Daikin Integration to BACnet, Modbus, KNX, WIFI, Mobile Apps
Your Buildings into a
Battery to Reduce Energy Costs
Phil C. Bomrad
Director Marketing Communications
Clean Urban Energy, Inc.
Throughout the U.S. and in many parts of the
world, there is a significant delta between energy consumed during on
peak times and off peak times. Because there has traditionally
been no effective way to store electricity, it must be generated,
distributed and used in sync in real time. Upgrades to the
generation and distribution infrastructure are costly and difficult to
justify when additional capacity may only be needed to support peak
loads. As a result, the task of satisfying peak demand presents a
challenge for everyone along the electric supply chain.
constraints during peak times impact our economy in many ways; from
higher energy costs for customers to increased CO2 emissions for
generators, reliability concerns for utilities and excessive risk for
competitive electricity suppliers. Each participant in the
electric supply chain is vulnerable to peak demand problems. The
result: billions of dollars of cost to our economy annually.
Utilities and grid operators have attempted to address the issue with incentive programs, rate structures, and awareness programs. In California, utilities provide what is called Technical Assistance/Technology Incentives programs (TA/TI) to provide customers funding to analyze and implement demand reduction strategies. For example, Southern California Edison (SCE) encourages commercial customers to participate in demand response programs by offering free technical audits and demand response equipment. Many utilities also have Permanent Load Shifting (PLS) programs, incentivizing building owners to implement expensive Thermal Energy Storage measures like ice plants, fuel cells, and lithium ion batteries.
organizations have also recognized the severity of our peak demand
problem and attempted to address with energy policy and grants.
In 2009, the US government awarded $3.4 billion dollars towards smart
grid projects though the American Recovery and Reinvestment Act (ARRA)
and the US Department of Energy (DOE) announced and additional $620
million to fund new technologies such as grid-scale energy
policy has also been used to address the issue. In 2011, the
Federal Energy Regulatory Commission announced Order 745, which will
make load reduction equivalent to incremental supply on wholesale
markets. Once implemented, this will provide a powerful incentive
for electricity consumers to reduce consumption at peak times.
there are limitations with each of these solutions. This paper
will discuss those shortcomings and introduce a new solution to relieve
electric demand is a pressing issue for consumers, suppliers, and
generators. Generators and energy suppliers are required to have
enough electric capacity to satisfy demand at all times. However,
consumers are rarely operating at peak levels. Figure 1 shows how
the aggregate load in California peaks for a very short period of time
in the afternoon, leaving significant electricity infrastructure idle
for much of the day.
enormous disparity between on-peak and off-peak electric consumption
can be explained using load factor. Load Factor is a measure of
how flat or consistent a load profile is. It is a percentage of
time that a building is near its peak. A typical commercial or
institutional building has a load factor of .55, meaning its peak
electric consumption occurs for a very short period of time. When
consumers are not operating at peak, the grid has excess capacity and
unfortunately, there is little use for electricity during pre-dawn
hours. If electricity could be efficiently stored like natural
gas, electricity use could be shifted to off-peak hours and a
building’s load factor would be closer to 1.0. This would
significantly reduce grid congestion and provide tremendous societal
benefit in the form of reduced emissions and lower costs.
utilities have built new capacity over the years, they have retired
older, less efficient generating plants. These plants are mainly
used to serve the peak load requirements. They are old, inefficient,
and environmentally problematic. As these “peaker” plants are
being torn down, utilities need to build new generating capacity to
meet demand. It takes several years to plan and build power
plants and transmissions and distribution systems and several millions
There is often public opposition to new infrastructure due to the NIMBY (Not in My Back Yard) philosophy. We, as consumers, want reliable and inexpensive power, but don’t want the transmission lines running through our back yards. This was especially evident in the years leading up to the California Energy Crisis. Metcalf Energy, for example, waited for approval to build a 600-MW combustion-turbine generating plant near Morgan Hill, California. Though the Sierra Club and the American Lung Association endorsed the construction, CISCO Systems was a major opponent. Though CISCO was a part of an industry that relied heavily on electricity for communications and manufacturing, they did not want a power plant next to their office park. It is a catch 22 situation.
Suppliers and utilities face a huge risk when peak soars because they either need to buy on the spot market in real time when prices are high, or spend significant funds on hedging to insulate themselves from the risk. Consumers want cost predictability almost as much as low prices, often feeling they can absorb increases in costs so long as they are aware of the increase beforehand. When suppliers have to provide flat energy prices to their customers yet buy on wholesale markets that are not flat, they are exposed to risk. They have obligations to provide power and positions in the market to meet those obligations, but are forced to either buy excess power or derivatives to cover their risk. These costs are included in the flat rate they provide consumers.
times, the power plants that are being used to deliver peak load are
inefficient, outdated plants. They have less efficient
transmission paths, and emissions from those plants are higher than
that of the base load plants. Utilities and generators are
penalized for emissions and the higher the emissions, the higher their
penalties. Many times these penalties are passed through to
consumers in rates, costing consumers millions of dollars annually.
