Eric Hirst

The Effectiveness of Incentives for Residential Energy Conservation

Studies evaluating incentive programs for residential energy efficiency are examined to assess the roles of the size and type of incentive and of nonfinancial aspects of the programs and to infer lessons for policy. Larger incentives are found to increase participation, but marketing and implementation may be more important than incentive size: participation varies tenfold between programs offering identical financial incentives, with more participation in programs operated by trusted organizations and aggressively marketed by word of mouth and other attention-getting methods. Preference for grants versus loans varies with income and other household characteristics. Low-income households can be reached by strong incentives if marketing and implementation are designed carefully.

Residential energy use : Analysis of disaggregate data

Abstract The Energy Information Administration recently published data they collected from the National Interim Energy Consumption Survey (NIECS). NIECS includes detailed information on 4081 individual households: demographic characteristics, energy-related features of the structure, heating equipment and appliances therein, recent conservation actions taken by the household, and fuel consumption and costs for the April 1978–March 1979 one-year period. This data set provides a new and valuable resource for analysis. We summarized and analysed the NIECS data on household energy consumption — total energy use, electricity use, and use of the primary space heating fuel. The regression equations constructed explain roughly half the variation in energy use among households. These equations contain 10 or fewer independent variables, the most important of which are fuel price, year house was built, floor area, and heating degree days.

Food-related energy requirements

I have used data from input-output studies to determine the quantities of primary and electric energy consumed in the agricultural, processing, transportation, wholesale and retail trade, and household sectors for personal consumption of food. Before one draws conclusions from these results, it is important to note the assumptions and approximations used in this analysis. First, the economic input-output data published by the Department of Commerce are subject to a number of inaccuracies, including lack of complete coverage for an industry, restriction of data for proprietary reasons, and use of different time periods for different data. Second, aggregation can combine within the same sector industries whose energy intensities differ widely. For example, eating and drinking establishments probably consume more energy per dollar of sales (because of refrigerators, stoves, and freezers) than do department stores. However, both types of establishment are included in retail trade. Thus energy use for food-related retail trade may be underestimated because of aggregation. Third, the energy coefficients are subject to error. In particular, the coefficients for the agricultural and trade sectors are vulnerable because energy use within these sectors is not well documented. Finally, the scaling factor used to estimate food-related energy use for the 1960s is approximate, in that it neglects the possibility that these energy coefficients changed differently with time. Because of these limitations, which are described more fully by Herendeen (6), a number of important issues were not addressed here. such as relative energy requirements for fresh, frozen, and canned vegetables; and for soybeans as compared to beef. This analysis shows that the U.S. food cycle consumes a considerable amount of energy, about 12 percent of the total national energy budget. The residential sector, which accounts for 30 percent of the total, is the most energy-intensive sector in terms of energy consumed per dollar of food-related expenditure. This is because food-related expenditures in homes are primarily for fuel to operate kitchen appliances and automobiles. The electricity consumed in these activities constitutes 22 percent of the total amount used in the United States. More than half of the electricity is used in homes, and more than two-thirds in the trade and household sectors. Thus agriculture and processing consume little electricity relative to the total amount used. From past trends, it appears that the amount of energy used in food-related activities will continue to increase at a rate faster than the population, principally because of growing affluence, that is, the use of processed foods, purchase of meals away from home, and the use of kitchen appliances equipped with energy-intensive devices, such as refrigerators with automatic icemakers. However, fuel shortages, rapidly increasing fuel prices, the growing need to import oil, and a host of other problems related to our use of energy suggest that these past trends will not continue. Fortunately, there are many ways to reduce the amounts of energy used for food-related activities. In the home, for example, smaller refrigerators with thicker insulation would use less electricity than do present units. If closer attention were given to the use of ranges and ovens (for example, if oven doors were not opened so often) energy would be saved. Changes in eating habits could also result in energy savings. Greater reliance on vegetable and grain products, rather than meats, for protein would reduce fuel use. Similarly, a reduction in the amounts of heavily processcd foods consumed—TV dinners and frozen desserts—would save energy. Retailers could save energy by using closed freezers to store food and by reducing the amount of lighting they use. Processors could use heat recovery methods, more efficient processes, and less packaging. Shipping more food by train rather than by truck would also cut energy use. Farmers could reduce their fuel use by combining operations (for example, by harrowing, planting, and fertilizing in the same operation), by reducing tillage practices, by increasing thc use of diesel rather than gasoline engines, and by increasing labor inputs. A partial return to organic farming (that is, greater use of animal manure and crop rotation) would save energy because chemical fertilizers require large energy inputs for their production.

