Overview

This repository contains an advanced Power Purchase Agreement (PPA) modeling and optimization framework utilizing PyPSA for energy system analysis. The model incorporates renewable energy generation, grid electricity, and battery storage to determine optimal energy procurement strategies.

https://github.com/PLANiT-Institute/pyPPA

Features

• Multi-Year PPA Analysis: Evaluate long-term renewable energy procurement strategies.
• Renewable Energy Cost Estimation: Calculate LCOE for PV, agrivoltaics, and offshore wind.
• KEPCO Grid Pricing Integration: Incorporates official KEPCO tariffs and projections.
• Battery Storage Modeling: Assess the financial and operational impact of battery integration.
• Carbon and REC Pricing: Includes customizable carbon pricing scenarios (e.g., NGFS) and REC costs.
• Scenario-based Analysis: Supports multiple scenario runs with configurable parameters.
• Streamlit Interface: Web-based UI for configuring and running PPA simulations.

Installation

To use this repository, clone it and install the required dependencies:

# Install the custom PLANiT_PPA package
pip install git+https://github.com/planit-institute/pyPPA.git@pip

# Download the necessary configuration files
download yaml and main.py from the repository and place them in the appropriate directory.

Usage

Running the Model

You can run the PPA model using the Streamlit interface:

streamlit run main.py

Alternatively, execute a single scenario from the command line:

python main.py --scenario my_scenario

Configuration

The model configuration is managed through an Excel file (scenario_defaults.xlsx), which defines key parameters such as:

• Load profiles
• Grid share constraints
• Carbon and REC price scenarios
• Battery inclusion and specifications

Key Components

ppamodule.py

• PPAModel: Main class that runs PPA optimization.
• Grid & REC Calculation: Processes grid data, REC values, and KEPCO pricing.
• Cost Analysis: Estimates CAPEX, OPEX, and LCOE.
• PyPSA Network Simulation: Builds a network for energy dispatch and optimization.
• Output Processing: Generates key insights on energy mix, emissions, and cost breakdowns.

main.py

• Streamlit-based UI for configuring and running simulations.
• Loads default parameters from scenario_defaults.xlsx.
• Supports running single or multiple scenarios.

Example Output

Upon successful execution, results are saved as an Excel file containing:

• LCOE Calculations: Cost analysis for different energy sources.
• Generation Mix: Share of grid, PV, wind, and agrivoltaics.
• Marginal Cost Analysis: Energy cost breakdown.
• Carbon and REC Analysis: Emissions and compliance costs.
• Battery Impact Assessment: Storage requirements and cost evaluation.