The result is that electric customers get high electric bills, either on the supply portion of their bill or on the delivery of the power. These charges can be seen in peak demand and peak consumption fees.
Problems/Shortcomings with Current
There are several solutions that have been introduced to address the peak load congestion. In some cases, these solutions have provided incremental improvements but unfortunately have failed to sufficiently address the whole problem. The following describes these approaches and associated deficiencies.
Response is a program wherein buildings lower peak demand in “response”
to a market condition. The market condition is typically a
curtailment request from the ISO or utility. Building owners can
sign up for a DR program and either volunteer or commit to certain
||Demand response programs have limitations in that building owners and managers often lack the technology to implement demand response, don’t have sufficient knowledge of risks and opportunities (costs and benefits analysis), and lack transparency to energy markets. As a result, adoption of demand response in commercial buildings has not met expectations.|
Thermal Energy Storage in Ice Plants
energy storage – ice plants are a great way to take advantage of
inexpensive off peak energy. A building would build an ice plant
to make ice during the middle of the night when prices are low
and then use the ice throughout the day to reduce chiller plant energy
consumption during peak times.
energy storage has shown to have a significant savings impact in many
markets that have a high spread between on-peak and off-peak prices and
many times utilities offer incentive programs for having the plant
installed. Unfortunately, an ice plant requires capital
investment-- typically over $1 million (approximately $1000/kW).
In addition to those costs, space to locate the plant and tanks and
associated operating costs are prohibitive for most buildings.
Chilled water storage requires about 20 to 30 cubic feet per ton-hour,
and ice storage requires about 3 to 4 cubic feet per ton-hour. As
a result, this strategy has been limited to areas of the country that
have the highest peak rates and largest customer buildings or campuses.
Onsite generation can be used to flatten load but requires capital investment.
are several methods of on-site generation available to commercial
office buildings. Solar panels and wind are the most
common. The cost per Watt of solar power is about twice that of
coal generation. Because solar panels can last from 25 to 40
years, return on investment is typically realized at between 8 and 12
years. While photovoltaic installations have proven effective on
large, warehouse type buildings with broad, flat roofs, they are not as
useful on urban, commercial office buildings simply because there is
not enough flat, sun-exposed area on which to mount them.
On-site gas generators are useful in hospitals and industrial sites, where steam created during generation can be recycled to serve another purpose. They are frowned upon in grid-congested urban areas because they produce steam and pollutant emissions. Diesel generators produce the highest amounts of nitrogen oxides, and are only permitted to provide emergency electricity.
Utility Rate Design
Rates – Time of Use and demand charges.
rates often charge more for on-peak
usage than off-peak and often carry demand charges and ratchets that
constitute a large percentage of the total electricity cost.
Customers lack solutions that enable them to adjust their
operation/building to manage on-peak usage.
Solution – Buildings as Batteries
solutions pose fundamental flaws. Thermal energy storage is
expensive, demand response is not sophisticated, and utility rates are
static. Each was intended to address an underlying problem: lack
of efficient method to store electricity. To truly solve the peak load
problem, building operations must be integrated to grid operations with
complete transparency between the systems. There are three basic
components that will enable this integration: 1) the building must have
an effective method to reduce load without expensive capital
investments; 2) the grid and building must communicate and have
intelligence; and 3) the building needs the ability to predict load
requirements to implement changes in advance of congestion
With these three areas addressed, grid constraints can be drastically reduced, which will save consumers money on demand charges and TOU rates, while increasing DR revenue.
How it Works
All buildings have an inherent capacity to store energy in their thermal mass. Some buildings have more efficient thermal mass than others, with components such as building materials, insulation, window specification, tenant use, and solar position impacting the thermal mass efficiency. The trick is to determine the efficiency of the thermal mass—to measure its ability to store energy-- which in turn impacts heat transfer between surfaces and air throughout the day. For example, if the space temperature of a room is 73 degrees and the mass (concrete, drywall, books, desks, etc.) is 69 degrees due to cool thermal energy being stored there earlier in the day, then there is a positive heat transfer of 4 units between the thermal mass and the air. This heat transfer takes heat out of the air and allows the space to remain comfortable long after the mechanical cooling has been reduced.
