Can Big Data Speed Renewable Energy?

Renewable energy is on a fast track that may get even faster. The advent of networked sensor systems and the much touted “Internet of Things” (IoT) has brought about the ability to collect and analyze vast amounts of data. Several firms are already aiming to use “big data” to revolutionize the energy industry. The implications are enormous, and could bring about a sea change in the sources of our energy, the distribution mechanisms, and the cost structure.

A July 2016 report from financial giant Citigroup postulates that the ability to analyze energy demand, sourcing, usage, and load balancing could result in customized energy delivery, obsolescence of current utility business models, and massive energy cost reductions. Big data is seen as enabling a truly transactive energy model, or to use a hackneyed analogy, the “Uberization” of the energy economy.

While some skeptics believe the capability is still overrated, much of the criticism is reserved for the “smart home” IoT and consumer applications, where turf battles are occurring on standards and interfaces but the basic technology is fully functional.

In actuality, the technology has arrived, the analytical software and modeling capability is here, and we are already seeing large sensor network deployments in industrial and commercial environments that serve to streamline operations, reduce energy costs, and track fleets. On the energy grid front, such firms as Tom Siebel’s C3 IoT, IBM and Enernoc offer engagement, demand-response, analytics and management services for utility and commercial customers. On the sourcing side, companies such as Vestas can predict "how the wind is likely to behave over any 10-square meter patch on earth during the 20-year life of a wind turbine," according to the New York Times.

The transactive energy vision was described by Greentech Media this way. “…Let’s start thinking of it as an internet-enabled free market, where customer devices and grid systems can barter over the proper way to solve their mutual problems, and settle on the proper price for their services, in close to real time.”

How is this seismic shift supposed to happen? Here is what some energy leaders envision. Greater automation and optimization through data, advanced software analytics, and transparency that enables the “democratization” of energy is expected to lead to an acceleration of the already fast-moving implementation of renewable energy sources such as solar and wind. At the same time the growth of microgrids and home solar will make the grid bi-directional. Energy monitoring and data analysis can create a more seamless system of give-and-take that enables utilities to balance the loads on demand, similar to “just-in-time” delivery of goods. Anticipatory scheduling based on computer modeling of weather and energy flow can help predict usage and even sudden spikes.

The aggregation – via software – of distributed energy resources, such as microgrids, will enable “virtual power plants,” (VPPs) tied together through energy storage and demand response resources, that can function as a single power plant/energy resource. This can save billions of dollars by eliminating the need for utilities to build new physical plants (as well as all the time needed to petition regulatory authorities and secure permissions, etc.). VPPs are already in development in such states as Vermont, New York, and Kentucky.

The challenges in the big data model are not inconsequential. Large amounts of investment will be required and many hardware-based utilities will be resistant to change. Yet the growing acceptance by those organizations of smart meters, wireless networks, and sensor capability shows that the utility mentality is already shifting. Additionally the availability of Software-as-a-Service (SaaS) companies who offer data analysis can help reduce the IT workload after an initial ramp-up. The risk, of course, is choosing the right provider. Software companies come and go, and analytical capabilities can differ in granularity and in algorithmic assumptions.

Utility companies with enough foresight will be able to weather this change and come out on top, with better insight into costs, new pricing models and value-added advantages for customers. Those utilities mired in the past and who persist in clinging to a costly aging infrastructure will be dragged into the future kicking and screaming, or will fail.

Tom Breunig is publisher and editor of Cleantech Concepts, an online magazine and market research firm dedicated to showcasing cleantech R&D projects.