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San Antonio’s electric utility making big data analytics the business of the people, for the people

  1. San Antonio’s Electric Utility Making Big Data Analytics the Business of the People …for the People J U N E 17 - 21, 2018 B y: R olando Vega, Ph.D ., P.E. Manager of analy tic s and bus ines s ins ight
  2. AGENDA 1. Notions of scale and time in Texas utility industry (VIDEO) 2. The making of a market – protocols and people’s role 3. Observations on the finality of Technology 4. CPS Energy “People First” philosophy 5. A turning point in Machine Learning / Artificial Intelligence 6. New market opportunities 7. Big data analytics @ CPS Energy
  3. Scale & time notions in utility big data analytics
  4. Some considerations for ERCOT market protocols • energy scheduling and dispatch • ancillary services • congestion management • outage coordination • settlement and billing • metering • data acquisition and aggregation • market information systems • transmission and distribution losses • renewable energy credit trading • registration and qualification • market data collection • load profiling and alternative dispute resolution 4
  5. Technology Finality is… by and for people • Technology investment decisions in electric utilities are made by and for people with a keen eye for creating value for its customers. Element
  6. Big Data Analytic System Dimensions • System components: – Hardware/Software – Data – Methods – Intangible components, such as processes, relationships, company policies, information flows, interpersonal interactions, and internal states of mind such as feelings, values and beliefs.
  7. Perspectives: Genesis of Big Data Analytic Systems Events Patterns Structure State of Mind React to snapshot Understand/Adapt changes in events over time Design/Predict causal connections Transform the system
  8. People in Big Data Analytic System Dimensions Events Patterns Structure State of Mind
  9. Events Patterns Structure State of Mind People in Big Data Analytic System Dimensions
  10. Events Patterns Structure State of Mind People in Big Data Analytic System Dimensions
  11. Events Patterns Structure State of Mind People in Big Data Analytic System Dimensions
  12. CPS Energy • What moves the electric utility of the 7th largest city in the U.S.? The answer is, People. • For years, CPS Energy has invested in development of local talent, local technology development, city growth, its employees and an asset infrastructure that is setting the stage for continued success.
  13. A turning point for Machine Learning • Open-source software has helped machine learning mature passed the point of academic research and inflated expectations. • Faster and more transparent technology deployment now possible • Machine learning techniques could be conveniently trained and deployed to predict more optimal solutions • But ONLY IF data inputs behave within reasonable range and with normal variability.
  14. New market opportunities 14
  15. Big data analytics enablement at CPS Energy • San Antonio’s electric utility big data enablement platform effort started in 2016. • We anticipate to have production-ready environment in June 2018. • HDFS used for data storage and Spark/BigDL+Tensorflow for advanced analytics • 2018 year to demonstrate what is possible • 2019+ time to scale predictive analytic technologies • Hybrid integration platform to accelerate API technology adoption production ready June 2018. • In process to implement data governance program.
  16. THANK YOU 16

Notes de l'éditeur

  1. After having matched the space and time progression, a point needs to be made about how a utility matures into adopting new technologies…in an effort to illustrate whre many of the dat leaders should put their attention in participating in some of the groups that create the protocols that dictate what we adopt and do as an industry.
  2. People is not only in the input and output…people is also into the system dimensions. Power plant goes into a forced outage, plant operators communicate and go to investigate what happened and fix the problem ASAP, while customer in Market Operations is trying to find second best alternative
  3. Power plant operators collect sufficient information and map all events into a common axis to contrast with all other forced outages quantitatively, and realize that there is a commonality with all events, temperature in boilers changes at a rate faster than 100 F in 15-minutes in all events.
  4. Power Plant operators note that generally the rate of temperature change is only exceeded when the turbine is not given maintenance in more than 3-months and therefore communicate with market operations and design a process where all plants will undergo maintenance based on signals from temperature rate of change and when certain threshold is reached.
  5. Power plant and market operation managers start discussions about whether solving this problem is the sustainable solution since the plant has generated more financial losses than gains in the last 2-years and there might be a different market this power plant may serve that does not expose the unit to fail.