Skip to content

API to calculate production plan #50

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
e wants to merge 1 commit into
base: master
Choose a base branch
from
Open

API to calculate production plan #50

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
157 changes: 157 additions & 0 deletions .gitignore
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,157 @@
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
.pdm.toml
.pdm-python
.pdm-build/

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

.idea/
99 changes: 99 additions & 0 deletions README.ORIG.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,99 @@
# powerplant-coding-challenge


## Welcome !

Below you can find the description of a coding challenge that we ask people to perform when applying for a job in our team.

The goal of this coding challenge is to provide the applicant some insight into the business we're in and as such provide the applicant an indication about the challenges she/he will be confronted with. Next, during the first interview we will use the applicant's implementation as a seed to discuss all kinds of interesting software engineering topics.

Time is scarce, we know. Therefore we ask you not to spend more than 4 hours on this challenge. We know it is not possible to deliver a finished implementation of the challenge in only four hours. Even though your submission will not be complete, it will provide us plenty of information and topics to discuss later on during the talks.

This coding-challenge is part of a formal process and is used in collaboration with the recruiting companies we work with. Submitting a pull-request will not automatically trigger the recruitement process.
## Who are we

We are the IS team of the 'Short-term Power as-a-Service' (a.k.a. SPaaS) team within [GEM](https://gems.engie.com/).

[GEM](https://gems.engie.com/), which stands for 'Global Energy Management', is the energy management arm of [ENGIE](https://www.engie.com/), one of the largest global energy players,
with access to local markets all over the world.

SPaaS is a team consisting of around 100 people with experience in energy markets, IT and modeling. In smaller teams consisting of a mix of people with different experiences, we are active on the [day-ahead](https://en.wikipedia.org/wiki/European_Power_Exchange#Day-ahead_markets) market, [intraday markets](https://en.wikipedia.org/wiki/European_Power_Exchange#Intraday_markets) and [collaborate with the TSO to balance the grid continuously](https://en.wikipedia.org/wiki/Transmission_system_operator#Electricity_market_operations).

## The challenge

### In short
Calculate how much power each of a multitude of different [powerplants](https://en.wikipedia.org/wiki/Power_station) need to produce (a.k.a. the production-plan) when the [load](https://en.wikipedia.org/wiki/Load_profile) is given and taking into account the cost of the underlying energy sources (gas, kerosine) and the Pmin and Pmax of each powerplant.

### More in detail

The load is the continuous demand of power. The total load at each moment in time is forecasted. For instance for Belgium you can see the load forecasted by the grid operator [here](https://www.elia.be/en/grid-data/load-and-load-forecasts).

At any moment in time, all available powerplants need to generate the power to exactly match the load. The cost of generating power can be different for every powerplant and is dependent on external factors: The cost of producing power using a [turbojet](https://en.wikipedia.org/wiki/Gas_turbine#Industrial_gas_turbines_for_power_generation), that runs on kerosine, is higher compared to the cost of generating power using a gas-fired powerplant because of gas being cheaper compared to kerosine and because of the [thermal efficiency](https://en.wikipedia.org/wiki/Thermal_efficiency) of a gas-fired powerplant being around 50% (2 units of gas will generate 1 unit of electricity) while that of a turbojet is only around 30%. The cost of generating power using windmills however is zero. Thus deciding which powerplants to activate is dependent on the [merit-order](https://en.wikipedia.org/wiki/Merit_order).

When deciding which powerplants in the merit-order to activate (a.k.a. [unit-commitment problem](https://en.wikipedia.org/wiki/Unit_commitment_problem_in_electrical_power_production)) the maximum amount of power each powerplant can produce (Pmax) obviously needs to be taken into account. Additionally gas-fired powerplants generate a certain minimum amount of power when switched on, called the Pmin.


### Performing the challenge

Build a REST API exposing an endpoint `/productionplan` that accepts a POST of which the body contains a payload as you can find in the `example_payloads` directory and that returns a json with the same structure as in `example_response.json` and that manages and logs run-time errors.

For calculating the unit-commitment, we prefer you not to rely on an existing (linear-programming) solver but instead write an algorithm yourself.

Implementations can be submitted in either C# (on .Net 5 or higher) or Python (3.8 or higher) as these are (currently) the main languages we use in SPaaS. Along with the implementation should be a README that describes how to compile (if applicable) and launch the application.

- C# implementations should contain a project file to compile the application.
- Python implementations should contain a `requirements.txt` or a `pyproject.toml` (for use with poetry) to install all needed dependencies.

