923 resultados para variable power, cycle-run, stochastic cycling


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To shift to a low-carbon economy, the EU has been encouraging the deployment of variable renewable energy sources (VRE). However, VRE lack of competitiveness and their technical specificities have substantially raised the cost of the transition. Economic evaluations show that VRE life-cycle costs of electricity generation are still today higher than those of conventional thermal power plants. Member States have consequently adopted dedicated policies to support them. In addition, Ueckerdt et al. (2013) show that when integrated to the power system, VRE induce supplementary not-accounted-for costs. This paper first exposes the rationale of EU renewables goals, the EU targets and current deployment. It then explains why the LCOE metric is not appropriate to compute VRE costs by describing integration costs, their magnitude and their implications. Finally, it analyses the consequences for the power system and policy options. The paper shows that the EU has greatly underestimated VRE direct and indirect costs and that policymakers have failed to take into account the burden caused by renewable energy and the return of State support policies. Indeed, induced market distortions have been shattering the whole power system and have undermined competition in the Internal Energy Market. EU policymakers can nonetheless take full account of this negative trend and reverse it by relying on competition rules, setting-up a framework to collect robust EU-wide data, redesigning the architecture of the electricity system and relying on EU regulators.

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Added t.p.: Magnitogidrodinamicheskoe preobrazovanie ėnergii otkrytyĭ t︠s︡ikl.

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Thesis (Master's)--University of Washington, 2016-06

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Two stochastic production frontier models are formulated within the generalized production function framework popularized by Zellner and Revankar (Rev. Econ. Stud. 36 (1969) 241) and Zellner and Ryu (J. Appl. Econometrics 13 (1998) 101). This framework is convenient for parsimonious modeling of a production function with returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered. In the first the errors are added to an equation of the form h(log y, theta) = log f (x, beta) where y denotes output, x is a vector of inputs and (theta, beta) are parameters. In the second the equation h(log y,theta) = log f(x, beta) is solved for log y to yield a solution of the form log y = g[theta, log f(x, beta)] and the errors are added to this equation. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, for firm efficiencies and for the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined. (c) 2004 Elsevier B.V. All rights reserved.

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2000 Mathematics Subject Classification: 37F21, 70H20, 37L40, 37C40, 91G80, 93E20.