Adaptive Resource Management System – ARMS
By Bill Ellard on Jan 16, 2014
Charles Darwin:
"It is not the strongest of the species … nor the most intelligent that survives. It is the one
that is the most adaptable..."
The same can be said for demand side management (DSM), demand response (DR), and energy
efficiency (EE) programs. It’s not the most intelligent program/system or the most expensive – it is the
one that is the most adaptable…
There are many resource managers:
Building owners – electric and water demand
Utilities – water, electric, natural gas suppliers
Municipalities - water, electric suppliers
Midstream infrastructure – oil, gas, water distribution systems
All these stakeholders need to manage scarce resources in a cost effective way. For the most part these
entities want to reduce waste in order to maximize profits. Maybe you’re a grocery store that wants to
reduce its electric demand charge, a water municipality that wants to reduce its lost and unaccounted
for water, an oil/natural gas pipeline operator that want to reduce its fugitive methane, or an electric
utility that wants to manage its generation by shaving peak loads with demand response programs.
In order to effectively manage your resources, you need to measure and know the data about your
resource. Typically this data is collected once per month, days or weeks later in time. Having monthly
data reads are fine for invoicing and billing, but almost useless for managing the actual resource. For
effective resource management we need much more real time data collection and analysis.
Now we know we need real time data collection – every minute, hour, or day. With this real time data
collection, we have a new problem – too much data! We need to collect this time series data into an
efficient data base (DB), maybe a big data nosql database such as Cassandra or MongoDB. Now for the
first time we have the time series data that is required for creating actionable management plans and
more importantly - benchmarking capabilities.
The Adaptive Resource Management System (ARMS) process:
Measure and know
Create a plan
Execute the plan
Analyze resulting data against the benchmarks
Adjust or create new plans and repeat
Managing your resources is an adaptive iterative process, not an event.
Now that we know the real time resource usage – many changes are now possible to be more efficient.
Here are some real customer business cases:
Case 1 – Building demand charge shaving
One commercial electric customer was getting a high monthly demand charge (highest 15 minute KW
average for a given month) even in months were usage should be low. After real time monitoring of the
buildings sub panels, the owner found that the elevator (almost never used) was coming on once a day
at 2am. This spike in usage was causing the demand charge. The building’s automation system was
changed so the elevator no longer cycled on. In this case just the measure and knowing the data was
needed to effect change and savings.
Case 2 – Building demand charge planning
Another electrical client – a supermarket, found out the large walk in freezer was cycling on often after
6pm in the summer months when the outside air temps were above 100 degree. After 5pm the store is
very busy and the electrical load profile of the rest of the store was very high as well, and the cycling on
of the well-insulated walk in freezer was creating a 170kw spike in usage that cost the store several
thousand dollars per month.
What if we were to change the freezer’s setting so we super chill it earlier in the day, then let it ride out
the 6-9pm high usage times. This bring us to the second important feature of ARM – benchmarking.
Now we can truly perform specific adaptive changes in our building and see if the plan actually worked.
In this case the benchmark data was the KW electric demand collected once per minute. We saw that
while this plan did reduce the KW charges between 5pm and 7pm, we still are seeing the same 170kw
spike after 7pm. This is the beauty of real time actionable data. Within 24 hours we knew this plan alone
would not save this custom money – so we need to adapt.
Now in addition to the freezer plan, the new plan was to automatically turn on the backup generator
after 7pm, whenever we saw more than 5 KW reads come in that were over 120 KW’s. With this new
action plan in the software, we could benchmark the very next day. As before, plan #1 kept the KW load
below 100KW from 5pm-7pm as before, but now after 7pm the backup generation would run for 5-10
minutes at a time, keeping the 15 minute KW average (demand charge) below 120kw.
In this case 2 action plans were required to lower the building’s monthly demand charge. For this user
we simulated that these plans would reduce the demand charge by 30%, a significant on-going monthly
savings that would increase over time as the utility raises it demand charge rates. Software analytics can
now be used to optimize these plans, as well as continued benchmarking that the plans are affective
over time. The payback for this investment (sub-meters, CT’s, com. device, + software) would be under 1
year.
Case 3 – Commercial solar array sizing and return on investment
A high end supermarket owner wanted to install a solar array on his roof. This building’s electric
spend was very high, including a demand charge that accounted for 50% of the overall utility bill.
