The impact of electricity market design upon investment under uncertainty: The effectiveness of capacity mechanisms

Vries, L.J. De; Heijnen, Petra

doi:10.1016/j.jup.2007.12.002

Cited by 92 publications

(41 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14 shows variations in the output by varying one parameter while averaging over the other (instead of holding the other constant). Table 3 provides the variance contributions of each calibration parameter to the total variance of the model output in terms of the main effect index defined in (10). It is clear that the installed thermal capacity is much more sensitive to the change in the price markup model parameter θ markup than the risk-preference parameter θ V aR .…”

Section: Sensitivity Resultsmentioning

confidence: 99%

“…Examples of power system models to which the emulation methods described in this paper could be applied include: models discussed in [39,40], which are used in GB Electricity Market Reform; a model presented in [13] to study the capacity market proposal of PJM; and models described in [7,[9][10][11][12] which are used for academic and industrial studies of electricity markets. The particular modeling methods used in these studies are different to those used in the LTGI model described in this paper.…”

Section: Generality Of the Bayesian Frameworkmentioning

confidence: 99%

“…Various long-term generation investment (LTGI) models have been developed for predicting real-world generation projections and hence guiding investment decisions and the design of energy policy [6][7][8][9][10][11][12][13]. From the perspective of policymakers, who wish to adequately account for uncertainty around future generation projections related to the real world, it becomes increasingly important to consider two main sources of uncertainty existing in these models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

Wilson

Dent

2016

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

. (2016) 'Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation.', Sustainable energy, grids and networks., 5 . pp. 58-69. Further information on publisher's website: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractInvestments in generation are high risk, and the introduction of renewable technologies exacerbated concern over capacity adequacy in future power systems. Long-term generation investment (LTGI) models are often used by policymakers to provide future projections given different input configurations. To understand both uncertainty around these projections and the ways they relate to the realworld, LTGI models can be calibrated and then used to make predictions or perform a sensitivity analysis (SA). However, LTGI models are generally computationally intensive and so only a limited number of simulations can be carried out. This paper demonstrates that the techniques of Bayesian emulation can be applied to efficiently perform calibration, prediction and SA for such complex LTGI models. A case study relating to GB power system generation planning is presented. Calibration reduces the uncertainty over a subset of model inputs and estimates the discrepancy between the model and the real power system. A plausible range of future projections that is consistent with the available knowledge (both historical observations and expert knowledge) can be predicted. The most important uncertain inputs are identified through a comprehensive SA. The results show that the use of calibration and SA approaches enables better decision making for both investors and policymakers. Keywords: Generation investments, calibration, uncertainty analysis, sensitivity analysis, Bayesian emulation Nomenclature Sets and Functions TSet of planning years of interest, indexed by t. P Set of past planning years. Set of fuel types including uranium, coal, gas, and carbon.Functions of the simulator and the emulator, respectively.Functions of the main modules within the simulator. GP(·, ·)Gaussian process function. p(·)Probability distribution function. Parameters and Variables

show abstract

Section: Sensitivity Resultsmentioning

confidence: 99%

Section: Generality Of the Bayesian Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

Wilson

Dent

2016

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

show abstract

“…For this aim, these studies predominantly use stochastic programming techniques under the assumption of inelastic demand (de Vries and Heijnen (2008), Hobbs et al (2002), Vásquez et al (2002)). Overall, the results of these modeling exercises are inconclusive, and it is stressed that the relative advantages of certain policies over competing measures are depending on the system characteristics under consideration.…”

Section: Gdp In Central Europementioning

confidence: 99%

Capacity Mechanisms on Central European Electricity Markets: Effects on Consumers, Producers and Technologies Until 2033

Traber

2014

SSRN Journal

View full text Add to dashboard Cite

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. Terms of use: Documents in AbstractThe reduced attractiveness of investments in reliable power plants under conditions of liberalized markets and the transition towards renewable energies has brought a discussion on capacity policies to Europe. We use a partial equilibrium model to compare important effects of three basic policies. A strategic reserve policy and a capacity market policy with administratively set capacity targets, and the obligation of generators to hold certificates of reliable capacities in relation to their supply. We find important differences of policies for consumers and producers that are depending on existing power plant structure and the elasticity of demand particularly in the medium term perspective until the year 2023. In the longer term until 2033 the results differ less pronounced. However, for the German case we demonstrate the potential to effectively reduce the burden on the economy to achieve a prescribed target through the implementation of a capacity certificate system.

show abstract

“…The model has been developed using concepts and tools from system dynamics, which is a branch of control theory applied to economic and management problems. This methodology has been extensively used in electricity market modeling to represent capacity expansion planning in wholesale markets [14,3,12,13,10]. The results show that in the long-run different policies should affect both investments and social costs, a market-driven development of demand response with the internalization of the CO 2 externality being the most efficient approach.…”

Section: Introductionmentioning

confidence: 99%

Assessing long-term effects of demand response policies in wholesale electricity markets

Cepeda

Saguan²

2016

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

The impact of electricity market design upon investment under uncertainty: The effectiveness of capacity mechanisms

Cited by 92 publications

References 12 publications

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

Capacity Mechanisms on Central European Electricity Markets: Effects on Consumers, Producers and Technologies Until 2033

Assessing long-term effects of demand response policies in wholesale electricity markets

Contact Info

Product

Resources

About