Renewable Asset Valuation & Hedging Strategy (France 2023)

Context & Objective

I built this project to simulate the financial reality of renewable energy producers in France. Using 2023 real-world data, my goal was to answer a critical question: How to balance profitability and risk when managing a portfolio of Wind and Solar assets?

This repository contains the full workflow: from raw data extraction (ETL) to the backtesting of hedging strategies (Merchant vs. PPA).

Methodology

I structured the analysis following a logical quantitative workflow:

1. Data Engineering & Preparation

• Loading and cleaning raw data from RTE (handling missing values and zero-production artifacts).
• Merging Grid data with EPEX SPOT market prices to create a unified financial dataset.

2. Grid & Mix Analysis

• Thermosensitivity: Visualizing the impact of seasonality and temperature on French consumption.
• Generation Mix: Analyzing the daily contribution of Nuclear, Hydro, and Renewables.

3. Stress Testing (Peak Load)

• Deep dive into the Annual Peak Load event (~80GW).
• Analysis of the Residual Load to understand why prices react (or don't) during high demand.

4. Market Fundamentals

• Correlation Analysis: Studying the relationship between Demand and Spot Prices (Merit Order Effect).

5. Financial Valuation

• Capture Prices: Calculating the real market value of Solar and Wind MWh vs Baseload.
• Cannibalization: Quantifying the discount suffered by Solar assets during midday peaks.

6. Portfolio Strategy & Risk Management

• Comparison of Solar vs Wind performance.
• Simulation of 3 hedging strategies for a 10 MW asset:
  • Merchant (100% Spot)
  • PPA (100% Fixed)
  • Optimized Mix (70% PPA / 30% Spot)

Key Findings (2023 Backtest)

1. Wind vs. Solar Performance

Wind assets significantly outperformed Solar in 2023.

• Reason: Wind generation is positively correlated with high winter prices (demand peaks), whereas Solar produces during lower-priced hours.

2. The Cost of Risk (Volatility)

I measured the monthly revenue standard deviation to quantify risk.

• Merchant Strategy: Extremely volatile (Standard Deviation > €74k/month for Wind).
• PPA Strategy: Switching to a PPA reduced financial volatility by 32%.

3. Final Recommendation

Based on the Sharpe Ratio logic (Risk/Reward), the Mixed Strategy (70% PPA / 30% Spot) is optimal.

• It secures debt coverage via the PPA.
• It captures market tension upside (scarcity pricing) during cold snaps via the 30% spot exposure.

Tech Stack

• Language: Python
• Libraries: Pandas (Time-series manipulation), Matplotlib (Visualization), NumPy (Vectorized calc).
• Data Sources: RTE (Open Data) & EPEX SPOT.

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Author: Yanis ALLAS
Connect with me on LinkedIn: linkedin.com/in/yanis-allas-5600b4294