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Data Analytics and Smart Grid: The Rising Tide for Power Utilities
While power utilities like to claim that they employ data analytics, they really
While power utilities like to claim that they employ data analytics, they really don’t. Utilities tend to have last-gen business intelligence (BI) reporting solutions that they call “analytics,” but that typically amount to not much more than reporting tools or descriptive analytics (primarily based on older database architectures running SQL), as opposed to the real-time and predictive software using complex event processing, to which the term “analytics” is now commonly understood to refer.
Utilities are today seeking to become more proactive in decision-making, adjusting their strategies based on reasonable predictive views into the future, thus allowing them to side-step problems and capitalize on the smart grid technologies that are now being deployed at scale. Predictive analytics, capable of managing intermittent loads, renewables, rapidly changing weather patterns and other grid conditions, represent the ultimate goal for smart grid capabilities.
Achieving an enterprise-wide IT architecture where all relevant data can be shared with all other necessary departments, systems and applications.
Ensuring that the enterprise is big-data-ready vis-a-vis the data storage and data management layers of its architecture.
Once utilities begin to overcome these foundational architecture issues, they can then begin to move into the deployment of analytics. The bulk of momentum behind utility analytics deployment is coming from:
Situational awareness gained through synchrophasor/phasor measurement unit (PMU) reporting the health of the transmission grid on an ongoing basis
Grid optimization analytics of the distribution networks (e.g., voltage management)
A recent GTM Research survey of more than 70 global utilities, which was conducted in partnership with the SAS Institute, displays how well different stakeholders understand the value that analytics provide. Not surprisingly, the survey confirms that utilities themselves report having the most momentum for analytics in the domains of customer management and grid operations.
FIGURE: In What Areas of the Business Do Analytics Seem to Have the Most Momentum?
Historically, very little, if any, analytics have been performed on the consumer side. This is due largely to the fact that this industry primarily operates in a monopolistic fashion, with only a smattering of states allowing retail competition. However, the era of smart grid has sparked a renewed interest in demand response and energy efficiency. It appears that utilities are beginning to improve both the data and the level of analysis they are willing to offer customers.
In considering utilities’ progress to date, it should be pointed out that most of the early success stories are rather narrow in scope and often are limited to a single domain. It is GTM Research’s conclusion that the true implementation of broader analytics (both customer-enabling and enterprise-wide) is not yet underway.
However, another trend that is occurring is that employees and customers are beginning to ask for access to particular datasets. At the current juncture, many utilities are not equipped to fulfill these requests, as they do not have enterprise-wide data architectures in place.
In many instances, this has resulted in a growing level of frustration, particularly as employees from other non-operational departments clamor for access to smart meter data. It is our belief that this situation will put some pressure on utility CIOs to properly design the right architectures to allow universal access.
Some progressive utilities, such as OGE, SCE and SDG&E, realize that there has been a paradigm shift and are beginning to make strides. However, the results of the survey indicate that the majority of utilities give themselves low marks in areas such as the utilization of analytics for reliability, the utilization of analytics for customer satisfaction, availability of enterprise-wide analytics, data integration of smart meter and grid operations data, and the propensity for data-driven decision-making in general.
The majority of utilities will therefore be challenged over the next ten years to invest properly in big data infrastructure, software, and services in order to avoid the risk of moving too slowly and having their enterprises be overwhelmed by the rising tide of smart grid data.