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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.

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.

The Baltimore Gas and Electric Company (BGE) undertook a dynamic pricing experiment to test customer price responsiveness to different dynamic pricing options. The pilot ran during the summers of 2008 and 2009 and was called the Smart Energy Pricing (SEP) Pilot . In 2008, it tested two types of dynamic pricing tariffs: critical peak pricing (CPP) and peak time rebate (PTR) tariffs. About a thousand customers were randomly placed on these tariffs and some of them were paired with one of two enabling technologies, a device known as the Energy Orb and a switch for cycling central air conditioners. The usage of a randomly chosen control group of customers was also monitored during the same time period. In 2009, BGE repeated the pilot program with the same customers who participated in the 2008 pilot, but this time it only tested the PTR tariff. In this paper, we estimate a constant elasticity of substitution (CES) model on the SEP pilot’s hourly consumption, pricing and weather data. We derive substitution and daily price elasticities and predictive equations for estimating the magnitude of demand response under a variety of dynamic prices. We also test for the persistence of impacts across the two summers. In addition, we report average peak demand reduction for each of the treatment cells in the SEP pilot and compare the findings with those reported from earlier pilots. These results show conclusively that it is possible to incentivize customers to reduce their peak period loads using price signals. More importantly, these reductions do not wear off when the pricing plans are implemented over two consecutive summers. Our analyses reveal that SEP participants reduced their peak usages in the range of 18 to 33% in the first summer of the SEP pilot and continued these reductions in the second summer.

The rollout of smart meters has enabled the provision of dynamic pricing to residential customers. However, doubts remain whether households can respond to time-varying price signals and that is preventing the full-scale rollout of dynamic pricing and the attainment of economic efficiency. Experiments are being conducted to test price responsiveness. We analyze data from a pilot in Michigan which featured two dynamic pricing rates and an enabling technology. Unlike most other pilots, it also included a group of “information only” customers who were provided information on time-varying prices but billed on standard rates. Similarly, unlike most other pilots, it also included two control groups, one of whom knew they were in the pilot and one of whom did not. This was designed to test for the presence of a Hawthorne effect. Consistent with the large body of experimental literature, we find that customers, including low-income participants, do respond to dynamic pricing. We also find that the response to critical peak pricing rates is similar to the response to peak time rebates, consistent with the finding of one prior experiment but inconsistent with the finding of two prior experiments. We also find that the “information only” customers respond to the provision of pricing information but at a substantially lower rate than the customers on dynamic pricing. We find that the response to enabling technology is muted. We do not find any evidence to suggest that a Hawthorne effect existed in this experiment.

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 (CI) 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 CI customer response to residential response, we find that small CI 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. [PUBLICATIONABSTRACT]

In just about any market-driven economy, prices play a central role. Prices can play the same role in the market for electricity. The residential market predominantly uses flat-rate pricing, which ignores the temporal variation in the cost of providing electricity. Various groups opposed to the rollout of dynamic prices have united under the banner of consumer protection to demand that flat-rate pricing of electricity remain the norm. However, flat-rate pricing for everyone leads to high prices for everyone. By contrast, time-varying prices, whether simple time-of-use or fully dynamic, act like a shock absorber. They help reduce or eliminate the need for expensive peaking capacity and lower (average) costs for everyone. If society is concerned about the plight of its less fortunate members and regards certain items as necessities, it could provide an income subsidy to the poor through an \"earned income tax credit.\" A second-best solution would be to design a product-specific subsidy for electricity, similar to food stamps.

California's power crisis has implications for power markets world wide, because of the severity and unpredictability of its impacts. This paper discusses the causes of the crisis and derives lessons for energy policy makers. The crisis was triggered by a fundamental imbalance between the growing demand for power and stagnant power supply. California's market design greatly magnified the problem, by disconnecting the retail and wholesale markets for electricity, and by requiring the investor-owned utilities to buy their power on a spot market. Low hydro conditions, hot weather, and rising natural gas prices put the market over the edge. A major lesson that has been learned is to introduce demand elasticity in restructured market designs, and permit buyers of power to hedge against price volatility by engaging in forward contracts.