Still in their infancy, battery-based intelligent storage systems
haven't built up the performance track record most banks and investors
like to see when committing capital to a new technology, project or
company. However, a recently released National Renewable Energy
Laboratory (NREL) report by Jeremy Neubauer and Mike Simpson
investigates the economic returns of grid-connected Li-ion battery-based
energy storage systems with and without on-site solar power generation.
(left: Sharp's SmartStorage Solution. Credit: Sharp.)
Peak Shaving to Lessen Utility Demand Charges
The ability to reduce utility peak-period demand charges in states
where time-varying rate (TVR) structures have been enacted - so-called
"peak shaving" - is proving to be a boon to pioneering "smart" energy
storage solution providers, as well as grid operators looking to reduce
strains on power grids. A small, but growing group of advanced energy
storage system vendors - from young, small startups to large, well
established power and electronics companies - are targeting commercial
and industrial companies, school districts, municipalities and other
large utility customers as they look to expand and further develop their
systems and businesses.
Peak power demand has been rising faster than demand for electricity
at other times of the day. That rise has led utilities to steadily raise
peak-power demand charges, the charge they add onto a utility bill that
is based on the highest "peak" electricity demand that the entity has
had over a given time frame. In some cases, utility demand charges have
risen to the point where they can account for as much as 50 percent of a
large electricity user's monthly bill.
However, recently developed advanced energy storage solutions that
are installed "behind the meter" on customer sites, employ real-time
predictive analytics and battery packs to intelligently store and
discharge energy in order to shave peak-power demand and optimize
financial returns to the end-user.
How much of a return can organizations expect? That answer is
complicated by numerous variables, including end-user load profiles,
utility rate structures, the type, power-energy ratio and capacity of
the battery-based energy storage solutions, and whether or not on-site
solar power generation is included in the mix.
Estimating Returns
In its latest report, "Deployment of Behind-The-Meter Energy Storage
for Demand Charge Reduction," NREL's Neubauer and Simpson use historical
solar irradiance, end-user demand profiles and TVR pricing from
Southern California Edison's (SCE) TOU-GS-2 option B rate structure from
around April 2013 to take a stab at quantifying expected returns. The
researchers said they selected this structure for two reasons: "(1) its
format is similar to many other demand-charge rate structures via
inclusion of continuously active facility demand charges and additional
time-sensitive demand charges, and (2) the peak demands on record are
reset at the end of each month (i.e., it uses a monthly ratchet)."
Their analysis is based on installed costs for energy storage systems
including the inverter of $300/kW and $300/kWh, respectively;
acknowledging however that these price levels "may well be lower than
what is necessary to purchase suitable hardware today." They point out
that R&D efforts in battery and inverter costs target price points
even lower than these.
Green Charge Networks' Li-ion battery-based intelligent energy storage system. Credit: Green Charge Networks.
The results of their analysis showed that "small, short-duration
batteries [30-40 minutes] are most cost-effective regardless of solar
power levels, serving to reduce short load spikes on the order of 2.5
percent of peak demand."
The researchers also found that the size and optimal operation of an
energy storage system plays a large role in determining end-users'
payback periods and financial returns. "The peak demand reduction
achievable with an energy storage system depends heavily on the shape of
a facility's load profile, so the optimal configuration will be
specific to both the customer and the amount of installed solar power
capacity," Neubauer and Simpson said.
They also noted little gains to the grid through these systems and
said that this highlights "the need for modified utility rate structures
or properly structured incentives."
While onsite PV does reduce overall energy costs, Neubauer and
Simpson said, "solar intermittency due to cloud cover may cause the peak
load - and thereby demand charges - to remain unaffected.
"This then makes demand charges an even larger fraction of the
remaining electricity costs. Adding controllable behind-the-meter energy
storage, however, can more predictably manage building peak demand, in
turn reducing electricity costs."
