“Its not that we don’t have access to our data – the problem is that the data is in twenty different systems”. That’s a common refrain we hear from operations and smart grid executives around the world. In fact, the more smart grid-savvy utilities become, the more data they accumulate and the bigger the problem becomes. The volume of data itself is only one of the challenges – SCADA, meter feeds, enterprise applications, energy management systems and weather feeds, to name a few, are also delivered in different formats, frequencies and time ranges.
In the above list you’ll notice three different categories of systems: IT (such as CRM and ERP systems, OT (operational data such as SCADA and meter reads) and XT (external data such as weather, fires and traffic). It is the merger of data from all three categories that is commonly required to provide the insight users need to make time-sensitive decisions.
“Why is this a problem I should care about?” you might ask. The answer: it is common for operational personnel to spend 80% of their time collecting data and 20% analyzing it. Apart from the obvious impact on productivity, manual collection and correlation of data across systems is error-prone and making a wrong decision, or being unable to make a timely decision, can be extremely costly.
IT, OT and XT data has to be analyzed, correlated, understood in context, and presented to users in a way that is easy to comprehend. Without successful completion of these tasks, when the next asset failure occurs, how will you know (just for starters):
• Which resources and customers are affected, how are they affected, and what exposure the company, its employees and customers have to risk, safety and liability?
• Which specific assets have failed, why have they failed, where they are, what other assets and customers they affect, and what can be done to minimize the impact of the failure?
• What triage is required, what remedial actions must be taken and who else needs to be made aware of the situation?
Many utilities are meeting this challenge head-on with the introduction of situational intelligence software into their operating environment. At its root, situational intelligence takes advantage of major recent advances in multi-dimensional geospatial software, big data and complex event processing to bring the complexities of an operating environment into sharp focus. It melds the fields of computer science, statistics, and analytics, and uses advanced visualization techniques to make massive amounts of data easily accessible and quickly understandable.
Some utilities ask vendors “How will you visualize the millions of meters we’ve deployed?” While it is indeed possible to visualize those meters, it begs the question of what users will do when they see a million dots on a map. In most cases, it is not the millions of meters that’s interesting – it is only the data about those meters that the user cares about seeing. Perhaps they want to know which meters haven’t been read today or see an analysis of those that are suspected of theft. Allowing users to zero in on the assets and events that require their attention and arming them with the ability to take immediate action on their findings, is a primary driver behind situational intelligence.
Visualizing a problem makes a significant difference to an operator’s ability to comprehend a situation. Rather than scrolling through tabular data to spot issues or looking at traditional line diagrams, users can take in the scope of a problem within seconds. Various visualization techniques are used in situational intelligence applications to draw attention to a problem. Some examples:
• A geospatial display showing the placement of power lines might be color-coded to indicate points of stress or failure
• Alerts displayed on screen or delivered to users’ mobile devices might raise awareness of an approaching storm
• The scope of an outage is easily understood when an operator can not only see the areas affected on a map, but can with a simple mouse-click, drill down to a precise location to identify the source of the problem
While there’s nothing new about plotting data points on a map, situational intelligence is more than just putting pretty pictures on a screen. It’s also about correlating different pieces of information, helping users understand and analyze the significance of what is displayed, and guiding them through the processes and actions that are required based on those analyses.
Take Hydro One for example. The Toronto-based utility visualizes and analyzes data for over 4.5 million transmission and distribution assets to make informed decisions about asset and grid reliability. Data from over 30 systems, including SAP BW, BusinessObjects and SAP ECC, is integrated and analyzed through Space-Time Insight’s asset intelligence application. Seven risk factors provide consistent color-coded insight across all assets and asset classes through several different visualization formats such as maps, charts, Duval Triangles, and survivability curves. Hydro One now has a clearer picture of assets performing above and below life-expectancy and is projected to save millions of dollars through better coordination of asset maintenance initiatives across departments.
Situational intelligence is playing a key role in the deployment of smart grids around the world. Whether it’s alerting operators to asset failures, facilitating a rapid response to outages, or ensuring compliance with regulations, the ability to visualize, analyze and correlate data from multiple sources in real-time, gives businesses the insights they need to make informed decisions, and as a result deliver more reliable and safer services to their customers. With so much big data being stored in SAP HANA and elsewhere, there’s no better time to consider how you’re going to take action on it.