n

    As discussed above, Duke Energy\u2019s data science initiative creates significant value that is captured in the form of lower costs and new revenue opportunities.\u00a0 Duke Energy shares this value capture with customers in the form of cheaper, more reliable energy.<\/p>\n

      \n
    1. Cost reduction.<\/em> Duke energy has saved millions of dollars using the insights highlighted above.\u00a0 Risk identification and equipment replacement time prediction has improved capital investments and equipment failure prediction has reduced maintenance costs.<\/li>\n
    2. Revenue Opportunities.<\/em> Identifying energy theft and failing meters and improving billing has allowed Duke Energy to recover millions of dollars in revenue.\u00a0 Duke Energy can also offer new products and services to different customer segments, including \u201cdemand-side management programs that reduce electricity use at peak times.\u201d [xii]<\/a><\/li>\n<\/ol>\n

      Challenges.<\/strong><\/p>\n

      As Duke Energy embarked upon its big data journey, it had to overcome several obstacles, including:<\/p>\n