#### Payload

The payload contains 3 types of data:
- load: The load is the amount of energy (MWh) that need to be generated during one hour.
- fuels: based on the cost of the fuels of each powerplant, the merit-order can be determined which is the starting point for deciding which powerplants should be switched on and how much power they will deliver. Wind-turbine are either switched-on, and in that case generate a certain amount of energy depending on the % of wind, or can be switched off.
- gas(euro/MWh): the price of gas per MWh. Thus if gas is at 6 euro/MWh and if the efficiency of the powerplant is 50% (i.e. 2 units of gas will generate one unit of electricity), the cost of generating 1 MWh is 12 euro.
- kerosine(euro/Mwh): the price of kerosine per MWh.
- co2(euro/ton): the price of emission allowances (optionally to be taken into account).
- wind(%): percentage of wind. Example: if there is on average 25% wind during an hour, a wind-turbine with a Pmax of 4 MW will generate 1MWh of energy.
- powerplants: describes the powerplants at disposal to generate the demanded load. For each powerplant is specified:
- name:
- type: gasfired, turbojet or windturbine.
- efficiency: the efficiency at which they convert a MWh of fuel into a MWh of electrical energy. Wind-turbines do not consume 'fuel' and thus are considered to generate power at zero price.
- pmax: the maximum amount of power the powerplant can generate.
- pmin: the minimum amount of power the powerplant generates when switched on.

#### response

The response should be a json as in `example_payloads/response3.json`, which is the expected answer for `example_payloads/payload3.json`, specifying for each powerplant how much power each powerplant should deliver. The power produced by each powerplant has to be a multiple of 0.1 Mw and the sum of the power produced by all the powerplants together should equal the load.

### Want more challenge?

Having fun with this challenge and want to make it more realistic. Optionally, do one of the extra's below:

#### Docker

Provide a Dockerfile along with the implementation to allow deploying your solution quickly.

#### CO2

Taken into account that a gas-fired powerplant also emits CO2, the cost of running the powerplant should also take into account the cost of the [emission allowances](https://en.wikipedia.org/wiki/Carbon_emission_trading). For this challenge, you may take into account that each MWh generated creates 0.3 ton of CO2.

## Acceptance criteria

For a submission to be reviewed as part of an application for a position in the team, the project needs to:
- contain a README.md explaining how to build and launch the API
- expose the API on port `8888`

Failing to comply with any of these criteria will automatically disqualify the submission.

## More info

For more info on energy management, check out:

- [Global Energy Management Solutions](https://www.youtube.com/watch?v=SAop0RSGdHM)
- [COO hydroelectric power station](https://www.youtube.com/watch?v=edamsBppnlg)
- [Management of supply](https://www.youtube.com/watch?v=eh6IIQeeX3c) - video made during winter 2018-2019

## FAQ

##### Can an existing solver be used to calculate the unit-commitment
Implementations should not rely on an external solver and thus contain an algorithm written from scratch (clarified in the text as of version v1.1.0)

102 changes: 8 additions & 94 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,99 +1,13 @@
# powerplant-coding-challenge


## Welcome !
## To run the app

Below you can find the description of a coding challenge that we ask people to perform when applying for a job in our team.

The goal of this coding challenge is to provide the applicant some insight into the business we're in and as such provide the applicant an indication about the challenges she/he will be confronted with. Next, during the first interview we will use the applicant's implementation as a seed to discuss all kinds of interesting software engineering topics.

Time is scarce, we know. Therefore we ask you not to spend more than 4 hours on this challenge. We know it is not possible to deliver a finished implementation of the challenge in only four hours. Even though your submission will not be complete, it will provide us plenty of information and topics to discuss later on during the talks.

This coding-challenge is part of a formal process and is used in collaboration with the recruiting companies we work with. Submitting a pull-request will not automatically trigger the recruitement process.
## Who are we

We are the IS team of the 'Short-term Power as-a-Service' (a.k.a. SPaaS) team within [GEM](https://gems.engie.com/).

[GEM](https://gems.engie.com/), which stands for 'Global Energy Management', is the energy management arm of [ENGIE](https://www.engie.com/), one of the largest global energy players,
with access to local markets all over the world.

SPaaS is a team consisting of around 100 people with experience in energy markets, IT and modeling. In smaller teams consisting of a mix of people with different experiences, we are active on the [day-ahead](https://en.wikipedia.org/wiki/European_Power_Exchange#Day-ahead_markets) market, [intraday markets](https://en.wikipedia.org/wiki/European_Power_Exchange#Intraday_markets) and [collaborate with the TSO to balance the grid continuously](https://en.wikipedia.org/wiki/Transmission_system_operator#Electricity_market_operations).

## The challenge

### In short
Calculate how much power each of a multitude of different [powerplants](https://en.wikipedia.org/wiki/Power_station) need to produce (a.k.a. the production-plan) when the [load](https://en.wikipedia.org/wiki/Load_profile) is given and taking into account the cost of the underlying energy sources (gas, kerosine) and the Pmin and Pmax of each powerplant.

### More in detail

The load is the continuous demand of power. The total load at each moment in time is forecasted. For instance for Belgium you can see the load forecasted by the grid operator [here](https://www.elia.be/en/grid-data/load-and-load-forecasts).

