Declining DG/CHP system package costs, more favorable regulatory treatment, and the development of ever smaller and more efficient DG and CHP systems ensures 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. Data and analysis sources are documented at http://www.dgmarketplace.com.

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:

• 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
• 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 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.

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:

• Utilization of waste heat at the customer’s site can transform unattractive generation-only applications into money-saving CHP applications.
• 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 installation 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:

• Revenue, energy , hourly load and other impacts of individual buildings analysis can be aggregated to determine service-area impacts and impacts by customer segments
• Representative customer samples insure accurate service area estimates
• Analysis can be repeated with alternative equipment prices and characteristics
• Analysis can be repeated with alternative electric and gas prices
• Analysis can be applied to forecast future DG/CHP impacts

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