PVT Solar Panel Investment Analyzer

Master's Thesis: Data-driven evaluation of PVT systems in Sweden

Python • Pandas • NumPy • SciPy • Climate Data Analysis
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Project Overview

Comprehensive financial modeling system for evaluating PVT solar panel investments across Swedish climate zones, incorporating thermal energy valuation, regional electricity price variations, and future climate change scenarios (RCP4.5/8.5).

Technical Implementation

The system uses Python for comprehensive data analysis, leveraging pandas for data manipulation, NumPy for numerical computations, and SciPy for financial optimization calculations. The model integrates climate data analysis with economic modeling to provide robust investment evaluations under various scenarios.

PythonPandasNumPySciPyMatplotlibClimate Data Analysis

Key Findings

Payback Period

15+ years across all Swedish regions

Internal Rate of Return

8.8% (Luleå) to negative IRR (southern regions)

Net Present Value

Negative across scenarios with current cost assumptions

Thermal Contribution

~47% of total energy value in northern regions

Key Features

  • Multi-region comparison (Luleå, Sundsvall, Stockholm, Malmö)
  • Climate scenario modeling (RCP4.5 vs RCP8.5)
  • Thermal energy valuation with regional heating demand
  • 30-year DCF analysis with degradation modeling
  • Swedish tax incentive integration (ROT deduction, Green tax)
  • Automated cash flow generation and CSV export
  • Successfully scaled 5-panel to 10-panel systems

Methodology

Energy Generation

Climate model predictions with temperature coefficient effects on PV efficiency

Regional Analysis

Electricity price integration across Swedish zones (SE1-SE4)

Thermal Valuation

Thermal displacement value calculations based on regional heating demand

Financial Modeling

IRR and NPV optimization using scipy with 30-year cash flow analysis

Technical Implementation

The system uses Python for comprehensive data analysis, leveraging pandas for data manipulation, NumPy for numerical computations, and SciPy for financial optimization calculations. The model integrates climate data analysis with economic modeling to provide robust investment evaluations under various scenarios.

Results & Impact

The research demonstrates that while PVT systems show technical feasibility across Sweden, current investment returns are challenged by high upfront costs. Northern regions show better performance due to higher thermal energy contribution, but even optimal scenarios require extended payback periods. The work provides valuable insights for policy makers and investors considering PVT technology adoption in Nordic climates.

Research Paper

The complete thesis document provides detailed methodology, results, and conclusions.

📄 Download Thesis Paper (PDF)