Fuel mix diversification incentives in liberalized electricity markets: A Mean–Variance Portfolio theory approach

Roques, Fabien; Newbery, David; Nuttall, William J.

doi:10.1016/j.eneco.2007.11.008

Cited by 260 publications

(187 citation statements)

References 17 publications

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“…Mean fuel prices are obtained from (ACIL Tasman, 2009) while their SD of gas and coal price used in this study is based on their historical trend which is estimated to be 30% and 10% of their respective mean value (Blyth, 2008;IEA, 2009b;Roques et al, 2008). These values are shown in Table 3.…”

Section: Stochastic Model Of Uncertain Parametersmentioning

confidence: 99%

“…These correlations are based on historical trends in Europe and some assumptions, which are approximately in line with recent studies (Awerbuch and Yang, 2008;Jansen et al, 2006;Yang and Blyth, 2007). Whilst not necessarily applicable to the actual fuel supply situation in the region being studies, they do highlight the importance of considering such factors in planning (Roques et al, 2008). Correlated samples of gas, coal and carbon prices are generated from their marginal lognormal distributions using the multivariate Monte Carlo simulation procedure.…”

Section: Stochastic Model Of Uncertain Parametersmentioning

confidence: 99%

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Assessing the value of wind generation in future carbon constrained electricity industries

Vithayasrichareon

MacGill

2013

Energy Policy

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Section: Stochastic Model Of Uncertain Parametersmentioning

confidence: 99%

Section: Stochastic Model Of Uncertain Parametersmentioning

confidence: 99%

Assessing the value of wind generation in future carbon constrained electricity industries

Vithayasrichareon

MacGill

2013

Energy Policy

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“…Originally designed to find the optimal mix of financial assets to keep the risk at a pre-specified level, or to minimize risk for a fixed rate of return, portfolio theory has recently also been applied to energy sector investment in a number of applications (see the review in Bazilian and Roques, 2008). Earlier work includes Bar-Lev and Katz (1976), Awerbuch and Berger (2003), Awerbuch (2006), van Zon and Fuss (2006), Krey and Zweifel (2006), and Roques et al (2008), among others). (Real options theory is another tool adapted from finance -see Box).…”

Section: Portfolio Theorymentioning

confidence: 99%

Technology Portfolios: Modelling Technological Uncertainty and Innovation Risks

Grübler¹,

Fuss²,

McCollum³

et al. 2013

Energy Technology Innovation

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AUTHORS' SUMMARYThe case study reviews a range of methodologies and model-based applications that can assist policy decisions under irreducible technology innovation uncertainties. One approach uses scenario analysis. A scenario study that explored a range of salient future uncertainties identified energy efficiency and conservation as the single most important and also most robust technology option for climate mitigation. Another approach uses portfolio theory to quantify the benefits from diversified technology portfolios in the framework of risk-averse decision-making. Such studies suggest risk aversion leads to higher adoption rates of currently higher-cost energy technology options such as modern biomass, renewables, and also carbon capture and storage (CCS). Diversification not only reduces the mean of risk exposure but also drastically lowers the tails of extreme undesirable outcomes. Portfolio theory and scenario analysis can also be combined, as illustrated by a study of three different energy system objectives (energy security, air pollution, climate change) within a multi-criteria optimization framework. Scenarios in which objectives were met individually, and then all together, under a range of uncertainties again demonstrated the benefits of portfolio diversification with greater emphasis on energy efficiency and "general purpose" energy conversion technologies. These examples discussed in this case study show that formal tools and approaches, including scenario analysis, portfolio theory, and multi-criteria optimization, are increasingly available to move technology portfolio decisions onto a more rational ground.

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“…More recent research [4,5,6] extended the analysis to various power expansion mixes. Focus on mean-variance portfolios has been shown in some applications testing different risk measures [7,8]. Mean-variance frameworks have been proposed to address the energy portfolio planning and the optimal allocation of positions in peak and off-peak forward price contracts [9].…”

Section: Power Portfolios Electricity Pricing Impact and Risk Analysismentioning

confidence: 99%

An impact assessment of electricity and emission allowances pricing in optimised expansion planning of power sector portfolios

Tolis

Rentizelas

2011

Applied Energy

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This version is available at https://strathprints.strath.ac.uk/44919/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. AbstractThe present work concerns a systematic investigation of power sector portfolios through discrete scenarios of electricity and CO2 allowance prices. The analysis is performed for different prices, from regulated to completely deregulated markets, thus representing different electricity market policies. The modelling approach is based on a stochastic programming algorithm without recourse, used for the optimisation of power sector economics under multiple uncertainties. A sequential quadratic programming routine is applied for the entire investigation period whilst the time-dependent objective function is subject to various social and production constraints, usually confronted in power sectors. The analysis indicated the optimal capacity additions that should be annually ordered from each competitive technology in order to substantially improve both the economy and the sustainability of the system. It is confirmed that higher electricity prices lead to higher financial yields of power production, irrespective of the CO2 allowance price level.Moreover, by following the proposed licensing planning, a medium-term reduction of CO2 emissions per MWh by 30% might be possible. Interestingly, the combination of electricity prices subsidisation with high CO2 allowance prices may provide favourable conditions for investors willing to engage on renewable energy markets.

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Fuel mix diversification incentives in liberalized electricity markets: A Mean–Variance Portfolio theory approach

Cited by 260 publications

References 17 publications

Assessing the value of wind generation in future carbon constrained electricity industries

Assessing the value of wind generation in future carbon constrained electricity industries

Technology Portfolios: Modelling Technological Uncertainty and Innovation Risks

An impact assessment of electricity and emission allowances pricing in optimised expansion planning of power sector portfolios

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