Timely analysis of Big Data a success differentiator for utilities
The need for the electric utilities to manage the supply and delivery of electricity has resulted in massive investment for new technology. This technology creates copious data volumes, variety of types and sources which combined is often referred to as “Big Data.” Clearly, these investments can lead directly to tangible benefits as expected, but electric utility big data offer substantially greater benefits. The industry’s ability to realize these benefits is still in its infancy. Smart meter deployment has shortened the utility’s billing cycle and improved billing accuracy; however, the bulk of the smart meter data stays within the meter-to-cash process as yet unavailable to others who could benefit.
Similarly, investments for improving grid monitoring by deploying phase-angle measurement units provide 30 to 100 samples per second of highly precise data – while existing SCADA systems collects samples at only 2 to 30 second intervals. Traditional SCADA systems assume all the measurements within an interval are time-aligned while phase angle measurement units precisely mark every sample with highly accurate time stamps for strict alignment and exact chronology. Similarly, smart meters identify electricity usage to each specific time interval. The time-stamp for each individual piece of data in the immense amount of data produced is the key to the electric industry’s ability to extract benefit from big data across their enterprises. In the explosion of utility data, time becomes the common characteristic of disparate data.
Utilities, for the most part, have maintained and analyzed their data in separate silos, each focusing on decision making targeted for their specific requirements, such as the operational decisions for control center or commercial decisions for customer service. As such, only small amounts of data move from one area to another. With the emergence of time-stamped data and the availability of cost-effective computer technology to store and manage it, utilities can, for the first time, accurately time align their data to extract new value. While the potential benefits are enormous, the industry has thus far only scratched the surface. For example, outage management systems can now integrate meter “last-gasp” notifications so the outage management application registers an outage before the customer has time to report it.
As more of the data becomes time-aligned, another emerging opportunity is to improve electricity demand forecasting. Historically, electricity demand forecasts look at key factors such as day of week, day of year, forecasted weather, and long-term demand growth. Utilities adjust their near-term, calculated forecasts to insure adequacy of supply should demand exceed expectations. These adjustments force some generation to run below optimum economic efficiency but guarantee enough generators in operation to allow rapid response to an unexpected increase in demand. Today, the industry can use highly accurate interval meter readings to augment and improve the quality of these demand forecasts, thus saving utilities fuel costs and equipment maintenance. Beyond improvements in the accuracy of the utility-wide demand forecast, these forecasts become very granular, identifying the specific locations for electrical demand fluctuations.
This information will eventually become part of the input into broader solutions to optimize the grid overall. By coupling highly accurate and granular demand forecasts with a detailed understanding of the equipment in the grid along with its history, reliability increases and costs fall. The data from smart meters and phase angle measurement units when integrated with asset data allows for improved grid reconfiguration, more efficient operations. It also enables a clear focus on maintenance of the equipment that will most likely fail or that which would have the most significant impact to critical or large numbers of customers if it did. In the future, as more of the data from additional data sources is time-aligned, timely analysis of big data will become the way of life for the successful utility.