Peak Load Pricing

11 Key excerpts on "Peak Load Pricing"

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  Microeconomics

  Theory and Applications

  • Edgar K. Browning, Mark A. Zupan(Authors)
  • 2019(Publication Date)
  • Wiley

    (Publisher)

  With peak-load pricing the quantity demanded in the peak period is less, so a smaller scale of operation is feasible. In terms of building adequate capacity, peak-load pricing means that the telephone company has to build and maintain less switching capacity. This cost saving also represents an efficiency gain from peak-load pricing. To a significant extent, the efficiency gains from peak-load pricing depend on the ability of users to curtail their consumption when confronted with a higher price during the peak period. The options here are greater than might be imagined. Some adjustments are quite simple. In the case of electricity production in Vermont, for example, a system of peak-load pricing has been used since 1974. Vermont families commonly fill dishwashers after dinner but do not turn them on until late at night, when rates fall. Businesses are also capable of adjusting their demand in response to a system of peak- load pricing. A case in point is provided by the Kohler Corporation, in Kohler, Wisconsin. Using Congestion and Surge Pricing to Combat Spikes in Traffic Demand In 2019, New York became the first American city to adopt congestion pricing as a way to address traffic gridlock. 8 The plan agreed to by Governor Andrew Cuomo and the State legislature envisioned charging drivers $12 to $14 for cars and $25 for trucks entering Manhattan below 60th Street during peak business (and traffic) hours. The congestion pricing will go into effect by 2021 and generate an esti- mated $1 billion annually in net revenue. These incremental funds will be devoted to undertaking repairs of and improvements to the City’s public transit system. London, Stockholm, and Singapore are three cities around the world that have already implemented a peak- load pricing system to deal with spikes in traffic demand.

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  Modern Economic Regulation

  An Introduction to Theory and Practice

  • Christopher Decker(Author)
  • 2014(Publication Date)
  • Cambridge University Press

    (Publisher)

  See Steiner ( 1957 :590); Joskow ( 2007a :1283). 59 There is an extensive literature on this topic. A good survey is presented in Crew, Fernando and Kleindorfer ( 1995 :215). See also Berg and Tschirhart ( 1988 : chp 5 ). Ault and Ekelund ( 1987 :655) present a historical account of the development of peak-load pricing. According to Joskow ( 2007a :1281) peak-load pricing principles were applied extensively in electricity pricing in France and England during the 1950s and 1960s, while Berg and Tschirhart ( 1988 :179) observe that peak-load pricing was studied in the mid 1970s by some of the more progressive state PUCs in the USA. 58 For example, the network assets must be able to accommodate the flow of power when it is actually demanded by customers (such as on a hot summer’s afternoon), or in the case of telephone or broadband networks, there must be sufficient capacity available when people actually want to talk to one another or use the Internet. Put differently, having spare network capacity available at 3 a.m. in the morning on the telephone, electricity or rail networks is unlikely to be of any use to anyone, as demand is generally low at that time. 4.3 Pricing principles: the multi-product firm 85 that units of consumption in peak and off-peak periods are essentially separate products and that efficiency requires their prices reflect their respective marginal supply costs. 62 However, the standard peak-load pricing result is sensitive to a number of assump-tions. 63 One important assumption is that demand at different points in time is largely independent. That is, consumers in peak periods are unlikely to consider consumption in an off-peak period as a substitute, and vice versa. In practice, however, there can be a close degree of substitutability between consumption in different periods and, depending on the relative price difference, some peak consumers may decide to switch to off-peak consumption.