The process starts by capturing data about the building such as floor area, construction materials, window specifications, mechanical systems, ceiling materials and construction, and interior furnishings. Also important is the position of building to the sun. The model includes hours of operation, number of tenants, percentage of conditioned space, and more. Finally, coupling of thermal mass to space air and decoupling of outside air to thermal mass is a critical component to modeling buildings and the Energy Plus model includes such analysis. Data is input into an online Building Survey and integrated into EnergyPlus, a Department of Energy whole building modeling tool that engineers use to design buildings and optimize energy and water use.
building has been modeled to determine the thermal capacity of the
mass, the building can integrate ongoing operational data from the
building automation system, hourly weather forecasts, and energy market
information such as day ahead or real time pricing, into the
platform. In markets that do not have dynamic pricing, the
published rate would be used. The platform algorithms simulate
different load profiles to determine optimal times to cool the
building. For example, if energy prices are low between 4:00 am
and 6:00 am and the outside temperature is 60 degrees with low
humidity, then the system will precool the building using free outside
air versus mechanical cooling. By cooling during unoccupied
hours, the building mass is sufficiently cooled and will provide
favorable heat transfer in the building that will last most of the day.
a comfortable workplace is a critical component in facility
management. Thermal comfort will generally not be compromised for
energy savings, as the adverse impact on productivity typically far
outweighs the gains from energy savings. Performing load shaping
has traditionally impacted comfort as precooling delivers uncomfortably
cool temperatures in the morning hours and peak load shedding often
entails letting temperatures float to uncomfortably warm levels later
in the day. Dynamically shaping load throughout the day without
impacting tenant comfort is accomplished by leveraging the thermal
ASHRAE 55 is a building standard
that specifies the combinations of indoor thermal environmental factors
and personal factors that will produce thermal environmental conditions
acceptable to a majority of the occupants within a space. The
environmental factors addressed in the standard are temperature,
thermal radiation, humidity, and air speed; the personal factors are
those of activity and clothing. In the standard, ASHRAE
introduces Predictive Mean Vote (PMV), which is an index that predicts
the mean value of votes of a large group of people. PMV is
essentially a prediction of how many occupants in a space will be
comfortable. A PMV of 0 is a thermally neutral sensation, which
is the lowest portion on the curve in Figure 3, while positive and
negative scores are too warm and too cold respectively. In the
region between plus and minus .5, thermal comfort is not
compromised. In a test by Morris et al, precooling and changing
PMV by -.5 reduced peak demand by 40%, which shows that minor
temperature adjustments can have a big impact on peak reductions
without sacrificing comfort. By leveraging thermal mass, CUE can
be more aggressive in load shaping by storing and releasing energy
throughout the day, delivering even greater
Maintaining control of a building is very important to
building owners and facility managers. Although CUE is a SaaS
platform that sends commands to the buildings automation system, the
commands are limited to space temperature setpoints that are within
predefined thresholds set by the building operator. As the
automation system receives the setpoints throughout the day, it
initiates control sequences to deliver the hourly temperature
requirements. The CUE algorithms work within the temperature
bands given by the building operator. There is no manipulation of
control sequences or PID loops.
provides a dynamic load shaping solution that takes inputs from three
sources: electric markets, weather, and buildings. Algorithms run
in the NOC throughout the day, as shown in Figure 4, and deliver
optimal space temperatures back to the building. In doing so, it
becomes an operational tool that initiates energy savings every day,
every hour. No manual intervention is required and because the
platform is a cloud application, there is no capital investment for
implemented its SaaS platform in a 976,000 ft² office building in
downtown Chicago. The building’s management team hoped that CUE
technology would achieve energy expense savings, promote energy
efficiency, and maintain the comfort of their tenants. In the
first month of operation, the CUE technology achieved all of these
Using CUE technology, the building
reduced peak demand costs by 30% and daily on-peak energy consumption
by up to 30%, increasing demand response revenue, and making the HVAC
system more efficient with its energy use. As Figure 5 shows, the
“pre-CUE” HVAC demand profile on two days, represented by the green and
blue lines, had a high peak load. After implementing the CUE
Thermal Energy Storage solution, the peak demand and on-peak
consumption was dramatically reduced, as is indicated with the black
trend line. The building manager maintained control of zone
temperature setpoints and was able to achieve these reductions without
impacting tenant comfort. Additionally, CUE technology found
untapped efficiencies in the building’s chiller operations. While
this building typically operated four chillers every day, they now only
on the electric grid are a growing problem globally. Demand is
increasing, plants are being retired, and transmission infrastructure
is dated. The imbalance between supply and demand during peak
times causes problems for all entities throughout the supply
chain. For generators, peak plants are typically less efficient
and costly to operate and may incur emission penalties.
Competitive electric suppliers are exposed to significant risk
associated with peak loads and spend millions annually to manage that
risk. Finally, consumers are subjected to high prices and
potential brownouts. There have been numerous technological and
legislative solutions introduced, rates altered, and utility programs
implemented. Each has shortcomings. Clean Urban Energy
provides a solution to grid constraints by utilizing a building’s
inherent thermal mass as an energy storage medium to allow electricity
to be used when prices are low, maintaining tenant comfort by releasing
energy from mass throughout the day. Because algorithms do
the optimization with inputs from energy markets, weather forecasts,
and building systems, no manual intervention is required by building
operators. It requires no capital investment, no expertise or
unique intellectual aptitude, and no sacrifice to tenant comfort.
as Batteries, managed by a sophisticated SaaS platform, solve the peak
load problem and overcome the challenges other intended solutions
encounter. The result, significant savings and community benefit.
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