Modeling economies of scale: the case of US electric power companies

Hisnanick, John J.; Kymn, Kern O.

doi:10.1016/s0140-9883(98)00019-x

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…This contrasts with the approach taken in econometrics where generation costs, or more typically plant costs, are broken down using a regression model [30,42,7,4,26,49,40,23,27,35,25].…”

Section: Introductionmentioning

confidence: 99%

Historical costs of coal-fired electricity and implications for the future

2011

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We study the costs of coal-fired electricity in the United States between 1882 and 2006 by decomposing it in terms of the price of coal, transportation costs, energy density, thermal efficiency, plant construction cost, interest rate, capacity factor, and operations and maintenance cost. The dominant determinants of costs have been the price of coal and plant construction cost. The price of coal appears to fluctuate more or less randomly while the construction cost follows long-term trends, decreasing from 1902 -1970, increasing from 1970 -1990, and leveling off since then. Our analysis emphasizes the importance of using long time series and comparing electricity generation technologies using decomposed total costs, rather than costs of single components like capital. By taking this approach we find that the history of coal-fired electricity costs suggests there is a fluctuating floor to its future costs, which is determined by coal prices. Even if construction costs resumed a decreasing trend, the cost of coal-based electricity would drop for a while but eventually be determined by the price of coal, which fluctuates while showing no long-term trend.

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“…This contrasts with the approach taken in econometrics where generation costs, or more typically plant costs, are broken down using a regression model [30,42,7,4,26,49,40,23,27,35,25].…”

Section: Introductionmentioning

confidence: 99%

Historical costs of coal-fired electricity and implications for the future

2011

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“…Even in the case of propane replacement, 25% bioheat utilization was required in order to break even in the median case. Offsetting purchased electricity was much less beneficial, because electricity is a relatively cheap commodity that benefits from economies of scale [48]. However, on-farm demand for heat varies widely between farms and between seasons, and careful evaluation is needed before establishing switchgrass for on-farm use.…”

Section: Discussionmentioning

confidence: 99%

Farm-level feasibility of bioenergy depends on variations across multiple sectors

Myhre¹,

Barford²

2013

Environ. Res. Lett.

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The potential supply of bioenergy from farm-grown biomass is uncertain due to several poorly understood or volatile factors, including land availability, yield variability, and energy prices. Although biomass production for liquid fuel has received more attention, here we present a case study of biomass production for renewable heat and power in the state of Wisconsin (US), where heating constitutes at least 30% of total energy demand. Using three bioenergy systems (50 kW, 8.8 MW and 50 MW) and Wisconsin farm-level data, we determined the net farm income effect of producing switchgrass (Panicum virgatum) as a feedstock, either for on-farm use (50 kW system) or for sale to an off-farm energy system operator (8.8 and 50 MW systems). In southern counties, where switchgrass yields approach 10 Mg ha −1 yr −1 , the main determinants of economic feasibility were the available land area per farm, the ability to utilize bioheat, and opportunity cost assumptions. Switchgrass yield temporal variability was less important. For the state median farm size and switchgrass yield, at least 25% (50 kW system) or 50% (8.8 MW system) bioheat utilization was required to economically offset propane or natural gas heat, respectively, and purchased electricity. Offsetting electricity only (50 MW system) did not generate enough revenue to meet switchgrass production expenses. Although the opportunity cost of small-scale (50 kW) on-farm bioenergy generation was higher, it also held greater opportunity for increasing farm net income, especially by replacing propane-based heat.

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“…Dhrymes and Kurz, 1964;Nerlove, 1963;Petersen, 1975;Christensen and Greene, 1976). Confirming this finding, Betancourt and Edwards (1987) and Maloney (2001) compare different model specifications in standard regression settings, Kopp and Smith (1980) and Goto and Tsutsui (2008) account for potential inefficiency using SFA, and Hisnanick and Kymn (1999) analyze the interplay of technical change and RTS in productivity growth. Huettner and Landon (1978) and Schmalensee and Joskow (1986) instead argue that increasing unit size reduces reliability, leading in reverse to a smaller optimal unit size with fewer outages.…”

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confidence: 86%

Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation

Seifert

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Scale characteristics are key properties of production functions that determine optimal firm sizes, and have considerable policy implications for sectors undergoing restructuring. However, estimates of scale characteristics typically vary with the assumptions of the underlying empirical model. This paper derives estimators of scale efficiency and scale elasticity for semi-parametric stochastic nonsmooth envelopment of data (StoNED) that are based on few assumptions and rely neither on a functional form nor on distributional assumptions, but satisfy basic microeconomic properties. The estimators are applied to a unique sample covering 124 natural gas-fired power plants operating in Germany in 2011. Terms of use: Documents inResults indicate that on average plants operate under constant to slightly decreasing returns-to-scale, and scale inefficiency is found to be overall rather low. However, considerable improvement potential exists due to technical inefficiency. The results allow the strong fragmentation of gas-fired electricity generation in Germany, but emphasize the importance of using best practices on plant level.JEL-Codes: D24,C14,O13,L94

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Modeling economies of scale: the case of US electric power companies

Cited by 21 publications

References 14 publications

Historical costs of coal-fired electricity and implications for the future

Historical costs of coal-fired electricity and implications for the future

Farm-level feasibility of bioenergy depends on variations across multiple sectors

Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation

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