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  Business Economics and Managerial Decision Making

  • Trefor Jones(Author)
  • 2004(Publication Date)
  • Wiley

    (Publisher)

  Peak Load Pricing When demand varies signi¢cantly by time of the day, the week or the year and costs of supply vary with the level of demand, then price structures may be constructed to re£ect the variations in costs or to limit investment in capacity. For example, a hair- dresser’s salon may ¢nd that demand for its services are signi¢cantly higher on Friday and Saturday, so that demand exceeds the capacity of the establishment, whereas on other days of the week demand is much less than capacity. One way for the hairdresser to bring demand into line with available capacity is to lower prices on Mondays to Thursdays and to increase prices on Friday and Saturday. If demand exceeds capacity su⁄ciently, then it may be in the interests of the ¢rm at some point to invest in new capacity, to employ more hairdressers and to meet a higher level of demand. In this instance the variation in price at peak is intended to limit demand, so 206 PART IV g PRICING, PROMOTIONAL AND INVESTMENT POLICIES G F O Q 1 Quantity Price/Expenditure P E Figure 10.4 Two-part tari¡ that the peak price is not explicitly related to costs. The hairdresser is also exploiting di¡erences in the willingness of individual consumers to pay higher prices on peak days. Assume a ¢rm has two separate and independent demand curves for its services, separated by time of the week. Its short-run marginal cost curve increases with the quantity sold to capacity, at which point it rises vertically. The short-run average cost curve is shown as falling to capacity output Q C and then increasing; this situation is illustrated in Figure 10.5. If a hairdresser charges a single price OP A in both periods, then demand for its services will be OQ 1 in o¡-peak periods and OQ 2 in the peak period. The ¢rm can meet demand in the o¡-peak period and still have excess capacity, while demand OQ 2 exceeds the capacity of OQ C in the peak period.

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  Handbook of Marketing

  • Barton A Weitz, Robin Wensley, Barton A Weitz, Robin Wensley(Authors)
  • 2002(Publication Date)
  • SAGE Publications Ltd

    (Publisher)

  users could easily get service from a competitor. Moreover, when competitors are not at capacity, they are most attractive to our users because our competitors will likely have lower off-peak prices and higher off-peak levels of service. The timing of competitive peak periods also influences our capacity and service strategy. When we face long periods of peak demand, and we face the same periods of peak demand as competitors, we have a relatively easy task managing demand. We can invest in larger capacity because we can more easily extract profits during periods of peak demand. In contrast, suppose that we face short periods of peak demand and that we face different periods of peak demand than competitors. Here, we have a far more difficult task. We cannot invest as much in capacity because we are unable to extract sufficient profits during the peak period for two reasons. First, the brevity of the peak period shortens the duration of peak profits and, thereby, decreases our total peak profits. Second, competitors offer credible alternatives for customers during times of our peak demand. So we are less able to extract profits during the peak because we would quickly lose customers to our competitors. Pricing Strategies To some extent, all price increases during times of peak-demand indirectly shift demand to off-peak periods. A higher rate for telephone calls during peak hours often causes some callers to shift their calls to lower-rate off-peak periods. Hence, we may not need an explicit strategy for shifting demand. When demand is perfectly predictable, we need not consider shifting when setting the peak price. We should increase the peak price until the capacity constraint is just binding. In other words, we increase the peak price until peak demand exactly equals our capacity. We should have no excess capacity and no excess demand. We do, however, need to consider shifting when we set the peak price when demand is somewhat unpredictable.

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  Peak Energy Demand and Demand Side Response

  • Jacopo Torriti(Author)
  • 2015(Publication Date)
  • Routledge

    (Publisher)

  6 Simulating Critical Peak Pricing DOI: 10.4324/9781315781099-6

  The roll-out of smart meters, the integration of renewable sources of energy and thinner capacity margins are likely to create the conditions for higher penetration of Critical Peak Pricing (CPP) programmes in residential electricity markets. This will foster Revenue Management practices by energy suppliers in the attempt to achieve efficient demand shifting even when the peak time is unknown. By implementing Revenue Management, utilities assume that demand is shaped by preferences, consumers act as individuals and react to significant changes in price. In keeping with this approach, this chapter treats peak electricity demand as consumption. This chapter makes use of a two-period demand model which introduces demand shifting and considers heterogeneity in end-users’ costs of switching demand. It is based on end-users’ demand profiles from the UK Government’s Household Electricity Survey study. Findings highlight how the distribution of time-pressured and time-elastic end-users affects changes in price and expected utility of end-users.

