Navigant Research Blog

Cleantech in the Era of Big Data

— April 1, 2014

The concept of big data – the notion that we are overwhelmed by a flood of digital information like nothing we’ve seen before – holds both promise and peril.  The allure is centered on the benefits that big data will bring, in areas from medicine to traffic to agriculture.  These benefits will translate into profits for companies that manage, transmit, and store all that data.

Then there’s the other side: that big data will lead to privacy intrusions, lack of freedom, and, from a very practical standpoint, yet another headache for executives and IT managers.  We have covered this topic in the past (see a great description of how automated demand response firms are focusing on data analytics or click here to read more about framing the problem for building operators) and our recent webinar, Innovations in Smart Building Data Analytics, also presented some excellent examples of how industry leaders are using data analytics for their customers.

The Three Vs

Many definitions of big data are available, but the most compelling framework was created by Doug Laney in a 2001 research report.  This description focuses on three prime elements: volume, velocity, and variety.  Volume refers to the bigness of the data – there are more sensors and signals than ever before, pumping out data on everything from location to temperature to transactions.  Velocity addresses the speed that the data is being created, from subsecond phasor measurement unit (PMU) data describing the power quality on the grid to the rate at which Facebook is gathering our likes.  (It should be noted that one overlooked aspect of velocity is not just speed, but also direction.  Data is streaming not just from our devices, but also to servers, corporate analytics processors, and back to customers, all over the world.)  Lastly, there is variety, which is the real game-changer.  Data has never been unitary, and the diversity of data forms, standards, protocols, and utilities is growing by the day.  While often presented as separate concepts, these three elements are intrinsically linked.  I’d like to present the three Vs as a nested hierarchy (see below).

The 3 Elements of Big Data


(Source: Navigant Research)

Data volume gets most of the attention (hence the name big data, not fast data or diverse data) and velocity gets the communication and IT folks excited.  But it’s the variety of the data, and the variety of the velocity and the variety of the volume, that makes the big data interesting.  It’s not just that data is big or fast; it’s the diversity of speeds and directions that data travels to its many users.

Big Data, Big Challenges

For example, utilities used to report monthly electricity usage; now customers can see how much power they use every 15 minutes – that’s three orders of magnitude difference!  In addition, utility data is now being served to customers, local grid operators, energy efficiency firms, and facility managers.  Lastly, it is the complexity of the variety (the variety of the variety) that creates challenges, as well.  For example, in the developing world, buildings are at many different levels of IT sophistication and electrical grids have to integrate old equipment and management processes along with new state-of-the-art high-tech factories that need highly reliable power.

So how is big data actually affecting cleantech markets and technologies?  Going forward, in our research and our blogs, we will touch on how big data is changing cities and how it’s being integrated into regular business practices.  We will explore how traditional firms are coming up to speed, while startups are using it to leapfrog their competition.  We’ll  also examine how big data is providing new opportunities and challenges to the cleantech markets and how those markets are responding.


The Next Smart Grid Investment Frontier: Rural Cooperatives

— January 8, 2014

On December 13, the United States Department of Agriculture announced another $1.8 billion in guaranteed loans for rural electric utilities.   This comes on top of $188 million announced in July and $280 million in April, for a total of nearly $2.3 billion in 2013.  While most of that money will go toward building new transmission lines and substations (and bringing service to millions of Native Americans), more than $68 million is committed to smart grid projects.

Thirty-five cooperatives in 26 states were recipients of the loans.  Details on the specifics of the projects aren’t available, but it’s interesting to compare the dollar figures for the projects with the cooperatives’ membership numbers.  Where some projects equate to more than a $500 investment per member, others are in the low single digits.  For example, it wouldn’t appear that smart meters are going in at Maquoketa Valley in Iowa – yet. (Click here for a comprehensive list of loan amounts, recipients and dollar amounts per member.)

Nonetheless, with ARRA stimulus funds largely spent by the investor-owned utilities (IOUs), many in the industry (particularly vendors) have been wondering from where the next big spending spurt will come.  Cooperatives, with their member-governed boards, are not burdened with proving rate cases before public utility commissions (PUCs).  And with their relatively small customer bases and large geographical service areas, co-ops that can build a compelling business case and build member support might just provide the industry with the next swell of investment.

In fact, with such large territories, the benefits of distribution automation and remote asset monitoring might be easier to defend for the co-ops, which serve roughly 20 million members nationwide, but which cover 75% of the United States’ geography.

This is not to suggest that smart grid spending by co-ops will rival the billions spent by IOUs over the past few years.  It will come in slowly but, I believe, surely.  With defensible business cases the highest priority for smart grid investments, the cooperatives’ unique structure and geographic challenges make them logical candidates for the types of reliability and efficiency projects that smart grid technology makes possible.


