Mitigating Financial Exposure in Power Purchase Agreements: A Unified Decision Framework

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🔬研究者:A decision-theoretic model for PPA structure selection under uncertainty, with general conditions for optimality.

🏢実務担当者:Actionable insights on choosing PPA types and volume structures to minimize financial exposure based on risk preferences.

Power Purchase Agreements (PPAs) play an important role in financing renewable energy projects. However, the complex volumetric uncertainty and market conditions make it difficult for users to determine the optimal PPA structures, underscoring the need for a unified modeling approach. This paper presents a quantitative decision-support framework for both the seller and the buyer to collectively select optimal PPA structures, including the PPA type (virtual or physical), the volume structure (Fixed-Volume, As-Generated, or As-Consumed), and the contract price. In this framework, we model the financial risk for both the seller and the buyer as the difference in outcomes with and without the PPA, taking into account uncertainties in demand and generation. We consider risk-neutral and risk-averse scenarios to identify the optimal structure that minimizes the shared risk between the buyer and the seller. The theoretical analysis establishes conditions for optimal PPA structures under general probability distributions of generation and demand. In the numerical studies, we show that the As-Consumed structure is preferred in supply deficit or unprofitable surplus scenarios, as it minimizes the buyer’s exposure to costly market balancing. In contrast, Fixed-Volume becomes the superior structure for capitalizing on profitable generation surpluses, though this comes with higher seller-side tail risk. The As-Generated structure is shown to be a niche, high-risk option, viable for risk-seeking parties or, counter-intuitively, as a specific hedge for highly risk-averse sellers against catastrophic generation shortfalls. Finally, we validate the framework through a real-world case study using 8,760 hours of historical PJM data, confirming the robustness of these decision rules under real-world volatility of generations, demand, and prices.

• semanticscholar https://doi.org/10.1145/3744255.3798122first seen 2026-07-24 06:48:39