  Introduction to Critical Peak Pricing

  The use of price-based DSR programmes is based on different levels of uncertainty around when peak electricity demand occurs. With Time of Use tariffs (Chapter 5 ), the repetition and routines of everyday lives and practices create the conditions for certainty around the timing of peak demand. In the past, stochastic peak-load models assumed suppliers have prior information about when peak loads would take place. However, the integration of intermittent renewable sources of energy in the supply mix increases the level of uncertainty regarding the size of the problem of peak demand. The massive roll-out of smart meters is likely to foster the uptake of dynamic pricing programmes, including Critical Peak Pricing (CPP), i.e. tariffs where prices are based on critical peaks for certain hours on event days. CPP is generally seen as an effective way of addressing peak electricity demand through price, as these are relatively rare forms of intervention associated with significant increases in price (Newsham et al., 2010 ; Faruqui and Sergici, 2010 ). In the future, critical peaks are likely to occur only a limited amount of times per year, somewhere in the region of 10–15 events per year, but may push prices up to 3–10 times as much as default tariffs (Energy Insights, 2012 ). The event days may be advertised by the utility a day in advance, based on their forecast of a particularly high demand. In the low-carbon future, a higher number of peak events is likely to occur because of unusually high demand with the connection of electric vehicles and electric heat pumps (Foxon, 2013

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  Energy Analysis and Policy

  Selected Works

  • Mohan Munasinghe(Author)
  • 2013(Publication Date)
  • Butterworth-Heinemann

    (Publisher)

  op does not infringe on the capacity limit QM. The optimal pricing rule now has two parts corresponding to two distinct rating periods (i.e., differentiated by the time of day):

  Figure 3.6 Peak Load Pricing Model

  peak period price ppk = a + b

  off-peak period price pop = a

  The logic of this simple result is that peak period users, who are the cause of capacity additions, should bear full responsibility for the capacity costs as well as fuel, operating, and maintenance costs, while off-peak consumers pay only the latter costs. Peak Load Pricing can also be applied in different seasons of the year2 . More sophisticated Peak Load Pricing models indicate that in an optimally planned system, marginal capacity costs should be allocated in proportion to marginal shortage costs during two or more different rating periods. If the peak period is too narrowly defined, Peak Load Pricing may shift the peak to another rating period; this would be an extreme case of price feedback effects, which were discussed earlier.

  Related problems of allocating joint costs arise in other energy subsectors as well -- an example is the allocation of capacity costs of natural gas, or of refinery costs among different petroleum products. The former may be treated like the electricity case. For oil products, the light refinery cuts that are tradable, such as kerosene, gasoline, and diesel, have benchmark international prices. However, other items like heavy residual oils may have to be treated like nontradables. Furthermore, associated gas that may be flared at the refinery is often assumed to have a low MOC, although subsequent storage and handling for use as LPG will add to the costs. A more complicated approach would be to use a programming model of a refinery to solve the dual problem as a means of determining shadow prices of distillates.

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  Rural Electrification For Development

  Policy Analysis And Applications

  • Mohan Munasinghe(Author)
  • 2019(Publication Date)
  • Routledge

    (Publisher)

