The capture potential forecast is based on the assumption that SES market growth will follow the normal S-shaped curve over time and over a given set of conditions. These conditions measure system economic performance and reflect our determination of key buying factors and other qualitative purchase decision criteria (identified in Figure 3-1) relevant to each building type. Because buying factors and the decision-making process do differ among building types, we have defined a separate set of conditions and have designed a separate set of curves for each building type: singlefamily residence (SFR), low-rise multi-family (MF), schools, and commercial buildings (offices and stores). Also, because the SES industry will be in a different stage of the product life cycle, and thus will experience a different rate of market acceptance over a given set of conditions in each of the three decades, we have designed separate curves for each decade. To actually design the curves, we define two market capture rates (based on results of discussions with consumers and key decision-makers) along the given set of conditions, and from these two points are able to calculate the mean and standard deviation of each normal curve, and thus the shape of the curve. The actual performance of the system (defined by the same set of conditions) is then measured against the mean, and the corresponding Z value defines the actual market penetration rate. An example of this procedure for multi-family apartments is presented in Table 3-3. A brief discussion of the rationale for each curve follows. * In the SFR market, the developer is the key decision-maker, and SES industry efforts must focus there (in the custom market, the owner also can play a direct role in the purchase decision). However, the developer will make purchase decisions based on assessments of market demands. Thus performance criteria is defined in terms of consumer requirements. * Z equals the system performance minus the mean of the distribution divided by the standard deviation of the distribution. It measures the area under the curve between the mean and the system performance coordinate. Since half of the area under the curve lies between the mean and the lowest point on the curve, we subtract the area between the mean and the system performance from .5 to determine the cumulative capture up to and including the system performance coordinate. Table 3-3 Capture Potential Methodology - Example 1. Assume Lower Bound: 1.5% of Market at 10% discounted ROI 2. Calculations a. = Determine mean (x) and standard deviation (%) of the curve: པ = 18.9% ROI This means that a 51% ROI will result in a 50% market capture. A Z value of -1.9 equates to 2.87% market The consumer is relatively unsophisticated regarding financial analysis, and primarily will be interested in fuel savings resulting from SES purchase, compared to the incremental SES mortgage and maintenance cost. Present value, life cycle, and Return on Investment (ROI) considerations will not occur to the average consumer. Thus, the performance criteria reflects the relationship between incremental SES cost and fuel savings. In 1980, given essentially an unproved product, the consumer will not consider a pay-back period as a relevant consideration, but will demand that in the first year of system operation, savings generated by the SES equal the incremental payments (mortgage plus maintenance) resulting from SES installation. This condition defines our minimum capture coordinate on the curve. In 1990 and 2000, SES's will be a proved product with proved performance. Thus the consumer can reasonably consider an operating cost payback period in defining a minimum purchase decision. We assert that the consumer will be interested only in a pay-back period of less than five years, reflecting the fact that the average consumer moves once every five years, and thus would not be interested in a system that did not pay back within that time period. Thus, given fuel savings and projected fuel price increases, we calculate the maintenance and annuity payment (and thus consider present value) that would result in a five-year operating cost payback of the system, and define this amount as the lower bound on market capture. Decision-makers in multi-family and commercial markets are more sophisticated than SFR consumers, and are demonstrating increased usage of cash flow and ROI as financial evaluation techniques and final decision criteria. Thus, we consider discounted ROI to be the most indicative and relevant economic performance measure for these market sectors. We calculate the ROI by assuming a 25% down payment as an outflow in the first year, and define the income (or deficit) stream by fuel savings minus the annual incremental SES mortgage and maintenance cost. We measure the ROI over a five-year period, and thus calculate the discount rate which equates this income stream to zero over the five-year period. Depending on the specific market sector (multi-family sector by investor- or owner-operated, versus commercial sector) we then define a discounted ROI rate which would generate a minimum market, and define a second market capture ROI, and thus define the normal curve in the same manner as mentioned for the SFR market. School building decision-makers have the highest awareness of lifecycle costing requirements and definitely consider pay-back implications in making purchase decisions. Thus we define economic performance and criteria in terms of total system cost pay-back, and define the normal curve in a manner similar to the SFR, MF, and commercial markets. Optimum System Definition Actual system performance and relative competitiveness vary depending on regional factors of total building load, percent solar utilization, fuel price, and other quantitative factors (Tables 3-1 and 3-2). Thus different regions reflect different optimum system performance, and it is important to determine the optimum system for each SMSA and region. This optimum system performance point is determined by inputting varying collector sizes for each SMSA study case, calculating resultant market capture rates and total dollar markets, and selecting that system generating the highest dollar market. Through this procedure, optimum collector size and optimum solar utilization for each SMSA and thus for each region is determined. Fuel Availability Since fuel prices determine the relative competitiveness of SES to conventional systems, we measure SES market performance under each energy source fuel price. Solar competes most favorably against electricity, and is hardly competitive against gas until about 1995. Thus, relative market size for each region and SMSA will depend on the relative usage of electricity compared to gas, and to a lesser extent oil. Our program calculates the market capture rate, and then adjusts the unit market depending on percent usage of each fuel type. Thus, market capture of 10% with electricity would, for example, result in an overall market capture of 5% if 50% of the market were supplied with electricity, and 1% if only 10% of the market were supplied with electricity. Thus the importance of relative fuel type usage is apparent. Our final penetration rates reflect overall market capture. Solar Energy System Market The output of the entire capture potential analysis is a regional and U.S. market for SES by units, dollars, total square feet, and percentage market capture, as well as a definition of major qualitative and quantitative forcing functions which can affect this market. These results form the basis for the social, environmental, and economic impact assessment, and for determining the Proof of Concept Experiments and market strategy or implementation planning. We selected four alternate cases to illustrate relative SES market performance, given varying economic and government role assumptions. These assumptions reflect our determination of economic factors which will have a strong influence on SES market performance: capital cost, fuel price and availability, and potential government financial participation. Case I, the base case, is the most conservative estimate of possible SES market performance. It is based on our detailed cost estimates and on relatively conservative estimates of fuel price escalation (see Table 3-4). These fuel price estimates are based on a model embodying the important cost parameters of the electric and gas utility industry. The base case also assumes the current electric utility practice of preferential rates for single-family homes and a slight moderation of the recent increasing trend toward electric heating. No direct government incentives to encourage SES purchase are assumed. The SES industry is presumed to be in essentially a market introduction stage. Furthermore, no cost credit is given for the fact that the collector will be integrated into the roof in new construction, resulting in a portion of the cost actually being new construction cost and not incremental SES cost. Case II assumes the same cost and fuel price escalation as the base case, but also assumes direct government incentives of a 25% tax credit on the SES incremental mortgage payment to SFR consumers, and an investment tax credit of 7% for MF and commercial markets. Case III assumes the same system cost, fuel availability, rate structure, and government role as the base case, but assumes a 1980 system cost reduction of 25%. This case would reflect a large government- or industryinitiated R&D effort, and a more advanced SES industry "state of the art". |