  Our nation's infrastructure for generating, transmitting, and distributing
electricity - "The Grid" - is a relic based in many respects on century-old
technology. It consists of expensive, centralized generation via large plants,
and a massive transmission and distribution system. It strives to deliver
high-quality power to all subscribers simultaneously - no matter what their
demand - and must therefore be sized to the peak aggregate demand at each
distribution point. Ultimately, the system demands end-to-end synchronization,
and it lacks a mechanism for storing ("buffering") energy, thus complicating
sharing among grids or independent operation during an "upstream" outage.
Recent blackouts demonstrate the existing grid's problems - failures are rare
but spectacular. Moreover, the structure cannot accommodate the highly variable
nature of renewable energy sources such as solar and wind. Many people are
pinning their hopes on the "smart grid" - i.e., a more distributed, adaptive,
and market-based infrastructure for the generation, distribution, and
consumption of electrical energy. This new approach is designed to yield
greater efficiency and resilience, while reducing environmental impact,
compared to the existing electricity distribution system. Initial plans for the
smart grid suggest it will make extensive use of existing information
technology. In particular, recent advances in data analytics - i.e., data
mining, machine learning, etc. - have the potential to greatly enhance the
smart grid and, ultimately, amplify its impact, by helping us make sense of an
increasing wealth of data about how we use energy and the kinds of demands that
we are placing upon the current energy grid. Here we describe what the
electricity grid could look like in 10 years, and specifically how Federal
investment in data analytics approaches are critical to realizing this vision.

Computers and Society