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Project Analysis Report

Generated at 2025-02-07 12:08:17

Architectural Overview​

Could not generate architectural overview

Directory Structure​

├── .
│   ├── assemblies/
│   │   ├── __init__.py
│   │   ├── energy_block.py
│   │   └── power_conversion_unit.py
│   ├── components/
│   │   ├── requirements/
│   │   │   └── load_profile.py
│   │   ├── __init__.py
│   │   ├── battery_container.py
│   │   ├── component.py
│   │   ├── mini_soh.py
│   │   ├── pcs_unit.py
│   │   ├── switch_gear.py
│   │   └── transformer.py
│   ├── demo/
│   │   ├── bp_templates/
│   │   │   ├── blueprint_library.json
│   │   │   ├── blueprint_skeletons.json
│   │   │   ├── bp_dictionary.json
│   │   │   ├── ess_component_models.json
│   │   │   └── example.json
│   │   └── clean_and_populate_workspace.py
│   ├── json_templates/
│   │   ├── efficiency.json
│   │   ├── efficiency_lookup_generation.py
│   │   ├── ess_excel.json
│   │   ├── ess_excel_output.json
│   │   └── ess_mvp.json
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── demo_widget.py
│   │   ├── ess_factory.py
│   │   ├── miscs.py
│   │   ├── output_generation.py
│   │   └── si_prefixes_conversion.py
│   ├── vizualisation/
│   │   └── styles.py
│   ├── altergo-settings.json
│   ├── dev-parameters.json
│   ├── main.py
│   ├── open_ai_project_analyzer.py
│   ├── result.json
│   ├── site_config.json
│   ├── summary.json
│   └── yoy_ess_metrics_plotting.py

Detailed File Analysis​

altergo-settings.json​

dev-parameters.json​

AI Analysis​

Documentation for dev-parameters.json​

📄 File Overview​

The dev-parameters.json file contains configuration parameters used for development purposes. These parameters are essential for connecting to external APIs and services, as well as identifying specific resources.

🔑 Key Parameters​

KeyValueDescription
altergoUserApiKey"464423a8e44746a0af8c5c7d615cbe0b"API key used for authenticating requests to the Altergo User API.
altergoFactoryApi"https://demo.altergo.io"Base URL for the Altergo Factory API.
altergoIotApi"https://iot.demo.altergo.io"Base URL for the Altergo IoT API.
assetId"678d51f467849c3fe2938fb2"Unique identifier for a specific asset in the system.

🚀 Usage​

This file is typically used in development environments to configure API endpoints and authentication keys. It ensures that the application can interact with the Altergo platform seamlessly.

⚠️ Security Note​

  • API Key (altergoUserApiKey): This is a sensitive piece of information. Ensure it is not exposed in production environments or version control systems.
  • URLs: The provided URLs are for a demo environment. Replace them with production URLs when deploying to a live environment.

🎨 Icons Legend​

  • 📄: File or document
  • 🔑: Key or sensitive information
  • 🚀: Usage or deployment
  • ⚠️: Warning or security note
  • 🎨: Legend or key

This documentation provides a clear understanding of the dev-parameters.json file and its purpose in the development workflow.

main.py​

open_ai_project_analyzer.py​

result.json​

site_config.json​

summary.json​

yoy_ess_metrics_plotting.py​

AI Analysis​

📊 ESS Capacity Variation Year-over-Year (YoY) Analysis

This Python script performs a year-over-year (YoY) analysis of the Effective Capacity, Effective Discharge Power, and Round-trip Efficiency of an Energy Storage System (ESS). It visualizes the results using Plotly and saves the output as an HTML file. The script also integrates with the Altergo SDK for dataset management.

🛠️ Key Components​

1. Imports​

  • Plotly: Used for creating interactive visualizations.
  • Pandas: Handles data manipulation and analysis.
  • Altergo SDK: Manages ESS configurations and dataset creation.
  • Custom Utilities:
    • extract_altergo_parameters: Extracts ESS configuration parameters.
    • build_ess: Constructs the ESS model based on provided blueprints.
    • styles: Contains predefined styles for Plotly visualizations.

2. ESS Configuration and Initialization​

  • altergoArguments: Extracts ESS configuration parameters.
  • blueprints: Contains available ESS models.
  • build_instruction: Defines site-specific ESS configurations.
  • ess_site: Initializes the ESS model using the build_ess function.

3. Aging Simulation​

  • The script simulates the aging of ESS components over 15 years.
  • For each year, it calculates:
    • Effective Capacity (in MWh)
    • Effective Discharge Power (in MW)
    • Round-trip Efficiency (as a percentage)
  • Results are stored in a Pandas DataFrame for further analysis.

4. Data Analysis​

  • Variations: Calculates year-over-year variations for:
    • Effective Capacity
    • Effective Discharge Power
    • Round-trip Efficiency
  • Cumulative Variations: Computes cumulative changes over time.

5. Visualization​

  • Bar Chart: Shows year-over-year variations in Effective Capacity.
  • Line Chart: Displays the trend of Effective Capacity over time.
  • Dual Y-Axes: Combines bar and line charts with separate y-axes for variations and absolute values.
  • Styling: Uses predefined styles from styles for consistent and professional-looking charts.

6. Output​

  • HTML File: Saves the visualization as an interactive HTML file (ess_capacity_variation_yoy.html).
  • Dataset Creation: Creates a dataset in the Altergo SDK for further analysis or sharing.

📈 Key Metrics​

  • Effective Capacity: Total usable energy storage capacity (in MWh).
  • Effective Discharge Power: Maximum power output (in MW).
  • Round-trip Efficiency: Efficiency of energy storage and retrieval (as a percentage).