  when loads are light. The marginal cost curve is simplified, assuming a single type of plant with fuel, operation, and maintenance costs given by the constant a and the incremental cost of capacity given by the constant b. The static diagram has been drawn to indicate·that the pressure on capacity arises due to peak demand Dpk• while the off-peak demand Dop does not infringe upon the capacity Q. The optimal pricing rule now has two parts, corresponding to two distinct rating periods (differentiated by time of day): peak price: Ppk = a + b off-peak price: P 0 p = a Unit Price ppk 0 Q Figure 8.3. Peak Load Pricing model. kWh The logic of this simple result is that peak period users, who are the cause of capacity additions, should bear full responsibility for capacity costs, as well as fuel and operation and maintenance costs, while off-peak consumers 227 should pay only the latter costs (see Appendix A at the end of this chapter for details). Peak Load Pricing can also be applied in different seasons of the year. Bnension of Siaple Models The simplified models presented so far must be extend-ed to analyze the economics of actual power systems. First. the usual procedure adopted in marginal cost pricing studies might require some iteration. as shown in Figure 8.4. Typically. a deterministic long range demand forecast is made assuming some given future evolution of prices. Then. using power system models and data. several plans are proposed to meet this demand at some fixed target reliability level. This is a more restricted version of the variable reliability model developed in Chapter 4. The cheapest or least cost system expansion plan. is chosen from the alternatives. Finally. strict LRMC is computed on the basis of this least cost plan. and an adjusted LRMC tariff structure is prepared. However. if the new tariff to be imposed on consumers is significantly different from expected prices based on initial assumptions regarding the evolution of prices.

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  Electricity Cost Modeling Calculations

  • Monica Greer(Author)
  • 2010(Publication Date)
  • Academic Press

    (Publisher)

  In 2004, the Government Accountability Office issued a report that concluded that increased use of demand response would improve efficiency in the electric utility industry and recommended that state utility commissions do more to promote demand response programs. The Energy Policy Act of 2005 directed the Secretary of Energy to provide “Congress with a report that identifies and quantifies the national benefits of demand response and make a recommendation on achieving specific levels of such benefits.” The U.S. Department of Energy (DOE) early the following year issued a report titled “Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them.” The act also instructed the Federal Energy Regulatory Commission to assess the use of demand response programs and related metering technologies in the nation, and in August 2006 the FERC released the results of an industrywide survey. In addition, states have showed renewed interest in demand response. For example, the state of California commissioned a study to evaluate the benefits of so-called “critical peak pricing” that would allow sharply higher prices during critical peak times.

  But what does the term “demand response” mean? The term is variously defined, but DOE's definition is representative: Changes in electricity usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.

  The DOE report says that states should consider aggressive implementation of price-based demand response a “high priority.” They should do this because “flat, average-cost retail rates that do not reflect the actual cost to supply power lead to inefficient capital investment in new generation, transmission, and distribution infrastructure and higher electric bills for consumers.”

  More specifically, it states that:

  “ [The] disconnect between short-term marginal electricity production costs and retail rates paid by consumers leads to an inefficient use of resources. Because customers don’t see the underlying short-term cost of supplying electricity, they have little or no incentive to adjust their demand or supply-side conditions. Thus, flat electricity prices encourage customers to over-consume relative to an optimally efficient system in hours when electricity prices are higher than average rates, and under-consume in hours when the cost of producing electricity is lower than average rates. As a result electricity costs may be higher than they would otherwise be because high-cost generators must sometimes run to meet the non-price responsive demands of consumers.”

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  Electricity Deregulation

  Choices and Challenges

  • James M. Griffin, Steven L. Puller, James M. Griffin, Steven L. Puller(Authors)
  • 2009(Publication Date)
  • University of Chicago Press

    (Publisher)

  Puller 14 setting on the air-conditioner or the time of day one operates the dishwasher, washing machine, or dryer, consumers can move demand out of the peak pe-riod into the off-peak period or perhaps choose to use less total electricity. Likewise, industrial users can move demand out of peak periods into off-peak periods by rescheduling downtimes and energy-intensive operations. Yet another factor contributing to the extreme short-run price inelasticity of demand is that almost all retail consumers face a constant price of electric-ity that does not vary as cost varies. Under rate-of-return regulation, con-sumers pay the average cost of generation, which is typically flat over broad ranges of output. Indeed, regulators made little effort to distinguish between costs incurred during peak and off-peak periods. Figure 3 superimposes a hy-pothetical average cost curve on a marginal cost curve resembling the one in figure 2. Under rate of return regulation, consumers would pay a single price equal to average costs weighted over the year. Thus the price consumers pay, P AC , which applies to both peak and off-peak periods, does not reflect the true social cost of power. There is no incentive to move consumption out of peak periods to off-peak periods and thereby conserve on peaking capacity. Many analysts advocate real-time pricing in which consumers pay prices tied to the hourly wholesale price. This would incentivize customers to move consump-tion out of peak to off-peak periods and thereby economize on system-wide capacity. Real-time pricing will send price signals that will evoke some price 15 Introduction Figure 3. Prices under average cost and marginal cost pricing elasticity. Not all consumers will necessarily prefer real-time pricing because it is costly to monitor these prices and adjust consumption. For those who may benefit from real-time pricing, retail prices (on the margin) need to be as volatile as wholesale prices.

