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Turning Utility DG Threats Into Business Opportunities:
Part I. Assessing the Threats
Jerry Jackson, Ph.D.
President, Jackson Associates
4819 Emperor Blvd. Suite 400
Durham, NC 27703
919-967-9000
Summary
A number of DG market studies over the last several years report substantial
distributed generation (DG) and combined heat and power (CHP) potential in
existing utility markets. Some studies indicate that as much as 20 percent
of current electricity production can be generated at customer sites more
economically than at central power plants.
Declining DG/CHP system package costs, more favorable regulatory treatment,
and the development of ever smaller and more efficient DG and CHP systems
insure that most utilities will find DG troublesome with revenue losses and
hourly load impacts growing at an increasing rate.
Two critical questions face each utility: (1) what are the potential revenue,
energy and hourly load impacts of DG/CHP over time? And (2) what options
exist for integrating DG systems in utility business plans to transform revenue
threats into business opportunities?
This paper, which is the first in a two part series, describes a microsimulation
process which accurately evaluates current and future DG/CHP revenue, energy
and hourly load threats within individual utility service areas. Microsimulation
is an analytical process developed to evaluate the impact of events in a
diverse population. Microsimulation models, which have been applied in energy
analysis since the 1970's to evaluate DSM program impacts, new technology
market penetration and a variety of other energy market issues, are presented
here as a way to address this critical issue.
While the examples are presented with actual utility customer data in several
locations, the data development process and analysis principles described
here are the same for all service areas.
DG Markets Are Local
As every DG supplier knows, the US DG market actually consists of more than
three thousands individual utility markets composed of investor owned, municipal,
cooperative and other publicly-owned utility service areas.
Differences in rates structures, customer electric and thermal loads and
other utility-specific factors result in differences in DG/CHP site economics
for identical customers in each utility service areas. For instance, a natural
gas-fired CHP engine system in a 200 bed nursing home in the PECO service
area provides a payback of 2.6 years while an identical system in the PP&L
service area has a payback of 7.6 years.
DG economics and the extent to which distributed generation will impact each
utility service area depends on a complicated interaction of customer electric
and thermal loads, rate structures, DG technology characteristics, natural
gas prices, interconnection requirements, regulatory requirements and incentive
programs.
Spark Spreads Misrepresent DG/CHP Economics
The first rule in evaluating DG potential is to ignore the traditional measure
of DG economics: spark spreads. Spark spreads reflect differences in the
average costs of customer generation and utility supplied power. An example
of a spark spread calculation is shown below:
Spark Spread = Onsite Generation Cost ($/kWh) - Utility Price ($/kWh).
With natural gas prices of $6.5/mmBtu and an electric generation efficiency
of 35%, the onsite generation cost is $0.063/kWh. The spark spread has to
be large enough to pay for the generator and its installation; an installed
cost of $500/kW requires an additional $0.014/kWh to achieve a payback in
four years. Thus, spark-spread analysis suggests that a utility electric
price of at least $0.078 is required to make DG an attractive option under
these circumstances.
There are two fatal problems with spark spread calculations as indicators
of utility service area DG market potential:
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Average prices in the calculations do not reflect customer avoided cost of
electricity purchases which depend on the customer's electric hourly loads
and typically complicated rate structures, and
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Spark spreads do not reflect contributions of waste heat applications for
space heating, water heating, air conditioning, etc in combined heat and
power (CHP) systems.
While the first deficiency is problematic enough to make spark spread analysis
unreliable, the second problem guarantees a serious under-estimate of DG
potential using spark-spread calculations.
CHP systems can dramatically change site economics. If, in the example above,
a combined heat and power application (CHP) uses half of the waste heat created
in the generation process (thereby replacing natural gas purchased for space
heating, etc), the cost of onsite generation is reduced by $0.02/kWh requiring
a utility price of $0.058 or less to keep utility power competitive (given
the gas price, equipment costs and the 4 year payback requirement in the
example).
Using half the waste heat in CHP applications is a moderate assumption consistent
with an overall system efficiency of 68%. A CHP system with an overall
efficiency of 85% would require a utility price of $0.047 or less to keep
utility power competitive. This example illustrates the substantial impact
of waste heat use on CHP economics and the fact that CHP systems will compete
with utility-supplied power in most service areas, at least for some customer
types.
In addition to showing inadequacies of spark-spread analysis, the example
above illustrates the fact that CHP systems can provide economical applications
even in electric systems with low electric prices.
Not surprisingly, most DG companies are focused on providing packaged CHP
systems which apply waste heat to space heating, water heating and air
conditioning. Other waste heat uses such as swimming pool heating and industrial
process heating applications are easy extensions for these systems.
CHP Applications Extend DG Benefits to Non-Traditional Customers
The extent to which CHP applications improve DG economics is shown in the
following table. Annual bill savings and simple payback are presented for
peak clipping and CHP systems for 200,000 square foot office buildings in
Dallas and Chicago.
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Peak Clipping
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Combined Heat and Power
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Annual Bill Savings
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Payback (Years)
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Annual Bill Savings
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Payback (Years)
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Dallas
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$12,401
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8.6
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$94,454
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3.8
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Chicago
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$12,330
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11.7
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$82,627
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4.4
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CHP systems paid for themselves in less than half the time required by peak
clipping systems while CHP annual electric bill savings are at least six
times as great with the CHP systems.