Field Trials Challenge Findings
Neubauer and Simpson's conclusion that shaving peak electricity
demand and reducing associated utility demand charges provides large
electricity users with incentive to use energy storage is one that Green
Charge Networks - a provider of behind-the-meter Li-ion-based energy
storage solutions - reached several years ago, company founder and CEO
Vic Shao said.
Shao echoed NREL's finding that utility rate structures and customer
load profiles play large roles in determining the optimal size and
configuration of, as well as the returns generated by, energy storage
solutions. He explained that power-to-energy ratios (kW:kWh) are
extremely important in calculating optional configurations: "Power to
energy ratios loom large in any calculation of the optimal size/scale of
a battery storage solution - power being the rate at which energy is
used and energy being the actual quantity. In turn, utility rate
schedules, and TVR rates in particular, play a defining role in setting
the basis for determining a given system's optimal power-to-energy
ratio, and hence size or scale.
"For example, in California, [utility demand charges] are centered
around two-hour discharge cycles, so a system with a 30kW:60kWh [or 1:2]
power-to-energy ratio tends to afford the optimal sizing and
economics," Shao elaborated. But utility rate structure in New York is
based on four-hour discharge cycles, "so we tend to use a 1:4
power-to-energy ratio," he added.
Shao said Green Charge has found that energy storage coupled with a
solar PV system adds to the benefits and enhances end-users' overall
returns. "During any billing period it only takes one instance of a
solar inverter tripping or some other factor to cause a huge spike in an
end-user's demand profile," he pointed out, which would wipe out all
the demand-charge savings from solar for a month, "and that happens more
than you might think," Shao continued. "Having an energy storage system
in place can sort of 'backfill' those energy savings lost on the solar
side."
"In our experience, it [solar PV] has been a strong plus. Solar
should reduce demand. However, it cannot guarantee demand charge
reduction; energy storage can provide that." The biggest challenge for energy storage system vendors and their
customers is predicting future electricity demand, according to Carl
Mansfield, who heads up Sharp Electronics Corporation's energy storage
division. Sharp just recently introduced its SmartStorage system for
commercial and industrial facilities. "You cannot predict future loads, or PV production, per 15-minute
intervals perfectly, and small errors can make big differences,"
Mansfield explained.
Neubauer and Simpson's conclusion that a 30-40 minute storage
solution has the best payback is dependent on this assumption, Mansfield
said. "If you ignore prediction error, that can be true," he added but
said that the 30-40-minute conclusion is inaccurate. "With that small a
capacity, a system has to be sized relatively small compared to a site's
peak demand, so the probability of prediction errors being significant
is relatively high." He said that depending on specific end-user load
profiles, "storage systems with 1-1/3 hour to 2-1/2 hour durations" tend
to be optimal. NREL researchers acknowledge that their study assumes
perfect forecasts. "Economic sensitivity to forecast errors, while not
explored herein, could be high, as such errors could result in
commanding too little action from the battery system when meter loads
are over predicted, or in running out of battery energy during peak load
times when meter loads are under predicted," the report states.
Although there's no hard and fast rule, Sharp has found that in
general, properties and facilities that have a very high load factor and
relatively broad, flat peaks aren't good candidates for peak shaving
through energy storage systems.
In addition, Mansfield said that Neubauer and Simpson's conclusion of
a three-year payback period, without incentives, for a behind-the-meter
energy storage solution is "fairly low." Sharp conducted more than 18 months of field-testing on its
behind-the-meter storage system. "We find that in many cases, payback
periods can be in the 4-5 year range." He noted, however, that payback
periods and ROIs "can be quite variable, and they're also dependent on
the net impact of solar PV generation" if PV is installed.
Sharp has found installation of energy storage plus solar PV shortens
the payback period, he added. "What we see with storage in general is
that you have to design the system for a particular property. Solar
narrows the peak, so the residual work a storage system has to do is
less when they are co-deployed. You can downsize the storage to achieve
the same results."
http://www.renewableenergyworld.com/articles/print/volume-18/issue-3/features/solar-and-energy-storage/quantifying-returns-does-energy-storage-coupled-with-pv-offer-big-savings.html