Utilities face a number of daunting challenge stoday, from capital constraints to geopolitical concerns over carbon emissions, from aging workforce skills to declining energy demand. At the same time, the industry must exceed rising customer expectations efficiently and cost-effectively, while reducing economic losses from power outages and quality issues - events that currently cost U.S. utilities approximately $188 billion a year.
Many utilities are finding that the potentially transformational benefits offered by emerging technologies are already undermined by the limitations of legacy systems and infrastructures, which are neither designed nor equipped to integrate with new technologies to manage the two-way flow of power. The smart grid, against this background, has emerged as an effective way for utilities to address the impact of the aforementioned challenges in terms of the industry's three major business functions: power delivery, asset management and consumer experience.
Smart grid employs sophisticated sensing, embedded processing, digital communications and specially designed software to generate, manage and respond to network-derived information. This makes a utility's business more observable, controllable, automated and integrated, resulting in improved reliability and efficiency. It also allows for better asset and work management, as well as integration of renewable energy sources, distributed generation and storage facilities.
The Challenges of the Smart Grid
The very asset that makes an energy grid smart - its wealth of data - creates major challenges. As utilities move toward smarter grids, they are faced with an unprecedented deluge of data. To take a first step toward turning this flood of new data sources into useful operational information, utilities and their stakeholders will need to take a holistic view of the data components and characteristics.
It is important to remember that each smart grid function can support multiple outcomes, and that each outcome can in turn contribute to multiple benefits. In general, data management design should optimize outcomes in two ways. First, it should extract clean, consistent and well-understood information that drives targeted benefits for the business. Second, it should minimize the costs of infrastructure needed to obtain and process the data necessary to deliver these benefits.
With a smart grid, the sheer volume and variety of potential data means this two-step approach is especially vital. A key attribute for managing, controlling and optimizing the smart grid is ensuring that the data across the grid is governed, readily measurable and observable.
This is particularly relevant for utilities, as power distribution grids have historically tended to lack detailed observability. Developing a true smart grid requires the creation of an explicit grid observability strategy. Parts of this strategy development already exist at most utilities, but the design will need to close the loop to optimize grid performance on a continual basis.
Creating such a strategy requires a solid understanding of master data as well as the nature and flow of smart grid data through the organization. It is useful to learn lessons from industries like financial services, airline and retail, which are accustomed to managing and tracking vast amounts of data, often in real time.
Five Distinct Data Classes
Grid data has largely been treated homogeneously. But data should be treated and managed in different ways based on its source, characteristics and applicability. The five separate classes of smart grid data, each with its own unique characteristics, are operational, nonoperational data, meter usage, event message and metadata. While the first three are relatively familiar to utilities, the last two are likely to present more problems as utilities adapt to the smart grid world.
The flood of raw data from smart grid devices and systems is not directly usable or even comprehensible. It needs to be transformed into useful information before it can be acted upon - a task complicated by the fact that useful information often is not obvious from simple inspection of the data.
Most utilities face four major data management challenges in developing smart grids: matching the data acquisition infrastructure to the required outcomes; learning to apply new tools, standards and architectures to manage grid data at scale; transforming processes throughout the business to take advantage of smart grid technology; and managing master data to enable the benefits from smart grid capabilities.
Utilities must ensure that the five data classes are reflected in the data integration architecture and they use the right analytics to turn the mass of data into usable information and business intelligence. If designed properly, the data architecture will provide the capabilities needed to deal with future change and evolution in their smart grids and business environment. More than just data stores, it will also need to include such elements as master data management, services and integration buses to effectively share data and information.
It won't come easy, but it will be well worth the effort.
Greg Todd would like to thank Dave Haak, John Miller and Benny Hagood for their contributions to this month's column. Dave Haak is senior executive with Accenture's Smart Grid Services practice; John Miller is chief data architect in the Accenture Information Management Practice and the global lead for Enterprise Data Management Smart Grid, Meter and Premise Services; Benny Hagood is North American Lead for Accenture Information Management Services Data Management & Architecture Practice and the Master Data Management Capability Lead.
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