Wind Plant Economic Analysis under Uncertainty

📌 Overview

This project provides a comprehensive probabilistic framework for the economic analysis of wind power plants.
Unlike traditional deterministic models, we use Monte Carlo Simulation (MCS) with correlated uncertainties to evaluate production, revenues, and financial indicators (NPV, IRR, LCOE, VaR/CVaR, Omega-LCOE).

The architecture integrates:

• Wind Resource Modeling: Monthly Weibull parameters, vertical extrapolation, air density adjustment.
• Power Curve Modeling: Manufacturer curves + SCADA calibration to align with real-world turbine behavior.
• Electricity Price Modeling: Historical datasets + block-bootstrap resampling to capture seasonal/market risks.
• Economic Model: CAPEX, OPEX, contracts (PPA/market/export), financing, taxes, and inflation scenarios.
• Risk Metrics: Probabilistic KPIs with emphasis on risk-adjusted returns.

This framework enables robust investment decisions under uncertainty in wind energy projects.

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🏗️ Project Structure

Code.zip/                       # Source code (Python)
│── main.py                     # Main entry point (runs full pipeline)
│── production_model.py         # Wind production model (Modes A/B/C)
│── price_model.py              # Electricity price simulation
│── economics.py                # Cashflow + financial KPIs
│── reporting.py                # Report generation (HTML/CSV/plots)
│── config.yaml                 # Central configuration file
│
├── data/                       # Datasets
│   ├── weibull_monthly.csv     # Monthly Weibull parameters (per city)
│   ├── wind_turbine_scada/     # SCADA dataset for calibration
│   ├── wind_power_forecasting/ # Time-series forecasting dataset
│   ├── us_electricity_prices/  # U.S. electricity prices dataset
│
├── outputs/                    # Simulation results
│   ├── mvp_report.html         # Main Monte Carlo simulation report
│   ├── production_paths.csv    # Energy production samples
│   ├── price_paths.csv         # Electricity price samples
│   ├── npv_distribution.png    # Example output chart
│
├── LICENSE
|
└── README.md                   # (this file)

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🔑 Key Features

• Three Production Modes (A/B/C):
  • Mode A: Weibull-only (statistical baseline)
  • Mode B: SCADA-calibrated (realistic turbine behavior)
  • Mode C: Hybrid (adds diurnal/short-term profiles)
• Monte Carlo Simulation with thousands of scenarios (default N=2000–10000).
• Config-driven architecture (config.yaml) ensures reproducibility and transparency.
• Modular design: Replace/improve any module (production, price, economics) independently.
• Outputs for investors: P50/P90 production, NPV distribution, IRR distribution, VaR/CVaR risk metrics.

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📊 Methodology

1. Wind Modeling

   • Weibull parameters (k, c) estimated per month per city.
   • Adjusted for hub height and air density.
   • Integrated with turbine power curves.

2. Production Modeling

   • Converts wind distributions to energy using turbine curves.
   • Optional SCADA calibration (power scale, v-shift, TI, availability).
   • Supports diurnal/seasonal profiles.

3. Price Modeling

   • U.S. electricity price dataset (monthly).
   • Converted to USD/MWh.
   • Monte Carlo via block-bootstrap (12-month blocks) to preserve seasonality.

4. Economic Modeling

   • Cashflows from CAPEX, OPEX, revenues.
   • Discounted at configurable project WACC.
   • KPIs: NPV, IRR, LCOE, Payback, VaR/CVaR, Omega-LCOE.

5. Reporting

   • Outputs consolidated in mvp_report.html (tables + charts).
   • Fan charts for revenue/cashflows.
   • Histograms for NPV and IRR.

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⚙️ Installation & Usage

1. Clone Repository

git clone https://github.com/<your-username>/WindPowerPlantAnalysis.git
cd WindPowerPlantAnalysis

2. Create Virtual Environment

python -m venv .venv
source .venv/bin/activate   # Linux/Mac
.venv\Scripts\activate    # Windows

3. Install Dependencies

pip install -r requirements.txt

4. Run Base Simulation

python main.py --config config.yaml

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📂 Datasets

• weibull_monthly.csv → Monthly wind speed Weibull parameters (10 cities in Iran).
• SCADA Dataset (Kaggle) → 2018 10-min SCADA data for calibration.
• Wind Power Forecasting Dataset (Kaggle) → 2.5 years, 10-min time series for turbine behavior.
• U.S. Electricity Prices (Kaggle) → Monthly prices from 2001–2024.

All datasets are publicly available and referenced in the project documentation.

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📑 References

• Windpowerlib Documentation
• Kaggle – Wind Turbine SCADA Dataset
• Kaggle – Wind Power Forecasting
• Kaggle – U.S. Electricity Prices
• IRENA Reports

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👥 Authors

• فرزاد نورسته
• علی خسروی

Advisor: دکتر حبیب رجبی مشهدی
Department of Electrical Engineering (Power Systems), Ferdowsi University of Mashhad (FUM)

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📜 License

This project is released under the CC BY-NC 4.0 License (Non-Commercial).

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⭐ Acknowledgements

This repository is part of our undergraduate thesis project:
"تحلیل ریسک و بررسی امکان‌سنجی پروژه‌های صنعتی با تأکید بر پروژه‌های تولید انرژی تجدیدپذیر".