Utilities can sage 3-10 times the cost using software upgrade instead of infrastructure upgrade

By Editor


Utilities can sage 3-10 times the cost using software upgrade instead of infrastructure upgrade

The commercial energy consumption market is vast and growing. Between 1985 and 2004, commercial consumption grew by 50 percent.

Energy Efficiency Goals Shared by Private Sector and

It is obvious that businesses are highly motivated to
find ways to reduce energy usage. Better energy management goals by the commercial sector are very much
shared by the electric utilities. The U.S. electric transmission and distribution grids cannot scale up fast
enough to accommodate the accelerated pace of electrical consumption patterns caused by the growing
population, increased per capita energy usage, growing commercial demand and so on.

The growing presence of electric vehicles (EVs) will further complicate the stress on the grid as illustrated in the graph to the right. This illustration reflects two energy consumptionmeasurements between 6 p.m. and 8 p.m. at a 7Eleven outlet in Brooklyn, NY. One of the days, the Level 3 charger was used around 7 p.m. and its impact on consumption is evident. Mass EV adoption would cause enormous impact on the stability of the electricity bills, especially in urban areas.

The commercial market needs a solution that
aims beyond merely reducing kWs used, but
instead works around the natural lows and highs in energy
consumption throughout a normal business day
by aggregating total load across the facility and time.
On the utility side, massive grid updates are needed to
provide reliable service to meet both current and growing
future demand. However, infrastructure upgrades
are costly, take a long time to plan and execute, and can
be highly disruptive. Systems that avoid
infrastructure investments while maximizing current
assets are a much more efficient way to meet the
growing demand.

Nationwide, spending by utilities to keep up with the
growing electric demand runs in the billions of dollars. This massive and growing capital expenditure is a strain
on the U.S. economy and is the focus of the Smart Grid6 movement. For example, according to its 2010
report, Consolidated Edison, Inc. spent $1.96 billion in operation and maintenance costs on its electric
operations, an increase of 13.2 percent from 2009. It further estimated that construction expenditures will exceed $5
billion over the next three years to keep up with rising demands. Considering that Consolidated Edison is
just one out of thousands of electric utilities across the country
altogether supplying over 4
million GWhs of energy annually
a solution to improve
efficiencyin a more cost –
effective manner is
much needed.

Currently, utilities tend to allocate most or all their capitalexpenditures to hardware equipment (bigger transformers, cables, power
gear, etc), construction andinstallation. This is a grossly inefficient use of capital, as is
shown in the graph.


In a September 2010 report7, the Edison Foundation
estimated that by year 2020, there will be 65 million smart meters deployed 
representing 50 percent of
U.S. households. This rich dataset holds tremendous promise of providing utilities real –
snapshot of loading on the grid down to the lowest level. Additionally, utilities are inundated with data from field assets such
as distributed photovoltaic systems and other network –
enabled devices, as well as
historic usage data. The challenge for many utilities is how to put the data to work to create actionable intelligence in real –
that allows them to maximize current field assets rather than having to expand their infrastructure. Green
Charge Networks (GCN) was part of a team assigned to find a cost –
effective solution to
do just that.

The ultimate goal of the project was to enable utilities

* Increase utilization of current assets to support demand spikes.

* Reduce capital expenditures as well as operating and maintenance expenses.

Meet the growing demand for electricity.

* Integrate automated demand response into grid operations.

* Integrate distributed generation resources into grid operations.

Today, demand response and/or emergency load reduction
programs are typically triggered with phone calls and emails. The events are called over a wide area that
may or may not make a difference to a small set of overstressed assets. GridSynergy Intelligence
a sophisticated rules engine that computes available options for surgically relieving loads at overstressed
utility assets in real –
time. With the ability to analyze down to individual feeders and transformers, it takes
into account available curtailment and/or generation capacity on the distribution grid, including:

Demand response assets, pledged curtailment and program constraints.

* Controllable high power equipment, such as electric vehicle chargers, HVAC,
refrigeration and lighting.

Controllable energy storage systems.