This table illustrates several important facts about the current DG market:
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Utilization of waste heat at the customer's site can transform unattractive
generation-only applications into money-saving CHP applications.
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CHP systems extend DG to non-traditional customers. Utilities have accommodated
DG and CHP applications from large industrial and commercial customers like
universities and hospitals for decades; however, smaller packaged CHP systems
have extended the market to even non-traditional customers like the mid-sized
office buildings shown in the above table.
New DG/CHP Supplier Business Models Overcome Customer Inertia
It is not surprising that the attractive paybacks provided by today's DG/CHP
systems have spawned a number of companies developed specifically to take
advantage of CG/CHP electric bill savings. One of the most visible of these
firms is Real Energy, a California company that provides DG/CHP systems to
utility customers in a turnkey operation where the customer's only role is
to realize guaranteed savings of 10-15% of typical electric bills. Real Energy
finances and installs the system and takes care of permits, fuel contracts,
maintenance and all other installation procedures. From the customer's
perspective, the only evidence of the DG system is the electric bill savings.
A 10% electric bill savings for a 200,000 square foot office building would
range from about $20,000 to $30,000 per year at an average electric rate
of 10 cents per kWh.
Not surprisingly Real Energy's successful business model is being applied
by other DG/CHP suppliers. The appeal to the customer is obvious. A no-effort,
risk-free savings of $20,000 - $30,000 for a moderately-sized office building
would be difficult to pass up.
Calculating DG/CHP Economics
Computing individual utility customer DG economics is a somewhat complicated
process. DG/CHP systems use natural gas (or other fuels) to generate electricity.
Fifty to seventy percent of natural gas energy inputs in the generation system
are converted to heat which can be captured and used for space heating, water
heating, air conditioning and other end uses. Each hour that the generator
runs reduces the amount of electricity which must be purchased from the utility
while any waste heat utilized for end use services (space heat) reduces the
amount of natural gas (or other fuel) which would have been purchased to
generate these end use services. Energy cost savings are determined by electric
and gas rate structures (which can be complicated with ratchet clauses, time
of use rates, demand charges and other features) requiring computations using
the customer's before and after DG/CHP hourly electric and gas uses.
Equipment type, size, design, and operating schedules of DG systems are optimized
to match hourly electric and thermal loads for each of the 8,760 hours of
the year.
DG Economics Are Different For Each Customer
Individual customer electric and thermal loads differ significantly across
customers, even those with similar business activities. Figure 1 shows week-day
hourly loads in July for 20 medium-sized air-conditioned office buildings
in Houston.
Figure 1. Individual Customer Hourly Load Diversity
Customer hourly load diversity translates into customer diversity in CG/CHP
system economics as well. Figure 2 illustrates this diversity with a scatter
plot of paybacks and annual kWh for a statistically representative sample
of customers in the LIPA service area. The figure includes only those customers
with paybacks of less than 7 years and customers with annual energy use between
0.5 and 1.0 MW.
Figure 2. LIPA Customers Payback Rates
Figure 2 shows that for any customer size (in terms of annual electricity
use), there is significant variation in payback rates achieved with CG/CHP
systems.
Customer diversity displayed in the figures above presents special problems
in determining DG/CHP impacts for a utility service area. Analysis based
on a limited number of average or typical customer types in this kind of
diverse market can not adequately represent the diversity of site economics
within a service area and therefore cannot adequately reflect service area
DG/CHP impacts.
Microsimulation Addresses These Difficult DG/CHP Assessment Issues
The process of evaluating or simulating a DG/CHP system installtion for a
sample of individual customers is called microsimulation . This methodology
was developed specifically to provide analysis of markets with the kind of
customer diversity displayed in the figures above; it is an analytical process
used in energy analysis since the 1970's. Microsimulation is an appropriate
quantitative method for incorporating the DG/CHP analysis issues raised above.
Microsimulation applies individual DG/CHP analysis to a statistically
representative sample of utility service area customers and statistically
extrapolates the results of this sample to the population.
This microsimulation process is used to estimate the potential LIPA revenue
impacts of the customer onsite DG and CHP systems. Analysis was conducted
for a sample of 2185 customers and the results were estimated for the entire
service area and aggregated to the business segments shown in the figure
below.
Figure 3. LIPA Service Area Potential DG/CHP Revenue Threats
Results of this analysis indicate current LIPA potential revenue impacts
total nearly $300 million per year with more 30 percent of that potential
in office buildings.
This microsimulation analysis can be conducted with alternative natural gas
prices and various assumptions on changes in the cost and efficiency of DG
and CHP systems in the future.
Microsimulation model-based utility service area DG/CHP impact analysis has
a number of advantages including:
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Revenue, energy , hourly load and other impacts of individual buildings analysis
can be aggregated to determine service-area impacts and impacts by customer
segments
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Representative customer samples insure accurate service area estimates
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Analysis can be repeated with alternative equipment prices and characteristics
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Analysis can be repeated with alternative electric and gas prices
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Analysis can be applied to forecast future DG/CHP impacts
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