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  Still Stuck in Traffic

  Coping with Peak-Hour Traffic Congestion

  • Anthony Downs(Author)
  • 2005(Publication Date)
  • Brookings Institution Press

    (Publisher)

  152 CHAPTER 10 Peak-Hour and Other Road Pricing A highly controversial suggestion for attacking traffic con-gestion is rationing access to major commuter roads by charging variable tolls during peak periods. Those tolls would be set so that the number of drivers willing to pay them would be small enough to permit continuous high-speed driving on the roads involved. As a result, more vehi-cles would be able to use those roads during peak hours than can now use them under free access but congested con-ditions. This improves the efficiency of the entire highway system. Such road pricing is controversial because it disadvan-tages low-income commuters compared with more affluent ones, and it charges what many people consider an “added tax” for something they can now do free—gain access to major roads during peak hours. Yet public and governmen-tal interest in road pricing is increasing because peak-hour congestion is becoming more intense and more widespread in many regions. There is an acute need to raise more money to pay for ground transportation facilities, some ver-sions of road pricing are already being successfully applied without the drawbacks mentioned above, and a new high-tech method of tracking vehicles and charging tolls could eventually make almost universal road pricing possible with minimum inconvenience to drivers. As a result, road pricing has begun evolving toward what might become much more widespread application. So understanding the basic nature of road pricing, how it is now evolving, and how it might evolve is an important aspect of understanding how to cope with peak-hour traffic congestion. 1 The Economic Theory of Peak-Hour Road Pricing Transportation economists argue that although persons driving onto congested roadways during peak hours are adding to collective costs by increasing delays for others, they are not required to pay the full costs generated by their own behavior.

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  Electricity Restructuring in the United States

  Markets and Policy from the 1978 Energy Act to the Present

  • Steve Isser(Author)
  • 2015(Publication Date)
  • Cambridge University Press

    (Publisher)

  However, many large customers have loads that are expensive to curtail on short notice, as the opportunity cost of stopping revenue-producing business activities far exceeds the cost of paying a high electricity price for a few hours. 9 In practice, many customers fac- ing mandatory hourly prices have contracted with third parties to hedge against price volatility rather than attempt to respond to prices. Smaller customers, even when smart meters are installed, face large opportunity costs to respond quickly to prices. 10 Although many smart meters include electronic shut-off switches, these are not designed to be used to rapidly curtail loads, as their purpose is to reduce the cost of switching customers on and off. As technology improves, and the cost of control equipment declines, demand will become more responsive to price, but the speed at which 9 See Jay Zarnikau and Ian Hallett, “Aggregate Industrial Energy Consumer Response to Wholesale Prices in the Restructured Texas Electricity Market,” Energy Economics 20 (2008): 1798–1808. 10 There are large economies of scale associated with controlling demand. At the residential level, the typical peak demand may be 4 kW, so a 25% reduction saves a kWh over a peak hour, which means that even at $10,000/MWh prices, the savings would be only $10 for that hour. As price became more responsive and price spikes were reduced, the value of potential savings would also be reduced. 410 Electricity Restructuring in the United States reliability incidents occur caution against dependence on demand responding to scarcity prices to ensure reliability. Some demand response products such as LaaRs allow the purchaser of demand response to require curtailment, or even directly control customer demand.

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