Abstract
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.
Highlights
Electricity is one of the most efficient ways to transform, transport, and use energy
The results in this study show that the optimal level of electricity forward sales is subject to different factors: the conditions inherent to the uncertainty over each generator’s energy generation and their correlation with the spot price, the risk assessment performed by the market − reflected in the Forward Risk Premium (FRP) value; and the type of risk indicator that is expected to be managed
We proposed 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
Summary
Electricity is one of the most efficient ways to transform, transport, and use energy. The electricity market is characterized by being highly volatile when compared to other commodity markets This high volatility in terms of price and quantity is due to market conditions (e.g., expectations or strategies of each company and economic dynamics of a region) and physical conditions (e.g., climate, water availability, fuel production or transportation capacity, and even damage to the power transmission network) (Mosquera-López, Uribe, & ManotasDuque, 2017). To face this situation, electricity generators − companies that make large investments with long capital recovery periods − must implement effective market risk management strategies to ensure compliance with their business objectives
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