{"id":32573,"date":"2018-11-13T19:05:31","date_gmt":"2018-11-14T00:05:31","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/turning-big-data-into-clean-electrons-at-nextera\/"},"modified":"2018-11-13T19:05:31","modified_gmt":"2018-11-14T00:05:31","slug":"turning-big-data-into-clean-electrons-at-nextera","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/turning-big-data-into-clean-electrons-at-nextera\/","title":{"rendered":"Turning Big Data into Clean Electrons at NextEra"},"content":{"rendered":"

The Renewable Paradox<\/strong><\/h3>\n

Renewable energy is a victim of its own success. Solar and wind project costs have dropped precipitously in recent years and now regularly outcompete fossil fuels as the least expensive form of energy generation.[1]<\/a>\u00a0However, with this decrease in cost, the industry has become much more competitive and increasingly commoditized, leading to tight margins and high-profile bankruptcies, particularly in the solar project market.[2]<\/a> Furthermore, renewables produce electricity intermittently, making a utility\u2019s job of perfectly matching electricity supply and demand even more difficult. As more renewables are connected to the grid, the intermittency issue grows, and integration becomes increasingly challenging.[3]<\/a> To be successful in this competitive space, renewable energy generators such as NextEra Energy are using machine learning to drive down costs and improve predictability of supply.<\/p>\n

Optimizing Operations<\/strong><\/h3>\n

NextEra claims to produce more electricity from solar and wind resources than any other company in the world.[4]<\/a> This scale provides unprecedented access to operational data, relating the conditions of a solar or wind farm to its energy output. By processing this data in meaningful ways using techniques such as machine learning, the company can reduce costs and increase output, creating sustainable competitive advantage in a challenging field. NextEra is already working to apply two distinct applications of machine learning: optimizing operating parameters and performing predictive maintenance.<\/p>\n

Setting operating parameters for its wind turbine fleet was once a manual process. Now, NextEra uses machine learning to dynamically adjust those parameters based on current conditions and continuously seek to maximize output.[5]<\/a> The results have been outstanding: \u201cWe operate at $3 to $4 [per MWh] better, including availability and operating costs, on the wind side than anyone else in the country.\u201d[6]<\/a> With a fleet worth billions and slim margins, even small improvements in performance can create significant returns.<\/p>\n

Predictive maintenance allows the operator to more accurately assess when an asset needs to be serviced, reducing maintenance costs and equipment outages. Here, machine learning supports the engineering team, according to the general manager of NextEra\u2019s Advanced Data Systems team: \u201cin the past [we] solely relied upon engineering knowledge to detect problems.\u2026Machine learning recognizes patterns and anomalies within the data that may not be as easy for engineers to see.\u201d[7]<\/a> As a result, the company can perform maintenance based on the condition of the equipment, rather than at specified time intervals, meaning they can \u201cstretch out the maintenance until the equipment needs it.\u201d[8]<\/a><\/p>\n

In the longer term, NextEra can use data to select the best sites for wind and solar projects, as with its purchase of wind forecasting company WindLogics.[9]<\/a> Supporting that capability is a major area of machine learning research into forecasting solar and wind output using weather and other environmental data. A recent paper uses one such technique to predict wind turbine output with error of 1-5% over timespans ranging from an hour to a year in advance.[10]<\/a> With improved forecasting, NextEra will be able to accurately bid on new projects and optimize performance of its existing assets.<\/p>\n

A Clean Energy Future?<\/h3>\n

Looking forward, machine learning presents many more opportunities to generate further competitive advantage for the company. For example, forecasting algorithms can help Florida Power & Light, a subsidiary, better manage electricity generation. A more accurate forecast of renewable power generation would allow the utility to minimize its generation reserve, thereby reducing costs. On the project development side, a startup called PowerScout claims to have developed software that predicts consumer uptake of solar energy, making it easier for companies to market to the best prospects.[11]<\/a> NextEra could use a similar concept to predict demand for its new solar or wind projects, perhaps by anticipating where utilities will need additional generation or storage and accounting for characteristics of potential sites.<\/p>\n

However, the role of machine learning in creating sustainable competitive advantage for NextEra is unclear. As more operators realize the value of big data in managing their operations, will NextEra be able to sustain its advantageous cost position, or will others be able to reach the same level? More generally, will NextEra\u2019s data advantage be enough to maintain its historically strong margins in a time of intense competitive pressure? (713 words)<\/p>\n

