Valuation of power purchase agreements for corporate renewable energy procurement

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🔬研究者:Provides a novel methodology combining inverse optimization and machine learning for PPA valuation, advancing energy finance literature.

🏢実務担当者:Offers a data-driven framework for corporate energy buyers to assess PPA value and negotiate contracts.

🏛政策担当者:Highlights the importance of accurate PPA valuation for renewable energy market design and corporate decarbonization incentives.

Corporate renewable power purchase agreements (PPAs) are long-term contracts that enable companies to source renewable energy without having to develop and operate their own capacities. Typically, producers and consumers agree on a fixed per-unit price at which power is purchased. The value of the PPA to the buyer depends on the so called capture price defined as the difference between this fixed price and the market value of the produced volume during the duration of the contract. To model the capture price, practitioners often use either fundamental or statistical approaches to model future market prices, which both have their inherent limitations. We propose a new approach that blends the logic of fundamental electricity market models with statistical learning techniques. In particular, we use regularized inverse optimization in a quadratic fundamental bottom-up model of the power market to estimate the marginal costs of different technologies as a parametric function of exogenous factors. We compare the out-of-sample performance in forecasting the capture price using market data from three European countries and demonstrate that our approach outperforms established statistical learning benchmarks. We then discuss the case of a photovoltaic plant in Spain to illustrate how to use the model to value a PPA from the buyer's perspective.

• openaire https://doi.org/10.1016/j.ejor.2025.05.054first seen 2026-05-05 19:07:57