The Rise Of Data In The Infrastructure Sector
Though the consumer and retail sectors often garner headlines for their innovative uses of “Big Data” and new mobile applications that rely on cloud technology to bring people together, these same technologies are fundamentally changing the development and operation of traditional infrastructure systems.
Some of the enthusiasm for data and mobile connectivity in the power, water and telecommunications markets is driven by awareness that intelligent devices and data analytics provide unprecedented visibility into “what is happening”on these systems. For operators, and those focused on managing the business side, there is value in this information.
There is also an emerging understanding that the increased application of intelligent devices and data analytics can help system operators improve efficiency and resiliency across a broad range of factors. As the variety of challenges facing critical infrastructure systems become more complex and more distributed, the value of intelligent devices and data becomes more defined.
For example, within the electric sector, power supply or the traditional process of delivering power to customers, is undergoing a significant transformation. The industry is seeing the rise of distributed generation such as roof-top solar and wind power, greater customer participation, and the on-going growth of the electric vehicle market. In essence, the problem of managing energy supply is migrating from a centrally managed problem to one of a massively distributed network with increasingly diverse assets. This is further complicated by additional levels of market and regulatory complexities. Without more data and more tools (analytics) to make sense of the information coming in from the system and to provide insights to drive actions, truly efficient management remains elusive.
Cloud Computing: Transforming the Infrastructure Sector
So how can cloud technology improve operations for large-scale electric and water utilities serving our largest cities or those smaller systems serving individual communities? First, operators can leverage the cloud to collect and store data from a large array of sources and can “scale” the size of the cloud environment to match the scale of data sources. Second, and perhaps more importantly, the scale of the computing resources can be matched to the nature of the problem; in effect the level of computing resources can be adjusted up or down based on need.
Large systems with hundreds of thousands of sensors delivering a wave of information can invoke as much machine horsepower as required to address massive problems, on demand. Further, depending on the nature of the problem, work can be broken into literally thousands of pieces and re-assembled on the back end to obtain results in the necessary time frame. Cloud computing offers great flexibility in applying advanced calculation engines.
Cloud computing can also be applied in unique ways such as the use of agents or special tools that seek to identify patterns in data and can refine their algorithms or models (i.e. learn) based on training protocols. Inductive tools allow for problem solving that cannot be addressed by other methods and to leverage the scale of the data and computing horsepower to identify critical data patterns. This capability will provide new means to optimize operational outcomes by identifying and applying adaptive controls. In power distribution, one possibility could be to address the challenge of system resiliency by incorporating greater energy efficiency into the planning matrix.
Improving Operations through Data Analytics
Another key opportunity for improving infrastructure performance is the real-time or near-real time application of data, otherwise known as “operational intelligence”. Advanced distribution management is an example of how applications that can leverage data from control systems (SCADA), load requirements and characteristics (both actual and forecasted), consider weather or other factors, are being developed to provide more efficient control of distribution networks.
Monitoring and diagnostics programs process system data via specialized models to identify emerging trends by early detection of small deviations from expected performance. The ability to recognize deviations could help avoid failures, or avoid situations where system capabilities don’t match requirements.
Predictive analytics can be applied in many instances by leveraging analytics to “predict” the value of particular data points based on historical data. Capital investment and maintenance budgets are areas where data can have meaningful impacts. Information on the condition of an asset can yield powerful insights and a means to establish a basis for making more informed decisions about the prioritization of repairs or the need for new investments.
“Intelligent devices and data analytics provide unprecedented visibility into ‘what is happening’ on the systems”
For utilities exploring complex planning problems that involve many moving pieces and long time frames, the use of tools to identify and evaluate options or identify alternative actions is a growing area of focus. Tools can vary widely based on the desired application but in general use the cloud to construct and evaluate countless alternatives and then apply math to identify and potentially refine the most interesting options. This is increasingly informing electric utility decision making as they migrate from centralized, conventional generation operations with limited customer engagement to more engaged entities harnessing distributed generation resources.
Through our work in the Critical Human Infrastructure market, Black & Veatch has seen a rise in the impact of Big Data, cloud computing and more integrated networks in managing and optimizing these complex and distributed systems. Water, energy, transportation, public safety/mobility management are all examples of distributed systems subject to the challenges of interconnection. Yet, these relationships indicate a wide array of potential users and a wide array of uses of overall system data–from control to collaboration, from decision support to strategy and planning. Through greater access to data, and the ability to see what is happening on the grand scale, decision-making can be informed and improved.
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