Structured renewable energy derivatives

The economic viability of renewable plants increasingly hinges on the ability to hedge simultaneous volume and price risk. This paper develops a framework that forecasts climatic variables and electricity prices. The framework transforms these forecasts into energy production and income scenarios and then prices structured derivatives under a risk-neutral procedure anchored to market forwards. Observations at a given location are modeled using both classical models and advanced machine learning techniques. Rolling backtests show that neural networks are among the top-performing models across the underlying targets and generate scenario distributions with improved downside-risk profiles relative to classical baselines; a scenario-implied minimum-variance hedge based on the average-price forward reduces the variance of the hedged profit and loss by about 70%; and the relative contribution of price-versus-volume sensitivities varies with the horizon, highlighting how dominant risk drivers shift with temporal aggregation. These results provide a bridge from data-driven forecasting to pricing and risk mitigation for hybrid wind–solar plants. This study introduces three original features: a joint forecasting and evaluation framework applied to multiple models over the same drivers, enabling like-for-like model risk comparisons; a forward-anchored scenario engine that propagates forecast uncertainty into joint Monte Carlo paths suitable for risk-neutral pricing and bivariate Greeks; and an empirical hedging module, providing an operational and risk-focused measure of hedge effectiveness.