BIG DATA ANALYTICS: IMPACTS ON AMERICAN ELECTRIC UTILITIES

Transcription

1 BIG DATA ANALYTICS: IMPACTS ON AMERICAN ELECTRIC UTILITIES Beth-Anne Schuelke-Leech, Matteo Muratori, Betsy Barry, B.J. Yurkovich, Giorgio Rizzoni CENTER FOR AUTOMOTIVE RESEARCH

2 SMART GRID SMART GRID: class of technologies used to bring utility electricity delivery systems into the 21 st century, using computer-based remote control, and communication (U.S. DOE). From: IEEE

3 BIG DATA AND SOFT GRID Soft grid: The supporting architecture, substructure, and IT solutions that makes possible for the smart grid to properly function, communicate, and coordinate. Big data: A collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. Big encompasses the volume, the diversity of data, and the speed with which the data are generated. Big data for electric utilities: Data (generation, transmission, distribution, consumption); smart grid sensors; customers information; internal and external documentation, including regulatory compliance and service quality.

4 GTM research identifies three main domains for electric utilities analytics: Enterprise Analytics, including real-time predictive analytics and complete situational awareness. Grid Operation Analytics, including asset, outages, and crisis management, fault detection, and energy theft control programs. Consumer Analytics, including behavioral analytics, time-varying electricity pricing, building energy management, social media data integration, microgrid analytics, and load control and management. There is more to this, all the components need to integrated in a dynamic complex framework. There are not little pockets of data, it is a massive amount of interconnected data.

5 BIG DATA AND SOFT GRID A network of solutions (i.e. smart grid, big data analytics) is necessary to achieve overall effectiveness. Policy Legal, regulatory, and corporate environment Economics & Markets Security, privacy, and customer participation, and demand management Technology Smart grid and soft grid deployment and implementation

6 PRESENTATION STRUCTURE Focus of this presentation is on: 1. How big data impact smart grid development: modeling and simulations. This includes forecasting, use of information collected, and evaluation of different technology adoption. 2. How do utilities manage the data collected and deal with data management, privacy, and security issues. 3. How utilities deal with regulatory and compliance issues: text-based data.

7 DATA FROM SMART GRID TECHNOLOGIES Data collected from smart grid technologies are numeric (e.g. power quality data, customer usage data, and electrical production data) Used to: Understand customer consumption patterns (both individual and aggregate); Energy production and demand management; Modeling, simulating and forecasting. To effectively deploy and use resources, modeling and simulations tools are required to: Implement the right technology; Address problems before deploying solutions in the field; explore a multitude of different solutions; capture the unintended consequences of many systems interacting with one another.

8 MODELING, SIMULATION, AND SMART GRID DEVELOPMENT Simulate a technical-social-economic system: interaction and behavior changes are to considered. Assumptions are going to critically influence the outcome. big data helps in guiding smart grid deployment and model calibration and validation Leveraging Big Data presents multiple technological and management challenges to electric utilities to accommodate new information into a complex existing system, including: Integrate new aspects into a regulated, reliable, and well established system (utilities are followers, not early adopters); Effective, reliable, and standardized communication protocols; Ensure security and reliability of electrical system and the data; Protect customer privacy while still capitalizing on their data.

9 LEGAL, REGULATORY, AND CORPORATE ENVIRONMENT POLICY LAYER: LEGAL, REGULATORY, AND CORPORATE POLICIES Utilities are constrained by regulatory compliance issues and document retention A significant amount of data affecting electric utilities is text-based (e.g. customer complains, contracts, regulations), as opposed to numerical data Analytical techniques are different: we cannot just apply a mathematical model or computer algorithms, human analysis is also needed Corporate strategy: data are not neutral, personal interpretation may pose a threat to organizational strategies and regulatory compliance

10 Global Utility Analytics Spending, From the Soft Grid : Big Data & Utility Analytics for Smart Grid

11 CONCLUSIONS Like deploying smart grids, big data analytics is not a panecean solution, per se. Effectiveness can be achieved only by a network of solutions. How utilities structure, analyze, and utilize their data determine how useful data are to them. Utilities are heavily regulated, conservative, and risk-adverse organizations, where reliability is dominant. They typically make capital investment decisions for 20 to 50 years. They are now being asked to integrate new technologies and new ways of doing business that may clash with their operating culture. Data are not neutral. What are the goals and the expectations of electric utilities? There is a cost associated to gathering, storing, and analyzing data. Utilities have to trade off costs with intended benefits.

12 Contact: Matteo Muratori Research Associate Engineer CENTER FOR AUTOMOTIVE RESEARCH