The main reason for the rooftop solar was to reduce the customers ever increase utility bill. Again,
without real time electric demand and consumption data this would be impossible to estimate the
savings. The owner believed that his demand charge was occurring during the day when his
business was busy, and that solar would produce electricity during the day as well, so the demand
charge would drop.
Once TSG sub-metered the building, and real time electric load data was available, it was obvious
that the solar array would not lower his demand charge. This particular building was experiencing
its highest KW power spikes well after 6pm, when the solar arrays production would have already
declined. Also, even though this building has a high insolation rate (very sunny), 10 or 15 minutes
of shading (clouds) will prevent the array from effecting the months demand charge.
So currently the most the solar array would offset would be the volumetric charges – the kwh’s
offset by the array’s production. This offset would only be worth about 4 cents per kwh, lower than
the cost to produce the kwh’s from the solar array (6-8 cents with incentives). The cost benefit does
not work with solar alone; this premise would also need a dispatchable on site generation source,
or DSM to lower this customer’s demand charge, and smart software to manage this.
Two solutions became apparent. This market has a 35kw propane backup generator, and our real
time software can turn on the genset whenever a demand charge is forming. We did the software
simulations and it looks like the genset would only have to run for an hour a day to prevent demand
charge spikes.
Another option was using demand side management (DSM). In this case our software would learn
and adapt to this buildings loads, chilling the large walk in freezer earlier in the day (10am-2pm), so
these compressors would not turn on between 6-8pm during the demand charge spikes. The beauty
of having real time data and software analytics is we could try different mini-projects to lower
electric spend and know within hours or a few days if they actually worked.
For many high electric spend facilities, the combination of rooftop solar with a gas/bio fuel microturbine, plus real time DSM and energy management software that TSG develops would be very
appealing, both economically and environmentally.
Case 4 – Municipal water - lost and unaccounted for (L&U)
In this business case, a city culinary water system had a high unaccounted for water loss in its
distribution system – 25%. This city metered the water produced from the water treatment plant, and at
the end of its system. Once a month all revenue meters were read and uploaded into the cities billing
system. At that point in time 25% of the water produced could not be accounted for – the L&U.
The cost of this lost or stolen water was high due to an older water treatment plant and the pump
stations which account for 80% of this municipality’s high electric bill. Also if the city could not find this
lost water, future housing growth would be limited. The first step was to add real time metering at
various key points along the distribution system. Now with this real time data we could see when the
loss was occurring (day, night, during certain hours of the day) and also see which sections of the line
are most problematic.
Once the real time data was available we could benchmark, and try new programs to lower the L&U.
One discovery was that in the business section of the system, larger losses were according during the
day. Upon more inspection the city found water hydrants that were being used in construction and even
irrigation. This theft from un-metered hydrants is not uncommon.
Case 5 – Oil and gas - L&U natural gas (fugitive methane)
Similar to the water utilities issue from above. After accounting for all the revenue metered natural gas
at the well heads, and the metering at the gas processing plant (the endpoint) over 10% of the natural
gas could not be accounted for. Two issues for this firm; first, they want to sell the natural gas not leak
it, and secondly these leaks are an environmental concern as CH4 is a potent greenhouse gas emission.
Again, by sub-metering at key points in the pipeline, one can ascertain were the bulk of the L&U is
coming from. Then action plans can be put in place to reduce the L&U, and can be benchmarked to see
the efficacy.
Case 6 – Electric utility demand response programs
In this case, the resource being managed are the utilities aging peaking generation plants. These plants
generally run on the hottest days of the year, when demand for electricity is especially high. The plants
use natural gas to generate electricity. Because peaking plants are more expensive to operate, they are
put into service only when necessary to increase capacity.
In some cases, these old peaking power plants must be kept on-line for just a few hours of use per year.
Adding larger scale solar arrays helps with the peaking plants fuel cost, but not with the utilities demand
spikes on an ongoing basis. These spikes can happen after 5pm, when people come home from work
and turn on their AC systems.
With real time metering and data collection, the utility could influence demand to the point of
eliminating these spikes in demand. One option is to allow the utility to automatically turn down cooling
systems of certain business and residential customers for a rebate on their power bill. Another option is
to send real time pricing signals to customers to lower demand as the utility see that a demand spike is
approaching.
These big data analytic systems will eliminate, or smooth these demand loads to the point where the
utility can decommission their older peaking power plants resulting in large savings to the utility, and a
more stable grid for their customers.