Review of utility home energy audit programs

Evaluation efforts of utilities with active home energy audit programs were reviewed to provide insights into the operations and effectiveness of existing utility home energy audit programs. About half the utilities contacted had little or no evaluation activity. Of those with evaluation activity, most conducted only informal evaluations for in-house use. A few utilities had conducted fully documented formal evaluations. On the basis mainly of written reports received from the utilities, findings about customer response to programs are summarized. The topics discussed include: determinants of program participation rates, use of financing, attitudes toward programs, actions taken, characteristics of participants and energy savings due to programs.

Separating and measuring the regulation and load-following ancillary services

Abstract Together, regulation and load-following address the temporal variations in load (and generation that does not accurately follow control signals). The key distinction between load-following and regulation is the time period over which these fluctuations occur. Regulation responds to rapid load fluctuations (on the order of one minute) and load following responds to slower changes (on the order of five to thirty minutes). Load-following is defined as the 30-minute rolling average of system load; regulation is then the difference between actual load for each 30-second interval and the rolling average. Hourly load-following is defined as the difference between the highest and lowest values of the rolling average within the hour. Regulation is defined as the standard deviation of the 120 regulation values for the hour. Finally, the implications of the current block-scheduling conventions on load following and regulation are discussed, as is the need for a new scheduling convention.

Indoor temperature changes in retrofit homes

The behavioral response (e.g. changes in indoor temperatures, attention to window and door openings) to residential technical efficiency improvements (e.g. attic insulation, storm windows) is an important and largely unresolved issue. Although there is considerable discussion concerning the extent to which households take back some of the energy savings due to technical efficiency improvements in increased comfort, there is almost no empirical evidence on the subject.

The Financial and Physical Insurance Benefits of Price-Responsive Demand

Abstract Implementing price-responsive demand programs requires policymakers to understand and accept the insurance aspects of dynamic pricing. Like other forms of insurance, the benefits are greatest when you most need them.

Key Transmission Planning Issues

Abstract One conclusion: economies of scale in transmission investment argue for overbuilding, rather than underbuilding, transmission. It is substantially cheaper per GW-mile to construct a higher-voltage line than a lower-voltage line.

A model of residential energy use

A model of residential energy use was developed to simulate the period from 1970 to 2000. The model predicts annual energy use by fuel, end use, and type of housing, and also estimates annual equipment installations and ownership, equipment energy re quirements, structural heat retention, fuel expendi tures, equipment costs, and costs for improving heat retention in new and existing housing units. Thus the model provides considerable detail on residential energy uses and associated costs. These details are useful for evaluating the effects of alternative energy conservation policies, programs, and tech nologies during the next quarter century. The present version of the model handles four fuels, eight end uses, and three types of housing. Each of these 96 components of total energy use is calculated each year as a function of stocks of occupied housing units and new construction, equipment ownership by fuel and end use, heat retention of housing units, average unit energy requirements for each type of equipment, and usage factors that depend on household behavior. Simulation of energy use from 1960 to 1975 shows that the model accurately predicts historical data on aggregate energy use, energy use by fuel, energy use by end use, and market shares of ownership of equipment.

The future of DSM in a restructured US electricity industry

During the past several years, more and more electric utilities have been running demand-side management (DSM) programmes. These programmes improve the efficiency with which customers use electricity and affect the timing of that use (eg to shift it away from high-cost times). Utilities run such programmes for two primary reasons. One is to improve customer service. The second is to acquire resources that, just like power plants, can meet customer energy service needs. DSM programmes often are less expensive and environmentally cleaner than power plants. By 1994, US utility DSM programmes had cut potential summer peak demand by 7% and annual electricity use by 2%. We examine the economics of DSM in the late 1990s, reviewing current estimates of avoided supply costs and the cost of conserved electricity for DSM programmes. We review the environmental effects of electricity production and the environmental benefits of DSM programmes. Finally, we consider alternative electric industry structures and how DSM can operate within these alternatives.