References<\/h3>\n

[1]<\/a>\u00a0Silvio Marcacci, “Cheap Renewables Keep Pushing Fossil Fuels Further Away From Profitability – Despite Trump’s Efforts,”\u00a0Forbes<\/em>, January 23, 2018,\u00a0https:\/\/www.forbes.com\/sites\/energyinnovation\/2018\/01\/23\/cheap-renewables-keep-pushing-fossil-fuels-further-away-from-profitability-despite-trumps-efforts, accessed November 2018.<\/span><\/p>\n

[2]<\/a>\u00a0David Frankel, Aaron Perrine, and Dickon Pinner, “How solar energy can (finally) create value,”\u00a0McKinsey Insights<\/em>, October 2016,\u00a0https:\/\/www.mckinsey.com\/business-functions\/sustainability-and-resource-productivity\/our-insights\/how-solar-energy-can-finally-create-value, accessed November 2018.<\/p>\n

[3]<\/a>\u00a0Kasun S. Perera, Zeyar Aung, and Wei Lee Woon, “Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey,”\u00a0International Workshop on Data Analytics for Renewable Energy Integration<\/em>: 96, Springer, accessed November 2018.<\/p>\n

[4]<\/a>\u00a0NextEra Energy, Inc., “Renewable Energy,”\u00a0http:\/\/www.nexteraenergy.com\/sustainability\/environment\/renewable-energy.html, accessed November 2018.<\/p>\n

[5]<\/a>\u00a0Stevan Jovanovic, “Innovation & Digitalization in Energy,” speech given at HBS Energy & Environment Symposium, 性视界 Business School, Boston, MA, November 3, 2018.<\/p>\n

[6]<\/a>\u00a0Jim Robo, CEO, remarks made at\u00a02018 Wolfe Research Utilities & Energy Conference, New York, NY, October 2-3, 2018. From transcript provided by SeekingAlpha,\u00a0https:\/\/seekingalpha.com\/article\/4209709-nextera-energy-inc-nee-ceo-jim-robo-2018-wolfe-research-utilities-and-energy-conference?page=4<\/p>\n

[7]<\/a>\u00a0“Predicting the future of generation,”\u00a0Energy Now<\/em>, January 31, 2017, http:\/\/www.nexteraenergy.com\/energynow\/2017\/0117\/0117_PredictiveAnalytics.shtml, accessed November 2018.<\/span><\/p>\n

[8]<\/a> Ibid<\/p>\n

[9]<\/a>\u00a0Russell Gold, “How a Florida Utility Became the Global King of Green Power,”\u00a0The Wall Street Journal<\/em>, June 18, 2018, https:\/\/www.wsj.com\/articles\/how-a-florida-utility-became-the-global-king-of-green-power-1529331001, accessed November 2018.<\/span><\/p>\n

[10]<\/a> Gul Muhammad Khan, Jawad Ali, and Sahibzada Ali Mahmud, “Wind power forecasting\u2014An application of machine learning in renewable energy,” 2014 International Joint Conference on Neural Networks (IJCNN)<\/em>: 1137, IEEE, accessed November 2018.<\/p>\n

[11]<\/a> “PowerScout Brings Machine Learning to Consumer Energy Market,” PowerScout press release (Oakland, CA, September 14, 2016).<\/span><\/p>\n

Featured image: Stephanie Sawyer, Getty Images,\u00a0https:\/\/fortunedotcom.files.wordpress.com\/2017\/12\/gettyimages-518650463.jpg, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

As prices drop and competition intensifies in the world of solar and wind, can generators use machine learning to maintain healthy margins?<\/p>\n","protected":false},"author":11274,"featured_media":35127,"comment_status":"open","ping_status":"closed","template":"","categories":[381,95,1479,1764,2010,185,87,14],"class_list":["post-32573","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-cleantech","category-energy","category-industrials","category-nextera","category-power-generation","category-renewable-energy","category-renewables","category-sustainability","hck-taxonomy-organization-nextera","hck-taxonomy-industry-energy","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nTurning Big Data into Clean Electrons at NextEra - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/turning-big-data-into-clean-electrons-at-nextera\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Turning Big Data into Clean Electrons at NextEra - 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