At any moment in time, all available powerplants need to generate the power to exactly match the load. The cost of generating power can be different for every powerplant and is dependent on external factors: The cost of producing power using a [turbojet](https://en.wikipedia.org/wiki/Gas_turbine#Industrial_gas_turbines_for_power_generation), that runs on kerosine, is higher compared to the cost of generating power using a gas-fired powerplant because of gas being cheaper compared to kerosine and because of the [thermal efficiency](https://en.wikipedia.org/wiki/Thermal_efficiency) of a gas-fired powerplant being around 50% (2 units of gas will generate 1 unit of electricity) while that of a turbojet is only around 30%. The cost of generating power using windmills however is zero. Thus deciding which powerplants to activate is dependent on the [merit-order](https://en.wikipedia.org/wiki/Merit_order).

When deciding which powerplants in the merit-order to activate (a.k.a. [unit-commitment problem](https://en.wikipedia.org/wiki/Unit_commitment_problem_in_electrical_power_production)) the maximum amount of power each powerplant can produce (Pmax) obviously needs to be taken into account. Additionally gas-fired powerplants generate a certain minimum amount of power when switched on, called the Pmin.


### Performing the challenge

Build a REST API exposing an endpoint `/productionplan` that accepts a POST of which the body contains a payload as you can find in the `example_payloads` directory and that returns a json with the same structure as in `example_response.json` and that manages and logs run-time errors.

For calculating the unit-commitment, we prefer you not to rely on an existing (linear-programming) solver but instead write an algorithm yourself.

Implementations can be submitted in either C# (on .Net 5 or higher) or Python (3.8 or higher) as these are (currently) the main languages we use in SPaaS. Along with the implementation should be a README that describes how to compile (if applicable) and launch the application.

- C# implementations should contain a project file to compile the application.
- Python implementations should contain a `requirements.txt` or a `pyproject.toml` (for use with poetry) to install all needed dependencies.

#### Payload

The payload contains 3 types of data:
- load: The load is the amount of energy (MWh) that need to be generated during one hour.
- fuels: based on the cost of the fuels of each powerplant, the merit-order can be determined which is the starting point for deciding which powerplants should be switched on and how much power they will deliver. Wind-turbine are either switched-on, and in that case generate a certain amount of energy depending on the % of wind, or can be switched off.
- gas(euro/MWh): the price of gas per MWh. Thus if gas is at 6 euro/MWh and if the efficiency of the powerplant is 50% (i.e. 2 units of gas will generate one unit of electricity), the cost of generating 1 MWh is 12 euro.
- kerosine(euro/Mwh): the price of kerosine per MWh.
- co2(euro/ton): the price of emission allowances (optionally to be taken into account).
- wind(%): percentage of wind. Example: if there is on average 25% wind during an hour, a wind-turbine with a Pmax of 4 MW will generate 1MWh of energy.
- powerplants: describes the powerplants at disposal to generate the demanded load. For each powerplant is specified:
- name:
- type: gasfired, turbojet or windturbine.
- efficiency: the efficiency at which they convert a MWh of fuel into a MWh of electrical energy. Wind-turbines do not consume 'fuel' and thus are considered to generate power at zero price.
- pmax: the maximum amount of power the powerplant can generate.
- pmin: the minimum amount of power the powerplant generates when switched on.

#### response

The response should be a json as in `example_payloads/response3.json`, which is the expected answer for `example_payloads/payload3.json`, specifying for each powerplant how much power each powerplant should deliver. The power produced by each powerplant has to be a multiple of 0.1 Mw and the sum of the power produced by all the powerplants together should equal the load.

### Want more challenge?

Having fun with this challenge and want to make it more realistic. Optionally, do one of the extra's below:

#### Docker

Provide a Dockerfile along with the implementation to allow deploying your solution quickly.

#### CO2

Taken into account that a gas-fired powerplant also emits CO2, the cost of running the powerplant should also take into account the cost of the [emission allowances](https://en.wikipedia.org/wiki/Carbon_emission_trading). For this challenge, you may take into account that each MWh generated creates 0.3 ton of CO2.

## Acceptance criteria

For a submission to be reviewed as part of an application for a position in the team, the project needs to:
- contain a README.md explaining how to build and launch the API
- expose the API on port `8888`

Failing to comply with any of these criteria will automatically disqualify the submission.

## More info

For more info on energy management, check out:

- [Global Energy Management Solutions](https://www.youtube.com/watch?v=SAop0RSGdHM)
- [COO hydroelectric power station](https://www.youtube.com/watch?v=edamsBppnlg)
- [Management of supply](https://www.youtube.com/watch?v=eh6IIQeeX3c) - video made during winter 2018-2019

## FAQ

##### Can an existing solver be used to calculate the unit-commitment
Implementations should not rely on an external solver and thus contain an algorithm written from scratch (clarified in the text as of version v1.1.0)
This is a simple FastAPI application that meets the requirements. To run the app just create a new virtualenv, install the requirements and execute the command `python main.py`.

```commandline
virtualenv venv
. ./venv/bin/activate
pip install -r requirements.txt
python main.py
```
Loading