[DEPRECIATED]

This repo has been depreciated and rebuilt in the new Denki Carbon repo

🔌 japan_grid_carbon_api 🔌

aka Denki Carbon

API and Backend to Calculate the Current Carbon Intensity of the Japanese Grid...roughly

API Documentation

• Staging Docs at docs-staging.denkicarbon.jp
• Production Docs at docs.denkicarbon.jp
• Docs also hosted on SwaggerHub at https://app.swaggerhub.com/apis/FraserTooth/DenkiCarbon/1

Work so far:

• Pipeline for Extracting and Formatting All Utility Data
• Combined and extensible endpoint using Flask routing
• Added Pytest Tests
• Implementation of the Serverless deployment framework
• Basic Website Built @ denkicarbon.jp - Repo

Planned Work:

• Make carbon intensity value data available in the form of an API, based on averaging historic data
• Combine Weather Data and Historic Data with ML to make forecast about 'likely current intensity'
• Allow historic query on intensity DONE!
• Allow historic query on predictions
• Incorporate all regions in Japan DONE!
• Build Good Documentation for the API Now Written up in Swagger 2.0 Spec
• Add Good Documentation for the Code
• Solve the 'Pumped Storage' Problem 👇

How to Contribute

• Create an Issue to Explain your Problem, Feature or pick up an existing one, do leave a comment and feel free to ask questions 😁
• Fork the Project
• Clone Locally and Install Dependancies
• Set up your Google Account and Run the API Locally
• Make and commit your changes
• Open a PR to the master branch of this repo with a detailed explanation of your work (inc. screenshots)
• Guidelines in CONTRIBUTING.md

Local Setup

This project is aimed at UNIX runtimes, if you are on Windows, consider using WSL2 which works fine.

Python Bits

• Install Python3 to match the current Google Cloud Functions Runtime
  • I use Python 3.8, and I use pyenv to manage my local versions
• Consider using a virtual environment
• Install Pip
• Install project dependancies

Node Bits

• Install Node and ensure npm is installed
• Run cd cloud_functions/ && npm install to install serverless and its dependancies

Runtime Bits

• Install the Google Cloud SDK
• Setup your Google Account, following the Serverless Google Cloud Functions Guide
  • Name the project japan-grid-carbon-api-<environment> (prod is currently just japan-grid-carbon-api)
  • Add a storage bucket called scraper_data_<environmentName>
  • Add the BigQuery Admin role and a role containing cloudfunctions.functions.setIamPolicy to your service account (in addition to the roles listed in the guide)
  • (Everything else should be automatically generated, but there are a couple gotchas in naming etc., ask in issues if you have any problems)
• Place your Google Account service key in the ROOT DIRECTORY OF YOUR TERMINAL RUNTIME cd ~ named ./.gcloud/japan-grid-carbon-service-key-<environment>.json to match serverless.yml
• Run ./local.sh api staging to run the api function locally, and ./local.sh scrapers staging to run the scraper function locally with hot-reload in staging
• Use cURL, Postman etc. and ping http://localhost:8080/<etc> to initiate the function

Pumped Storage Problem

My initial assumption was that the Pumped Storage carbon intensity factor would be 0 gC02/kWh, like it is in the UK. This assumption was based in the idea that one would only 'charge' the pumped storage when there was renewable electricity available to charge it. Since, at least in the UK, renewable energy is considered 'spare' then all the 'spare power' goes into charging to storage.
However, some digging showed that for the TEPCO region, from 2014-2020, for >2500 datapoints, pumped storage was 'charged' when there was not enough renewable energy to fill it. The vast majority of these occasions were overnight (1AM - 5AM), where assumably the charging was done to ensure resiliance for the next day.
This poses a challenge, we now know that the Electricity in Pumped storage is not carbon neutral. And in order to model the Carbon inside the pumped storage for a given hour, we will have to look at the historical behaviour of the asset and figure out how much non-renewable power has gone into storage.
In the meantime, we can use a small average figure for the Carbon Intensity, but it would be good to get a more accurate result.

Data Sources

Data has been retrived from:

• CEPCO: Source Link
• Chuden: Source Link
• HEPCO: Source Link
• KEPCO: Source Link
• Kyuden: Source Link
• Okiden: Source Link
• Rikuden: Source Link
• TEPCO: Source Link
• Tohokuden: Source Link
• Yonden: Source Link
• And some other places

Example Graphs So Far:

An Early Carbon Intensity Graph, this is the average amount of Carbon Emitted in Tokyo for every unit of electricity used, for all hours of the day, for each month of the year.
Derived from Public TEPCO Data

Useful Functions Snippets

# Create a Virtual Environment
python -m venv .venv

# Open Virtual Environment
source .venv/bin/activate

# Read the Recent Logs
gcloud functions logs read

# Deploy Manually
#   First argument, environment (check deploy.sh if you need to change project IDs etc.)
./deploy.sh staging

# Run Locally
#   First argument, api name
#   Second argument, environment
./local.sh api staging
./local.sh scrapers production

# Run Tests
pytest -vv

# PIP Freeze with Line to Get Around pkg-resources==0.0.0 bug in Linux
pip freeze | grep -v "pkg-resources" > requirements.txt

Links Used when calculating the Carbon Intensities in Japan

• https://www.fepc.or.jp/about_us/pr/pdf/kaiken_s_e_20170616.pdf
• https://e-lcs.jp/news/detail/000208.html
• https://www.jepic.or.jp/pub/pdf/epijJepic2019.pdf
• https://www.fepc.or.jp/english/library/electricity_eview_japan/__icsFiles/afieldfile/2016/08/24/2016ERJ_full.pdf