How I Went to an Energy Storage Conference and a Data Analytics Session Broke Out

Published: Mon 21 Apr 2014
A blog entry by Christine Hertzog

Contributed by:

Christine Hertzog
Managing Director
Smart Grid Library

Christine Hertzog's Blog

Rodney Dangerfield had a great line about how he went to the fights and a hockey game broke out. It was a concise and witty commentary on the frequency of bench-emptying fist fights between hockey teams. We’re going to witness a similar trend in energy storage conferences. Many will transform into data analytics events.

Energy storage has extraordinary potential to transform electric utility operations and business models. There are many moving parts to it – there are almost daily announcements about new breakthroughs in chemistries that improve energy density, safety, and number of roundtrip charges and discharges.

Some moving parts of the energy storage market will mirror trends and characteristics of solar energy. Energy storage breakthroughs will result in rapid decreases in costs that have been witnessed in solar technology production and deployment. Privately-owned energy storage, like rooftop solar, will create whole new businesses that focus on deployment and maintenance of batteries and local jobs that require skilled workers. Energy storage solutions will also challenge utilities and regulators – another example of how technology outpaces policy. Just like net metering and feed-in tariffs were created for distributed solar photovoltaic (PV) installations, special tariffs will be created for energy storage assets that are not owned by utilities.

There is added complexity to energy storage tariffs insofar as batteries can deliver a variety of difference services to utilities ranging from ancillary services to keeping the lights on for specific periods of time during grid service disruptions. While solar panels constitute opportunities for their owners to reduce purchases of electricity from a utility or sell power back to it, energy storage has a greater variety of potential business cases. In addition to firming renewable sources of generation, owners of energy storage assets will have new opportunities to transact with utilities or with energy services aggregators. The basis of these transactions may be to place distributed energy storage assets alongside solar generation to enable greater participation in demand response programs. Today, demand response usually means a business must modify their operations to accommodate reductions in energy use. For some businesses, that may not be feasible as they lack that flexibility in their use of energy, or don't want to invest in management systems that support automated responses to DR events. But if that business can rely on energy storage to take up the slack for demand response or other utility requests for capacity, voltage, or frequency responses, then they can earn money leveraging their energy storage assets.

And that’s how a funny thing happened when I attended a recent Agrion event about energy storage. A data analytics event broke out.  In order for companies to participate in these new types of transactions, there’s a significant amount of data that must be gathered and analyzed. One company, Bosch Energy Storage Solutions has focused on development of complex software analytics and algorithms to define customer power needs. They are building data sets to forecast energy use patterns based on a number of historical and realtime data sources. They look 24 hours ahead to anticipate energy use as well as review the last week and the last year’s data to understand historical patterns of use in order to formulate the best energy storage management decisions. Another company, Stem, created a software platform that uses predictive algorithms to manage individual energy storage systems and aggregated storage systems to perform load reduction in conjunction with utilities’ operational needs. They collect granular usage data at a higher resolution than what is gathered by smart meters and combine this data with tariff information to build reliable financial models and determine the optimal times to discharge and charge batteries.

These are two examples where data analytics applications help run energy storage as cost-effectively as possible, and help build the business cases for sales resources to sell storage solutions to customers. There will be many more applications to follow that help energy storage solution purchasers determine which technologies and what storage applications make the most sense based on unique requirements. So don’t be surprised if you go to an energy storage event and discover that it’s a data analytics event too.