Default enrollment for time-varying rates, with an opt-out, will reduce peak demand and far more than a default flat rate with a TVR opt-in.
Intelligent networks support better decision making.
applicable for operations purposes—primarily in its retail business unit for billing purposes.
– Lack of advanced analytics: Even if utilities do have their data in order, they might not be able to analyze all of the data in a real-time situation. Utilities do have some real-time applications in use today, but most are not ready to handle the complex needs of an intelligent grid. For example, control applications, such as SCADA, EMS, and DCS, have not advanced quickly enough to incorporate factors like consumer behaviors or weather information to provide useful real-time analytics to utilities.
Opportunities for Change
Some utilities are finding that the intelligent grid provides a way to overcome these challenges. As utilities gain more information—particularly about their distribution networks and end users—through intelligent grid initiatives, they now have more information to feed into their decisions. The key issue now is how utilities collect, sort, and analyze this information to make better decisions. Some utilities are taking advantage of new technologies to help them improve their data availability and analytics capabilities.
Utilities that are organizing their data have two main approaches for organizing data: putting the data in a central repository or building better connections between different data sources. Some utilities are working to bring their disparate data sources into a common format and database. For example, a large investor-owned utility (IOU) in the western United States had energy delivery data scattered across its offices, with many engineers maintaining their own personal databases. As the company took on more proactive maintenance practices, it realized that its employees needed to access data remotely and understand asset trends across the dispersed service territory. As a result the utility brought all of its operations data into one central database of time-series data.
Getting information to work together does not necessarily require a utility to pull all data into one database. Instead, utilities are developing other ways to begin connecting disparate systems, including an integration bus model, service-oriented architecture (SOA), cloud computing, or a mashup of technologies. For example, The California ISO uses an SOA solution to better display and analyze spatial information. These efforts include overlaying GIS data on satellite imagery and overlaying time-series data on GIS. The system can visualize the grid, substations, generators, nuclear power plants, and wind farms as well as mash up weather data and forecasted demand supply. Google Earth is used to visualize the real-time analytics.
Analyzing and Interpreting The Data
As utilities get their data in order, the question then becomes how to make use of the data. Some utilities are taking advantage of opportunities to make better use of the data.
One way they’re doing this is by applying more advanced dashboards. Green, yellow, and red aren’t just for traffic lights anymore. In the current intelligent grid analytics market, dashboards—geared toward everyone from the field crews to senior executives—provide reporting tools that calculate and consolidate metrics on a single screen to allow employees to easily monitor information. Metrics can include anything from transformer and substation status to SAIDI, CAIDI, and SAIFI indexes. For example, one U.S.