PV Battery Sizing Optimizer & Visualizer

This application helps users calculate the optimal combination of Photovoltaic (PV) system size (number of panels) and external battery capacity to maximize financial savings over a specified period. It utilizes the differential_evolution algorithm from scipy.optimize to find the best configuration within a user-defined budget.

Features

• Optimization: Automatically finds the optimal number of PV panels and battery size (kWh).
• Financial Analysis: Calculates investment costs, potential savings, and payback period.
• Customizable Inputs: Allows configuration of budget, energy prices (fixed, variable, or percentage increase), panel efficiency, battery costs, and more.
• Visualization: Interactive plots showing:
  • Daily consumption and production (rolling averages).
  • Battery charge levels.
  • Energy bought/sold grid interaction.
  • Cumulative savings over time.
• Interactive UI: Built with Streamlit for an easy-to-use web interface.

Installation

1. Clone the repository:

   git clone <repository_url>
   cd PV-Battery-Sizing-Optimizer-Visualizer

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

Run the application using Streamlit:

streamlit run PV_battery_optimizer_EUR.py

Once running, your web browser should open automatically to the application URL (usually http://localhost:8501).

User Interface Guide

1. Upload Data: You can upload your own consumption.csv and irradiance.csv files or use the default sample data provided.
2. Set Parameters: Adjust the slider and input fields for:
   • Budget: Maximum amount you are willing to invest.
   • Projection Years: Number of years to calculate savings for.
   • Energy Prices: Current buy/sell prices and pricing model.
   • System Specs: Panel size, efficiency, battery costs, etc.
3. Run Optimization: The app will process the inputs and display the optimal configuration and financial projections.

Input Data Formats

If you provide your own data, ensure the CSV files follow these formats:

Consumption Data (consumption.csv)

Should contain daily energy consumption data.

• Columns: date, energy
• Units: energy in kWh per day.
• Format:

  date,energy
  2023-01-01,15.5
  2023-01-02,14.2
  ...

Irradiance Data (irradiance.csv)

Should contain daily horizontal irradiation data.

• Columns: date, irradiance
• Units: irradiance in J/cm² per day.
• Format:

  date,irradiance
  2023-01-01,538
  2023-01-02,285
  ...

Project Structure

• PV_battery_optimizer_EUR.py: The main application script containing the Streamlit UI and optimization logic.
• classes.py: Defines the data structures (UserInputs, OptimizationResults, PVSystem, Battery) used across the application.
• requirements.txt: List of Python libraries required to run the project.
• consumption.csv & irradiance.csv: Default sample data files.
• sample_consumption/: Directory containing additional sample consumption data files.

Dependencies

• Streamlit: For the web interface.
• Pandas: For data manipulation.
• NumPy: For numerical operations.
• Matplotlib: For plotting graphs.
• SciPy: For the optimization algorithm (differential_evolution).