A load factor based mean–variance analysis for fuel diversification

Gotham, Douglas J.; Muthuraman, Kumar; Preckel, Paul V.; Rardin, Ronald L.; Ruangpattana, Suriya

doi:10.1016/j.eneco.2008.10.004

Cited by 37 publications

(10 citation statements)

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“…Furthermore since the load is not uniform, such assumptions would fall short in recognising the value of different types of generation technology given the tradeoffs between their fixed and variable costs. The importance of this has been recognised for example in Gotham et al (2009) which decomposes load into various types having different load factors in order to better value their characteristics. Furthermore, estimating the value of intermittent renewable generation technologies, particularly wind, using the levelised cost method is highly problematic because it does not reflect the different values of generation which have greater or lesser intermittency and dispatchability.…”

Section: Monte Carlo Model For Assessing Generation Portfoliosmentioning

confidence: 99%

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

Vithayasrichareon

MacGill

2013

Energy Policy

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Section: Monte Carlo Model For Assessing Generation Portfoliosmentioning

confidence: 99%

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

Vithayasrichareon

MacGill

2013

Energy Policy

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“…We adopt this decision principle also for the cost based investment decision of generation assets in the system portfolio. Following Jansen et al (2006) and Gotham et al (2009), we thus use total system costs instead of "return" and variance of total costs as the relevant risk measure.…”

Section: The Risk-adjusted Portfolio Problemmentioning

confidence: 99%

Mean-Variance Optimization of Power Generation Portfolios Under Uncertainty in the Merit Order

Sunderkötter

2011

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. Terms of use: Documents in AbstractIn this article we discuss welfare-optimal capacity allocation of different electricity generation technologies available for serving system demand. While the classical peak load pricing theory derives the efficient portfolio structure from a deterministic marginal production cost curve ("merit order"), we investigate in particular the implications of possible reversals in the merit ordersometimes also referred to as fuel switch risksinduced by uncertain operating costs.We propose a static, non-convex optimization model combining the classic peak load pricing model with elements of mean-variance portfolio (MVP) theory and analytically discuss possible solution cases and important optimality properties. We examine the approach in a case study on the efficient structure of generation portfolios consisting of CCGT and hard coal technologies in Germany.With special emphasis, we study the emergence of overcapacities (exceeding maximal demand) in efficient portfolios and show that diversification is not beneficial per-se. The results show that the efficient technology mix may be significantly impacted by a risk for reversals in the merit order. Therefore, our findings support the importance of considering this risk factor especially with long-term investment horizons.The model is applicable to various investment problems related to production of nonstorable goods under price uncertainty of input factors. Similar problems can e.g. be found in transportation systems or in the process industry.

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“…By deploying simple costs and the corresponding variance in their proposed framework, e.g. Jansen et al (2006) and Gotham et al (2009) avoid inconsistencies in the risk and return measure without compromising the clarity of the classical MVP theory.…”

Section: Measuring Risk and Returnmentioning

confidence: 99%

“…For conclusions on the optimal generation technology mix for an electricity market as a whole, a solid long-term modeling framework should therefore better be based on the integrated modeling of the long-term market optimum taking into account operating and investment costs instead of unit costs: Based on this modeling principle, Gotham et al (2009) proposed more recently a static cost-based model for optimal capacity allocation in a mean-variance framework with different load segments to be served. In addition, the model presented by Delarue et al (2009) captures ramp-up costs as well as uncertain availability of renewable technologies such as wind.…”

mentioning

confidence: 99%

“…be found in Oren (2000) 9 In a differentiated modeling of the peak-load pricing problem regarding new and historic investments as e.g. done by Gotham et al (2009), historic (sunk) investment costs are neglected. This enables the model to capture the optimal transition from an existing plan fleet the future long-term optimum, which is however not the main purpose of our analysis.…”

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Valuing Fuel Diversification in Optimal Investment Policies for Electricity Generation Portfolios

Sunderkoetter

2009

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. Terms of use: Documents in AbstractOptimal capacity allocation for investments in electricity generation assets can be deterministically derived by comparing technology specific long-term and short-term marginal costs. In an uncertain market environment, Mean-Variance Portfolio (MVP) theory provides a consistent framework to valuate financial risks in power generation portfolios that allows to derive the efficient fuel mix of a system portfolio with different generation technologies from a welfare maximization perspective.Because existing literature on MVP applications in electricity generation markets uses predominantly numerical methods to characterize portfolio risks, this article presents a novel analytical approach combining conceptual elements of peak-load pricing and MVP theory to derive optimal portfolios consisting of an arbitrary number of plant technologies given uncertain fuel prices. For this purpose, we provide a static optimization model which allows to fully capture fuel price risks in a mean variance portfolio framework. The analytically derived optimality conditions contribute to a much better understanding of the optimal investment policy and its risk characteristics compared to existing numerical methods. Furthermore, we demonstrate an application of the proposed framework and results to the German electricity market which has not yet been treated in MVP literature on electricity markets.

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A load factor based mean–variance analysis for fuel diversification

Cited by 37 publications

References 4 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

Mean-Variance Optimization of Power Generation Portfolios Under Uncertainty in the Merit Order

Valuing Fuel Diversification in Optimal Investment Policies for Electricity Generation Portfolios

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