🎨 Visualization Features​

  • Color Coding:
    • Red Bars: Represent year-over-year variations in Effective Capacity.
    • Green Line: Shows the trend of Effective Capacity over time.
  • Dynamic Y-Axis: Adjusts based on the range of variations for better readability.

🚀 Usage​

  1. Run the Script: Executes the ESS aging simulation and generates the visualization.
  2. View Results: Open the HTML file (ess_capacity_variation_yoy.html) in a browser.
  3. Integrate with Altergo: Use the generated dataset for further analysis or reporting.

📂 Files Generated​

  • ess_capacity_variation_yoy.html: Interactive Plotly visualization.
  • capacity_variation.html: Dataset stored in Altergo for future reference.

🛑 Dependencies​

  • Plotly: For visualization.
  • Pandas: For data manipulation.
  • Altergo SDK: For ESS configuration and dataset management.
  • Custom Utilities: For ESS model construction and styling.

📝 Notes​

  • The script assumes a 15-year simulation period. Adjust the range in the loop for different durations.
  • Ensure all dependencies are installed and properly configured before running the script.

🖼️ Icons Legend​

  • 📊: Data Visualization
  • 🛠️: Tools and Utilities
  • 📈: Metrics and Analysis
  • 🎨: Styling and Design
  • 🚀: Execution and Usage
  • 📂: Output Files
  • 🛑: Dependencies
  • 📝: Notes and Tips

This script provides a comprehensive analysis of ESS performance over time, making it a valuable tool for energy storage system monitoring and optimization.

assemblies\energy_block.py​

assemblies\power_conversion_unit.py​

assemblies\__init__.py​

components\battery_container.py​

components\component.py​

components\mini_soh.py​

components\pcs_unit.py​

components\switch_gear.py​

components\transformer.py​

components\__init__.py​

demo\clean_and_populate_workspace.py​

json_templates\efficiency.json​

json_templates\efficiency_lookup_generation.py​

AI Analysis​

📄 efficiency_lookup_generation.py Documentation

This Python script generates a lookup table for the efficiency of various components (e.g., transformer, inverter, battery) over their lifetime and saves the data to a text file.

🛠️ Functions​

generate_efficiency_lookup​

def generate_efficiency_lookup(starting_efficiency: float, end_of_life_efficiency: float, efficiency_range_in_years: int, nb_of_decimals: int = 2) -> dict:
  • Purpose: Generates a lookup table for the efficiency of a component over its lifetime.
  • Parameters:
    • starting_efficiency (float): Initial efficiency of the component.
    • end_of_life_efficiency (float): Efficiency of the component at the end of its life.
    • efficiency_range_in_years (int): Number of years over which the efficiency is calculated.
    • nb_of_decimals (int, optional): Number of decimal places to round the efficiency values. Default is 2.
  • Returns: A dictionary with two keys:
    • range: List of years (0 to efficiency_range_in_years - 1).
    • efficiency_values: List of efficiency values corresponding to each year, rounded to nb_of_decimals.

📂 Data Generation​

Efficiency Lookup Tables​

The script generates efficiency lookup tables for three components:

  1. Transformer:
    • Starting Efficiency: 0.99
    • End of Life Efficiency: 0.985
    • Lifetime: 15 years
    • Decimal Precision: 5
  2. Inverter:
    • Starting Efficiency: 0.98
    • End of Life Efficiency: 0.975
    • Lifetime: 15 years
    • Decimal Precision: 5
  3. Battery:
    • Starting Efficiency: 0.97
    • End of Life Efficiency: 0.94
    • Lifetime: 15 years
    • Decimal Precision: 5

📝 Output​

File Writing​

  • The script writes the generated lookup tables to a text file named efficiency_lookup.txt.
  • Each component's lookup table is written as a one-line JSON string with compact separators and single quotes.

Example Output​

transformer: {'range':[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],'efficiency_values':[0.99,0.98967,0.98933,0.989,0.98867,0.98833,0.988,0.98767,0.98733,0.987,0.98667,0.98633,0.986,0.98567,0.98533]}
inverter: {'range':[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],'efficiency_values':[0.98,0.97967,0.97933,0.979,0.97867,0.97833,0.978,0.97767,0.97733,0.977,0.97667,0.97633,0.976,0.97567,0.97533]}
battery: {'range':[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],'efficiency_values':[0.97,0.968,0.966,0.964,0.962,0.96,0.958,0.956,0.954,0.952,0.95,0.948,0.946,0.944,0.942]}

🖨️ Execution​

  • The script prints a confirmation message after writing the data to the file: 
    Data has been written to efficiency_lookup.txt

🛠️ Dependencies​

  • NumPy: Used for generating linear efficiency values over the component's lifetime.
  • JSON: Used for serializing the lookup tables into a compact string format.

🎨 Icons​

  • 📄: File
  • 🛠️: Function
  • 📂: Data Generation
  • 📝: Output
  • 🖨️: Execution
  • 🛠️: Dependencies
  • 🎨: Icons

This script is useful for simulating the degradation of component efficiency over time, which is essential in fields like renewable energy systems, electronics, and mechanical engineering.

json_templates\ess_excel.json​

json_templates\ess_excel_output.json​

json_templates\ess_mvp.json​

utils\demo_widget.py​

utils\ess_factory.py​

utils\miscs.py​

utils\output_generation.py​

utils\si_prefixes_conversion.py​

vizualisation\styles.py​

demo\bp_templates\blueprint_library.json​

demo\bp_templates\blueprint_skeletons.json​

demo\bp_templates\bp_dictionary.json​

demo\bp_templates\ess_component_models.json​

demo\bp_templates\example.json​