Uncertainty in electricity markets from a semi-nonparametric approach

Trespalacios, Alfredo; Cortés, Lina M.; Perote, Javier

doi:10.1016/j.enpol.2019.111091

Cited by 16 publications

(15 citation statements)

References 43 publications

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“…In fact, "fat tails", i.e., a higher probability of extreme values, may be more relevant. The same goes for leptokurtic, platykurtic and similar distributions [20].…”

Section: Risk Assessmentmentioning

confidence: 64%

A Conceptual Framework to Classify and Manage Risk, Uncertainty and Ambiguity: An Application to Energy Policy

Gaudard

Romerio

2020

Energies

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Global issues are such that we should assess and manage a variety of risks and uncertainties. Due to increasing world complexity, the development of an adequate and innovative conceptual framework, anchored in the literature, is required. This article contributes to this effort with an approach particularly relevant to decision-makers dealing with threats of different natures, limited heterogeneous information, and experts' assessments tainted by doubts. Our approach is based on two pillars: 1) An "acuity scale", based on the probability of the occurrence of an event, its impact and the experts' degree of doubt; 2) A taxonomy focused on the concepts of risk, uncertainty, gamble and butterfly ambiguity. Accordingly, we present in a second step the major management implications of such approach. Global policy trends (e.g., sustainability transition) put energy sector decision-makers at the forefront of risk and uncertainty management. Consequently, we carry out a case study focused on Swiss energy policy since the 1980s, including its inception, the turnaround provoked by the Fukushima accident, and the government's 2050 energy strategy. Our investigation shows that the proposed conceptual framework allows for the development of an original analysis of the main drivers that influence governmental policies and stakeholder strategies.

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“…In fact, "fat tails", i.e., a higher probability of extreme values, may be more relevant. The same goes for leptokurtic, platykurtic and similar distributions [20].…”

Section: Risk Assessmentmentioning

confidence: 64%

A Conceptual Framework to Classify and Manage Risk, Uncertainty and Ambiguity: An Application to Energy Policy

Gaudard

Romerio

2020

Energies

View full text Add to dashboard Cite

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“…Furthermore, if d j3 > 0 (d j3 < 0), then the j-th marginal pdf features positive (negative) skewness, when d j4 > 0 the marginal pdf of z jt exhibits leptokurtosis, and the higher-order even parameters account for extreme values. A further discussion on the interpretation of such parameters can be found in the studies by [18,21,23] for the case of electricity markets.…”

Section: Spot Price and Energy Generationmentioning

confidence: 99%

“…Nevertheless, ref. [18] indicates that some variables in electricity markets exhibit conditions of skewness and kurtosis and higher-order moments that are not adequately represented only using normal distributions. These authors show that semi-nonparametric (SNP) distributions allow a better fit for hydrologic inflows, spot price, and even demand for electricity data.…”

Section: Introductionmentioning

confidence: 99%

Modeling Electricity Price and Quantity Uncertainty: An Application for Hedging with Forward Contracts

2021

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Energy transactions in liberalized markets are subject to price and quantity uncertainty. This paper considers the spot price and energy generation to follow a bivariate semi-nonparametric distribution defined in terms of the Gram–Charlier expansion. This distribution allows us to jointly model not only mean, variance, and correlation but also skewness, kurtosis, and higher-order moments. Based on this model, we propose a static hedging strategy for electricity generators that participate in a competitive market where hedging is carried out through forward contracts that include a risk premium in their valuation. For this purpose, we use Monte Carlo simulation and consider information from the Colombian electricity market as the case study. The results show that the volume of energy to be sold under long-term contracts depends on each electricity generator and the risk assessment made by the market in the forward risk premium. The conditions of skewness, kurtosis, and correlation, as well as the type of the employed risk indicator, affect the hedging strategy that each electricity generator should implement. A positive correlation between the spot price and energy production tends to increase the hedge ratio; meanwhile, negative correlation tends to reduce it. The increase of forward risk premium, on the other hand, reduces the hedge ratio.

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“…In this regard, Trespalacios, Cortés, and Perote (2020) indicate that some variables in electricity markets exhibit conditions of skewness and kurtosis and higher-order moments that are not adequately represented only by means of normal distributions; they demonstrate that Semi-NonParametric (SNP) distributions allow a better fit to hydrologic inflows, spot price, and even demand for electricity data. Consequently, these authors recommend to manage risks in this type of market from a flexible SNP approach, where normality is a particular case, and to not only consider normal distributions to describe uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Modeling Electricity Price and Quantity Uncertainty: An Application for Hedging With Forward Contracts

2020

Self Cite

View full text Add to dashboard Cite

Energy purchases/sales in liberalized markets are subject to price and quantity uncertainty, which should be jointly modeled by relaxing the unreliable normality assumption for capturing risk. In this paper, we consider the spot price and energy generation to follow a bivariate semi-nonparametric distribution defined in terms of the Gram-Charlier expansion. This distribution allows to jointly model not only mean, variance, and correlation, but also skewness, kurtosis, and higher-order moments. Based on this model, we propose a static hedging strategy for electricity generators that participate in a competitive market where hedging is carried out through forward contracts that include a risk premium in their valuation. For this purpose, we use Monte Carlo simulation and consider information from the Colombian electricity market as the case study. The results show that the volume of energy to be sold under long-term contracts depends on each electricity generator and the risk assessment made by the market in the Forward Risk Premium. The conditions of skewness, kurtosis, and correlation, as well as the type of risk indicator to be employed, affect the hedging strategy that each electricity generator should implement.

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Uncertainty in electricity markets from a semi-nonparametric approach

Cited by 16 publications

References 43 publications

A Conceptual Framework to Classify and Manage Risk, Uncertainty and Ambiguity: An Application to Energy Policy

A Conceptual Framework to Classify and Manage Risk, Uncertainty and Ambiguity: An Application to Energy Policy

Modeling Electricity Price and Quantity Uncertainty: An Application for Hedging with Forward Contracts

Modeling Electricity Price and Quantity Uncertainty: An Application for Hedging With Forward Contracts

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