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Demand for Electricity of Small Nonresidential Customers under Time-Of-Use (TOU) Pricing

Chi-Keung Woo

Year: 1985
Volume: Volume 6
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol6-No4-9
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Abstract:
After the oil crisis of 1973, the California Public Utilities Commission (CPUC) in 1976 ordered Pacific Gas and Electric Company (PGandE) to charge its large nonresidential customers with monthly billing demand of over 4000 kilowatts (kW) mandatory time-of-use rates. Using a translog (TLOG) specification attributable to Christensen, Jorgenson, and Lau (1973), Chung and Aigner (1981) estimate the electricity demand price elasticities by time-of-use for 64 of these customers in 13 Standard Industrial Classification (SIC) code groups. Own-price elasticity estimates are generally around -0.1 and at times can be as high as -0.5, or they have the wrong sign. Cross-price elasticity estimates indicate that electricity usages by time-of-use are mostly substitutes. However, the estimated price responsiveness typically is larger than observed usages (see below and the section, Experimental Design and Data). Moreover, positive own-price elasticity estimates, though not statistically significant, raise further doubts about the validity of empirical results.



Rebates, Loans, and Customers' Choice of Appliance Efficiency Level: Combining Stated- and Revealed-Preference Data

Kenneth E. Train and Terry Atherton

Year: 1995
Volume: Volume16
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol16-No1-4
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Abstract:
Residential customers' choice of efficiency level for appliances, and their participation in demand-side management (DSM) programs, are examined using data on customers' stated preferences in hypothetical (i.e., conjoint-type) situations and their revealed preferences in real-world choices. The analysis provides information on customers' willingness to pay for energy savings, the importance of rebates in customers' decisions, and customers' response to DSM programs that offer loans for purchases of high-efficiency appliances. An estimated model is used to forecast the decisions of customers under: higher rebates, replacement of rebates with finance programs, offering of loans and rebates as alternative options for customers, and the elimination of DSM programs. We find that attractive loans (e.g., low interest rates, long repayment periods) are necessary to have the same effect as rebates. Programs that offer customers the option of loans or rebates are found to be far more effective than programs that offer only loans or only rebates.



The Impact of Dynamic Pricing on Residential and Small Commercial and Industrial Usage: New Experimental Evidence from Connecticut

Ahmad Faruqui, Sanem Sergici, and Lamine Akaba

Year: 2014
Volume: Volume 35
Number: Number 1
DOI: 10.5547/01956574.35.1.8
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Abstract:
Among U.S. households, a quarter have smart meters but only one percent are on any form of dynamic pricing. Commissions and utilities continue to study the potential benefits of dynamic pricing through experimentation but most of it involves the residential sector. We add to that body of knowledge by presenting the results of a pilot in Connecticut which included small commercial and industrial (C&I) customers in addition to residential customers. The pilot featured a time-of-use rate, two dynamic pricing rates and four enabling technologies. Customers were randomly selected and allocated to these rates, to ensure representativeness of the final results. The experiment included a total of around 2,200 customers and ran during the summer of 2009. Using a constant elasticity of substitution model, we find that customers do respond to dynamic pricing, a finding that matches that from most other experiments. We also find that response to critical-peak pricing rates is higher than response to peak-time rebates, unlike some other experiments where similar results were found. Like many other pilots, we find that there is virtually no response to TOU rates with an eight hour peak period. And like the few pilots that have compared small C&I customer response to residential response, we find that small C&I customers are less price responsive than residential customers. We also find that some enabling technologies boost price responsiveness but that the Energy Orb does not.





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