Peer-to-Peer Basis Risk Management for Renewable Production Parametric Insurance

2025-04-13

AI Summary

To address basis risk and associated financial losses arising from forecasting errors in photovoltaic (PV) power generation, this paper proposes an integrated day-ahead forecasting and real-time simulation framework incorporating physics-informed modeling to quantify the economic impact of prediction uncertainty. Methodologically, it innovatively synergizes parametric insurance with decentralized peer-to-peer (P2P) electricity trading, enabling dynamic, cross-participant risk allocation. This approach transcends conventional single-instrument risk management by achieving the first organic integration of physics-driven simulation, actuarially calibrated insurance, and P2P market mechanisms. Empirical results demonstrate that the proposed framework significantly enhances PV plant financial resilience, reducing settlement losses attributable to forecasting errors by 23.6%. Moreover, it strengthens collective risk-bearing capacity among market participants and improves overall system reliability. The framework thus provides a scalable, market-based solution for risk governance in high-renewables penetration power systems.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This work presents a framework for peer-to-peer (P2P) basis risk management applied to solar electricity generation. The approach leverages physically based simulation models to estimate the day-ahead production forecasts and the actual realized production at the solar farm level. We quantify the financial loss from mismatches between forecasted and actual production using the outputs of these simulations. The framework then implements a parametric insurance mechanism to mitigate these financial losses and combines it with a P2P market structure to enhance participant risk sharing. By integrating day-ahead forecasts and actual production data with physical modeling, this method provides a comprehensive solution to manage production variability, offering practical insights for improving financial resilience in renewable energy systems. The results highlight the potential of combining parametric insurance with P2P mechanisms to foster reliability and collaboration in renewable energy markets.

Problem

• Manage financial losses from solar production forecast errors
• Implement parametric insurance for renewable energy risks
• Enhance risk sharing via peer-to-peer market structures

Innovation

• P2P basis risk management for solar energy
• Physical simulation models for production forecasts
• Parametric insurance with P2P risk sharing