With an increasing share of renewable energy resources participating in electricity markets, there is a growing dependence between renewable power production and clearing prices of spot markets. Modeling this dependence using bivariate analysis can result in underestimation of market risks and adverse effects for stakeholders' risk management. To enable an undistorted risk assessment, we are using a copula approach to precisely and jointly model electricity prices and infeed volumes of wind power. We simulate the case of wind farm operators using power purchase agreements (PPAs) to shift the price risk to an energy trader, who integrates the infeed into its portfolio. The trader's portfolio can either be geographically dispersed, or highly localized. Based on its portfolio, the energy trader can decide to use derivatives such as futures to manage its risk exposure. The trader decides on future volumes subject to its portfolio's inherent volatility. With a given risk averse strategy, a sufficiently diverse portfolio can help reduce the necessity to trade futures and subsequently the disadvantage of having to pay potential risk premiums.

Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios