Derived demand

Abstract: Recent micro-simulation studies of the demand for clectricity hy residences have attempted to modlel jointly the demand for appliance and the denmanid for electricity by appliance. Within this context it becomes important to test the statistical exogeneity of appliance dummy variables typically included in demand for electricity equations. If, as the theory would suggest, the demand for durables and their use are related decisions by the consumer, specifications which ignore this fact will lead to biased and inconsistent estimates of price and income elasticities. The present paper attempts to test this bias using a subsample of the 1975 survey of 3249 households carried out by the Washington Center for Metropolitan Studies (WCMS) for the Federal Energy Administration. We discuss and derive a unified model of the demand for consumer durables and the derived demand for electricity. To determine the magnitude of the bias resulting from estimating a unit electricity" consumption (UEC) equation bv ordinary least squares when unobserved factors influence both choice of appliances and intensity of use. we intr-oduce and cstimate a joint water-heat space-heat choice model, and concluide with the consistent estimation and specification of demand for electricity equations.

Abstract: Industrial demand for energy is essentially a derived demand: the firm's demand for energy is an input is derived from demand for the firm's output. Inputs other than energy typically also enter the firm's production process. Since firms tend to choose that bundle of inputs which minimized the total cost of producing a giving level of output, the derived demand for inputs, including energy, depends on the level of output, the submitions possibilies among inputs allow by production technology, and the relative prices of all inputs.

Abstract: Since the energy crisis of 2000–2001 in the western United States, much attention has been given to boosting demand response in electricity markets. One of the best ways to let that happen is to pass through wholesale energy costs to retail customers. This can be accomplished by letting retail prices vary dynamically, either entirely or partly. For the overwhelming majority of customers, that requires a change out of the metering infrastructure, which may cost as much as $40 billion for the US as a whole. While a good portion of this investment can be covered by savings in distribution system costs, about 40% may remain uncovered. This investment gap could be covered by reductions in power generation costs that could be brought about through demand response. Thus, state regulators in many states are investigating whether customers will respond to the higher prices by lowering demand and if so, by how much. To help inform this assessment, this paper surveys the evidence from the 15 most recent pilots, experiments and full-scale implementations of dynamic pricing of electricity. It finds conclusive evidence that households respond to higher prices by lowering usage. The magnitude of price response depends on several factors, such as the magnitude of the price increase, the presence of central air conditioning and the availability of enabling technologies such as two-way programmable communicating thermostats and always-on gateway systems that allow multiple end-uses to be controlled remotely. In addition, the design of the studies, the tools used to analyze the data and the geography of the assessment influence demand response. Across the range of experiments studied, time-of-use rates induce a drop in peak demand that ranges between 3 and 6% and critical-peak pricing (CPP) tariffs induce a drop in peak demand that ranges between 13 and 20%. When accompanied with enabling technologies, the latter set of tariffs lead to a reduction in peak demand in the 27–44% range.