Smart Grid Compels Utilities to Learn New Skills

In an earlier post, we discussed the essential role that telecommunications infrastructure plays in making the smart grid a reality. After all, it hardly makes sense to populate devices in homes and businesses capable of two-way communication with the electric power grid if the lines connecting all of the end points can’t handle the traffic! Yet even if we could snap our fingers and bring the requisite connections into being, there is still plenty of work to be done.

Think for a moment about all of the large, mainstream companies that are building smart devices into their offerings. Some customers are already monitoring and adjusting their home temperature using the Internet. It doesn’t take a leap of faith to contemplate controls for major appliances, security systems, lighting, et al. These offerings will affect the consumer experience of their electric service, and yet the product functions and user interface are beyond the purview of the electric provider. In fact, given the proliferation of proprietary technology, it’s a fair bet that offerings from multiple vendors will not “play well” together. How should the electric company’s customer service representatives respond when they receive calls from aggravated users? Will the reps even know what their customers are talking about?

Security will take on a whole new meaning. In bygone days, providers could focus their efforts on physical security. Unfortunately, a smart grid creates opportunities for malicious attacks that could compromise privacy and thwart performance. Firewalls are a necessary element of any cyber security plan, but they’re no longer sufficient.

Information management will need to go up a notch. The smart grid holds the promise of leveraging all of the inbound data to understand, manage, and forecast energy use – a.k.a., “predictive analytics.” Among the first applications to be explored is a distribution fault anticipator (DFA). It looks for data patterns that suggest impending disturbances in the grid. Using a DFA, a co-op can get out in front of problems, thereby avoiding service disruptions and labor-intensive (costly) trouble-shooting exercises. However, predictive analytics applications are only effective when the co-op has the ability to collect, process, and analyze mountains of data to create useful, actionable information.

1. Co-ops and their industry associations need to advocate for industry standards covering household devices that interact with the power grid. Standards could go a long way toward easing the impending customer support nightmare. They also benefit consumers by enabling plug-and-play interoperability and encourage robust competition to keep costs reasonable. Until such standards are widely accepted and adopted, build training modules and protocols to prepare your service reps to handle customer calls. If it is not practical to train all of your reps, create a “tech support team” to field these calls.

2. All co-ops need a robust cyber security plan crafted by subject matter experts. This plan includes provisions for appropriate cyber security technology, periodic testing to ferret out and correct weaknesses, and staff training to be mindful of “Trojan horse” emails, et al.

3. While vendors are working to create predictive analytics solutions, these systems are not self-starting or self-maintaining. Consider the investments in people and processes that you’ll need to make to take full advantage of these tools.