Skip to main content

Python application with nevergrad optimization model for calculating and simulating the least cost of an energy Mix under constraints.

Project description

MixSimulator

MixSimulator is an application with an optimization model for calculating and simulating the least cost of an energy mix under certain constraints. The optimizers used are based on the Nevergrad Python 3.6+ library.

The primary objective of the simulator is to study the relevance of an energy mix connected to each Inter-connected Grid through the coefficient of usage of each unit in the production cost.

Version 0.4

The current version is a multi-agent system (MAS) approach but keeps the previous classic optimization approach available. Check test_mas.py for more details. (Available events are : powerplant shutdown and powerplant power_on).

An experiment on evaluating both method is available in Experiments/Scenario_type.py or Open In Colab

Note

This project is a work in progress so it can not yet used in production (Many changes are on their way). Feedbacks are welcome!

Specifications :

  • Generic simulator, compatible with data from Madagascar and those from abroad (but may require data pre-processing beforehand);
  • Optimization of duty cycle (or usage coefficient) under constraints ;
  • Get the production cost and various performance indicators (CO2 emission, unsatisfied demand);
  • Estimate of the costs of a mix or a power plant over the long term ;
  • Comparison of the performance indicators on different optimizers. (see EvaluationBudget method)

Perspectives :

  • Add other constraints ;
  • Long-term Optimization ;
  • Pair with a transmission and distribution power grid simulator (MixSimulator can provide input data).

Suggestions are welcome!

How to install

It can be installed with :

pip install mixsimulator

MixSimulator is written in Python 3.6 and requires the following Python packages : nevergrad, prophet, typing, numpy, pandas and matplotlib.

How to run

As MixSimulator is a python package, it can be called and used as we can see in test_classic.py and test_mas.py.

Official documentation will accompany the first release version.

Datasets

Power plants dataset

The dataset "dataset_RI_Toamasina_v2.csv" is for the test and it comes from the Inter-connected energy mix of Toamasina Madagascar (2017) and Some information from the dataset is estimated.

Dataset features needed:

  • tuneable (boolean): is the control unit controllable or not?
  • green (boolean): is it a renewable energy plant?
  • hydro (boolean): is it a hydro power plant?
  • fuel (boolean): is it a thermal power plant?
  • centrals : identification
  • fuel_consumption (g/MWh): fuel consumption (in the case of a fossil fuel power plant)
  • availability (%): plant availability
  • fuel_cost ($/g): price of fuel used
  • init_value ($): initial investment in setting up the plant
  • lifetime (years): plant lifetime
  • carbon_production (g/MWh): emission rate of CO2 from the power plant
  • raw_power (MW): nominal power of the plant
  • nb_employees: number of employees at the central level
  • mean_salary ($): average salary of plant employees
  • demand (MWh): electricity demand
  • lost (MWh): electrical loss at another level (ie: transport network)

Hydro specification :

  • height (meter): height of the stream ;
  • flow : flow of the stream ;
  • stock_available : water reservoir ;
  • capacity : max water reservoir.

nb_employees * mean_salary can be used as a variable cost of the plant if you want to directly use other informations as variable cost.

Demand and Variation datas

There is also "DIR-TOAMASINA_concat.csv" about Consumption data (in kwh, more details in demand/) and "dataset_RI_Toamasina_variation_template.csv" about limits in variation of power plants load following (WIP).

If you have datasets of any region in the world that can be used to evaluate our model, please contact us.

Contact

For questions and feedbacks related to the project, please send an email to r.andry.rasoanaivo@gmail.com or soloforahamefy@gmail.com or tokyandriaxel@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mixsimulator-0.4.5.tar.gz (344.7 kB view details)

Uploaded Source

Built Distribution

mixsimulator-0.4.5-py3-none-any.whl (80.6 kB view details)

Uploaded Python 3

File details

Details for the file mixsimulator-0.4.5.tar.gz.

File metadata

  • Download URL: mixsimulator-0.4.5.tar.gz
  • Upload date:
  • Size: 344.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

File hashes

Hashes for mixsimulator-0.4.5.tar.gz
Algorithm Hash digest
SHA256 9f2c11bc304603e9e5e2ae53ac62bcc133b76973181d3da26a3165a11212f78e
MD5 cea7c5eeae7c58340fa679355b85f778
BLAKE2b-256 07c84e5ebbd4bd0ec71472478b789e6b3450041cf2a0823074913959d91e5f6a

See more details on using hashes here.

File details

Details for the file mixsimulator-0.4.5-py3-none-any.whl.

File metadata

  • Download URL: mixsimulator-0.4.5-py3-none-any.whl
  • Upload date:
  • Size: 80.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

File hashes

Hashes for mixsimulator-0.4.5-py3-none-any.whl
Algorithm Hash digest
SHA256 c69e8e9a73ebeb4155325d849fb62bf18f484e6accfb9873c3e6ce951642af80
MD5 0e36b57e8e35955289115142c88d4c87
BLAKE2b-256 c034f19d46df233095a496c0d8104962ae3c66765206590f2dacac8c551c51b5

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page