Aggregation of Small Loads

Two important project goals were increased utilization of
grid assets and integration of automated demand response. For most utilities, especially ones serving
large urban areas, the aggregate of small commercial loads make up the majority of the utility’s total load. under todays programs rules these small accounts often
do not qualify to enroll in demand response programs as
they do not meet the minimum curtailment pledge.

GCN sought a solution to this issue by monitoring the
energy usage at 7 –
Eleven convenience stores in Brooklyn, NY. GCN learned that these outlets typically
have a summer peak demand of just under 50 kW. On its own, this would mean that these types of small retail
outlets do not qualify for most traditional electric utility demand response programs, which tend to have
minimum pledged curtailment requirement of 50 kW. The solution lies in the aggregation of multiple loads, which is possible through the application of GridSynergy Execution. When put to the test, GridSynergy Execution aggregated the energy usage of multiple 7 –
Eleven stores and indeed met the minimum threshold by directing the collection of stores to respond in unison to demand response events.

In addition to automated and aggregated demand response, GridSynergy also enables
the use of high –
powered electrical devices, such as electric vehicle chargers, to be used without affecting
the stability of the grid or causing demand charges for the operator.

A Cost – Effective Solution
Savings of Three to 10 Times the Cost

One of the five project goals was the reduction of
capital expenditures while meeting demand growth. GCN calculated the financial advantage of using software
versus infrastructure upgrades and estimates that utilities will save between three and 10 times the cost
when utilizing software to maximize the efficiency of current assets versus implementing hardware upgrades. The
savings depend on the kind of infrastructure planned to meet the demand growth. One clear example of
such savings came from project partner AVIS

Rent – A – Car.
AVIS offers the ability to rent electric vehicles at their LaGuardia airport
location, and due to the fast turnaround needed at rental facilities, had the need
to install more than 20 electric vehicle chargers. Going the traditional infrastructure upgrade route, the
service equipment, construction cost and the associated operational interruption would cost more than
$0.5 million. Instead, GCN installed its GridSynergy powered energy storage system at one –
of the cost. AVIS now has 21 EV chargers installed at LaGuardia Airport thanks to using the software –
solution tackling pockets of load spikes during peak –
demand periods.

An Industry – Wide Approach

The best way to ensure utmost reliability and efficiency
of the U.S. electric grid is to take a comprehensive approach from utility to end –
By involving and incentivizing commercial users, and automating participation in demand response programs, the entire
electricity distribution chain benefits. Additionally, commercial players may be encouraged to integrate various
forms of renewable energy.

GCN studied ways to involve commercial accounts that
create a win –
for both the private business and utility. The answer lies in the avoidance of demand
charges and utility service upgrades. GCN developed an embedded software package (GridSynergy Client) for
commercial users, such as large office complexes, hospitals and retail establishments.

The software powers the smart controller technology
installed at the facility
GridSynergy Client balances the load at the electric panel in real – time.
As such, it minimizes electric usage peaks that cause expensive demand charges. For example, GCN found that 7-
Inc. spent $210 million on electricity in 2010, of which $45 million in demand charges. By using our
technology, 7-
Eleven could have used the same number
of Kilowatts, while saving as much as $30 million that year
alone. This results in ongoing savings on operating costs that can run millions of dollars each year.
Importantly, it reduces the electricity bill in ways that are invisible to the facility and customer. On the other side
is the electric utility.

GridSynergy ClientTM offers the capability for four
complementary hardware modules to further minimize the need for utility service upgrades. These include:

* Energy storage using green charge networks green station technology.

* SmartGrid enabled electric vehicle chargers.

* Building automation systems and automated demand response.

* Photovoltaic systems or other forms of renewable energy.

GridSynergy ClientTM may also tie in to other high – demand
electrical devices such as generators, HVAC equipment, foodservice equipment, etc.


The four – year project clearly
demonstrated that using software instead of power hardware assets can improve the reliability and efficiency of the grid, keep
up with rising electric demands on the grid and reduce capital expenditures. In summary, the software solution

Real time grid insights enabling utilities to act proactively and preemptively.

Savings of between three and 10 times the cost of infrastructure upgrades.

Maximization of current assets to meet spike demand.

Integration of automated demand response into grid operations.

Integration of distributed generation resources into grid operations.


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