Research

Peer-to-Peer Basis Risk Management for Renewable Production Parametric Insurance (Joint work with Fallou Niakh, Christian-Yann Robert and Michel Denuit)

Published in Annals of Operations Research

Abstract

The financial viability of renewable energy projects is challenged by the variability and unpredictability of production due to weather fluctuations. This paper proposes a novel risk management framework combining parametric insurance and peer-to-peer (P2P) risk sharing to address production uncertainty in solar electricity generation. We first design a weather-based parametric insurance scheme to protect against forecast errors, recalibrated at the site level to mitigate geographical basis risk. To handle residual mismatches between insurance payouts and actual losses, we introduce a complementary P2P mechanism that redistributes the remaining basis risk among participants. The method leverages physically based simulation models to reconstruct day-ahead forecasts and realized productions, integrating climate data and solar farm characteristics. A second-order theoretical approximation links heterogeneous local models to a shared weather index, making risk sharing operationally feasible. In an empirical application to 50 German solar farms, our approach reduces the volatility of production losses by 55\%, demonstrating its potential to stabilize revenues and strengthen the resilience of renewable investments. 

Moving forward blindly: capacity planning, uncertainty and environmental targets (Joint work with David Benatia)

Published in Energy Economics

Abstract

Public energy transition policies often use forward-looking scenarios of energy demand, production and cost to determine the cost of the future electricity mix. These projections can determine the direction of policy support for certain types of energy but often represent a central planner operating in a deterministic environment. This assumption is very strong for long-term (decades) investments and neglects uncertainty and the role of risk aversion. However, recent years have taught us the strength that unexpected extreme events (COVID-19 crisis, war in Ukraine) can have on electricity demand and fuel prices. Moreover, it is difficult to establish precisely in advance the productivity of renewable energies, especially in the presence of more frequent extreme weather events with climate change. We evaluate the deviations of a deterministic optimal renewable energy investment path in the presence of uncertainty on key factors (load, capacity factor and gas price) from an anticipated stochastic trajectory using dynamic programming. We perform a simulation exercise calibrated on the German energy transition as an illustration.

A mean-field game model of electricity market dynamics (joint work with Peter Tankov and Roxana Dumitrescu)

Published in Quantitative Energy Finance

Abstract

We develop a model for the long-term dynamics of electricity market, based on mean-field games of optimal stopping. Our paper extends the recent contribution [Aïd, René, Roxana Dumitrescu, and Peter Tankov, ``The entry and exit game in the electricity markets: A mean-field game approach." Journal of Dynamics \& Games 8.4 (2021): 331] in several ways, making the model much more realistic, especially for describing the medium-term impacts of energy transition on electricity markets. 

In particular, we allow for an arbitrary number of technologies with endogenous fuel prices and enable the agents to both invest and divest. This makes it possible to describe the role of gas generation as a medium-term substitute for coal, to be replaced by renewable generation in the long term, and enables us to model the events like the 2022 energy price crisis. 

Extreme Weather Risks to Renewable Power Systems: Taxonomy of Structural Impacts and Vulnerabilities (Joint work with Anne Barros)

Submitted to ESREL conference proceedings

Abstract

The escalation of climate change-driven extreme weather, combined with the energy transition toward Variable Renewable Energy (VRE), creates complex failure modes in power systems. This paper proposes a formalized vulnerability taxonomy to address these evolving failure modes, distinguishing three types: Type I (Structural Faults), Type II (Dynamic Instability driven by low inertia), and Type III (Thermal Derating and cumulative aging). A review of modeling frameworks, ranging from statistical fragility curves to high-fidelity electro-thermal simulations, is conducted for each type. The analysis highlights some limitations of static availability models and advocates for a multi-physics approach to accurately characterize vulnerabilities facing the emerging climatic stress and grid modernization.

Stochastic Sequential Restoration Planning with Risk Awareness and Ferroresonance Avoidance (Joint work with Xianyi Yang, Jakob Puchinger, Adam Abdin and Anne Barros)

Submitted to ESREL conference proceedings

Abstract

Large-scale blackouts caused by extreme weather events pose critical challenges to modern power systems, disrupting essential services and causing severe economic and social losses. Conventional restoration strategies typically rely on predesigned backbone networks that assume intact transmission infrastructure and stable operating conditions. However, storms and other extreme events frequently induce extensive physical damages to power system components, invalidating these assumptions and highlighting the need for more adaptive restoration approaches.This study develops a stochastic sequential optimization framework to model and enhance post-storm power system restoration under uncertainty of failure probability. By integrating weather data, the failure probabilities of transmission lines are estimated and incorporated into a markov decision process (MDP) formulation. We consider uncertainty in line failure probabilities but assume that operators can update their estimation based on the latest available information as restoration progresses, which aligns with real-time situational awareness practices. The power grid is represented as a graph, where operators sequentially energize components to reestablish connectivity between substations. The model enforces critical operational and safety constraints, such as generation capacity limits, power flow constraints, and ferroresonance avoidance rules, often neglected in existing studies, to ensure both efficient and secure restoration. The proposed model enhances grid resilience by enabling adaptive, data-informed restoration planning that dynamically responds to evolving system conditions. To the best of our knowledge, this is the first study to incorporate uncertain failure probability and ferroresonance avoidance explicitly into power system restoration modeling. Future extensions may include large-scale simulation studies for national grids resilience assessment

Conferences speaker

ISEFI 2025 (Paris): ''Peer-to-peer risk basis management for renewable production parametric insurance"

IAEE 2025 (Paris): "Peer-to-peer risk basis management for renewable production parametric insurance"

Bachelier Seminar 2024 (Paris) : "A mean-field game model of electricity market dynamics"

ISEFI 2023 (Paris): "Moving forward blindly: capacity planning, uncertainty and environmental targets"

YEEES 31 (Vienna): "A mean-field game model of electricity market dynamics"

CEEM Ph.D. Conference on Electricity Markets (2023): "Moving forward blindly: capacity planning, uncertainty and environmental targets"

IAEE 2022 (Tokyo):  "Optimal investment strategy in renewable power under uncertainty"

YEEES 29 (Ghent): "Optimal investment strategy in renewable power under uncertainty"

External Seminars

Future Cities Lab (January 2026): "Peer-to-peer risk basis management for renewable production parametric insurance"

HEC Montréal (2023), FiME Summer school (2023) and CEEPR, MIT (Boston, 2023) on "Moving forward blindly: capacity planning, uncertainty and environmental targets"

Award

Lights on Women Scholarship recipient (4th edition, 2022)

AF2I  2023 scolarship for the subject: Investissements, macroéconomie et énergies