Aging grid assets, demanding customers and increasingly complex regulatory requirements (designed to protect the environment while ensuring the security and privacy of customers) present new and unique challenges to the utilities industry. Throw in the added complexity of distributed generation sources (wind, solar and more) and the difficulties associated with distributing reliable energy to consumers at a reasonable price, and the tasks at hand can seem rather daunting.
In response to these challenges, utilities are adding intelligence to their grids: for example, the smart grid, which improves visibility to grid operations and hastens response to outages and, better yet, anticipates outages, taking steps to prevent them before customers lose power.
But a smart grid requires data, and lots of it, to generate the kind of real-time insight needed for a complete view of grid operations. When fully operational, a smart grid generates huge volumes of high-velocity data. The deployment of smart meters alone, often the first step in the journey to a smart grid for distribution companies, can result in a 3,000x increase in the amount of data generated.
Buried somewhere inside all this grid and meter data are valuable insights utilities can use to better understand customer behavior, detect outages, fraud or theft and more accurately forecast energy demand. Extracting insight requires solutions capable of managing the incredibly high volumes of streaming or at-rest data—IBM Big Data & Analytics solutions, to be precise.
Several IBM utilities clients are already getting value from the IBM Big Data & Analytics platform.
An Italian utility deployed a big data and analytics solution to capture and analyze the near real-time stream of information from grid sensors for a central, KPI-driven view of grid operations. The solution detects anomalous events, predicts their potential impact on service quality, issues alerts and makes adjustments to resolve issues dynamically. The result was a 99.9 percent improvement in their ability to identify and diagnose problems in near real-time
In a first-of-a-kind joint effort with IBM, a French utility used IBM Big Data & Analytics capabilities to gain new visibility into future consumption, gaining the ability to forecast national energy demand every 30 minutes for a full year in advance, versus short-sighted daily forecasts.
Centerpoint Energy uncovered anomalies by synthesizing and analyzing a large stream of data from 5,500 cell relays and 2.3 million smart meters—anomalies that in turn were enabled faster response to outages and improved customer service.
For more information on deriving insight into the grid, visit IBM’s Big Data & Analytics Energy and Utilities site.