In Energy Crisis, Japan Turns to Demand Response

— January 3, 2014

Besides gaining recognition over the years for some rather odd technological innovations, Japan has come into focus as an innovator in advanced smart grid capabilities.  Most recently, announcements of demand response (DR) projects in Japan have permeated the energy news reports.  Accenture, Schneider Electric, Comverge, and EnerNOC have all begun major projects in research and development (R&D) and DR pilots in the country over the past 6 months.  In early December EnerNOC said it will partner with Japanese general trading firm Marubeni to deploy DR for peaking capacity and load balancing for commercial and industrial (C&I) customers.  Comverge is engaged in R&D for DR in Japan, working under the sponsorship of Japan’s Ministry of Economy, Trade and Industry, while Schneider will partner with Tokyo Electric Power Company (TEPCO) to deploy 50 MW of industrial DR.

Amid Japan’s unfolding energy crisis, some have wondered why the DR market has remained in an exploratory state – and arguably still remains in one.  Since the Fukushima Daiichi disaster in 2011, the country has scrambled to replace over 30% of the country’s energy resources with increased imports of oil and natural gas.  The U.S. Energy Information Administration estimated that 2012 Japanese liquefied natural gas (LNG) imports accounted for 37% of all global shipments.

Stretching Scarce Resources

Prior to Fukushima, Japanese policymakers imagined the smart grid as a means of incorporating increased renewable sources such as wind and solar, as well as low density energy sources such as geothermal, battery systems, and hydraulic power.  While these resources are still being developed and promoted (especially wind), there is a clear need to employ smart grid capabilities for energy efficiency and curtailment in order to conserve energy and keep costs at a manageable level for utilities and customers.

So what would DR look like if employed throughout Japan?  Accenture’s DR billing system will support building energy management systems and electric vehicle ancillary services, while companies like Toshiba and Panasonic are working to integrate other smart city technologies into Japan’s energy infrastructure.  With automated DR technologies rapidly evolving, the nation’s potential to leverage this burgeoning infrastructure to respond to its energy shortage is promising.


Plug-In Vehicles: For Utilities, More Opportunities than Challenges

— January 3, 2014

According to the Energy Information Administration’s latest Residential Energy Consumption Survey (RECS), the average U.S. household consumed 11,321 kilowatt-hours (kWh) of electricity in 2009.  If the average 2014 light duty vehicle is rated at 24 MPG and travels 12,000 miles a year, then the amount of energy consumed by the vehicle over the year is equivalent to about 16,000 kWh of electricity.  When the EV Project 2Q 2013 data is analyzed, it shows that 96% and 99% of the energy consumption for participating Nissan LEAF and Chevrolet Volt plug-in electric vehicles (PEVs), respectively, took place at the residence of the PEV owner from April to June of 2013.  That would seem to mean the transition from petroleum-fueled to electric-fueled transportation will present serious challenges for utilities, which face a drastic increase in demand from the residential sector.  This, however, is not the case, due to the efficiencies of electric drive, the charging behavior of PEV owners, and the ways in which individually owned PEVs can be used by utilities to help match electricity supply with demand.

PEVs are far more efficient than petroleum-powered vehicles.  With an average battery size of 40.1 kWh, battery electric vehicles (BEVs) have an average range of 124.2 miles.  For plug-in hybrids (PHEVs), the figures are 11.7 kWh and 27.2 miles for all-electric driving.  Using the same 12,000 miles per year metric, the average BEV consumes 3,869 kWh of electricity a year and the average PHEV (utilizing all of its electric drive capacity every day) 4,271 kWh of electricity.

Non-Peak PEVs

While these energy requirements are significantly less than petroleum-powered vehicles, they still represent a significant demand increase of around 33% for BEVs and 37% for PHEVs for an average U.S. household.  At the average U.S. residential electricity rate of $.12/kWh, utilities collect around $450 per BEV and $520 per PHEV, per year.  To reap such revenues, some modifications to the existing utility grid infrastructure are necessary, but not many.

The increased use of air conditioning has required utilities to develop a grid supply infrastructure that can meet the highest peak demand loads of summer afternoons, when AC units en masse are turned on.  Studies on the charging behavior of PEV owners have shown that few PEVs are plugged in during these times, fewer still in areas where PEV owners are enrolled in time-of-use programs.  Thus, peak electricity demand will not be greatly multiplied by PEV charging.  Further, using PEVs for demand response programs, for grid balancing, renewables integration, and demand charge reduction, will help utilities supply electricity and may actually reduce peak demand loads.

Where utilities are most vulnerable to PEV demands is at the local distribution transformer.  Residential customers are supplied electricity through a transformer that feeds a number of units.  If all or many of the units supplied by a transformer require increased load for PEVs, the transformer may need to be upgraded to increase peak capacity and use.  However, data from California utilities shows that local grids need upgrades to serve PEVs less than 1% of the time.  The net effect to utilities should be new revenue streams with few costs.


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