About

The problem at hand to be solved is optimizing the dispatch and transmission flow over time in an energy grid. We focus on a simplified version which focuses on deciding which generators to commit to meet the demand. This repository provides classical and quantum-based containerized solvers in python to solve the unit commitment problem given by a PyPSA network. It also contains the code to provide it as service on the PlanQK platform.

How to use

You can use this repository by making various recipes in the makefile. An optimization run can be started by making the recipe make general, which will start a docker container. Various settings can be adjusted in either the makefile or by providing a config file. We provide an example network and config file which can be used right away. The recipe will start an optimization run for every combination of network, config file and setting found by in the makefile. The result of the optimization will be saved as a JSON file.

This repository also contains some auxiliary scripts to generate problem instances and analyze results. Using those will be explained later.

Available solvers

Currently, we support the following solvers for the unit commitment problem:

1. Mixed Integer Linear Programming using GLPK. The linear programm is obtained via PyPSA.
2. Simulated Quantum Annealing. More information on this solver can be found here
3. QAOA via IBM's Qiskit runtime. This is extremely limited in problemsize. Some parts require an API-Token
4. D-Wave's cloud solvers. These require an API-Token and include quantum annealing and a hybrid solver.

Adjusting the Makefile

The makefile contains variables that specify where the PyPSA network to be solved is stored, which config file to use, and can also be used to temporarily overwrite values in the config file. A brief overview of the parameters you can adjust are the following

1. CONFIGFILES : This contains a glob which will be used to search the folder input/configs/ for config files.
2. NETWORKNAME : This contains a glob which will be used to search the folder input/networks/ for PyPSA networks.
3. SAVE_FOLDER : This specifies a path relative to the repositiories's root where the results will be saved to.

Overwriting values of the config files is further explained in the Makefile. Additional information on the variables above can also be found in the makefile. When cloning the repositiory, they are set up to point to an example network, the template for new config files, using the simulated quatum annealing solver and save it to results_general_sweep.

Configuring a solver

Which solver and which settings are used can be specified in a yaml file. The file input/configs/config-all.yaml contains information on all possible parameters that can be set for the various solvers, the structure that the config file has to have and the possibe values for each parameter.

Dependencies

We require docker to run the optimizations and make to use the Makefile. If you want to run it without docker, you need python3.9 and make the recipe for the virtual environment. This will download and install the python packages given in the requirements.txt. Using that environment also installs all packages required to plot the results.

We specifially need Python3.9 because that is what the Python binding of the simulated quantum annealing solver were made for.

Additional uses

The folder scripts/ contains various scripts that provide additional utility.

Plotting results

We provide some functionality to aggregate result saved in json files as pandas data frames and generate plots based on them in plot_results.py. You can do so by using the PlotAgent to read results, and make_figure to generate a plot based on that data. Further information can be found in the doc strings. The file make_plots_example.py provides a few examples. You can also run the recipe make plots to execute the script make_plots.py. This script is ignored by git so you can copy and rename the example file.

Generating test networks

The script problem_generator.py generates random problem instances of the unit commitment problem. You can specify the number of busses and the average capacity of a transmission line when calling it. These networks will be written to the folder networks. The script convert_to_json.py can be used to convert pypsa networks into json files, which is the format used by the planqk service.

Testing PlankQK service

The folder scripts contains two short scripts to build the image similar to the one build by the platform. Further information can be found in these scripts. They will build an image using the PlankQK_Dockerfile. When testing it with QAOA, keep in mind that it will take forever if the network is not tiny. The result that is returned by the service when testing it locally, will be written to the file JobResponse at the top level of the repository.

Documentation

You can find documentation for D-Waves cloud solvers here. For QAOA, you can find the algorithm here and the documentation for qiskit here. You can find further information on the PlanQK platform and PyPSA github page.

For more information on the solvers, you can have a look at DESCRIPTION.md, which is the description of the service on the platform and containts exlainations on the different solvers and configuration that you can use. The file UseCaseDescription.md gives a broad overview over the unit commitment problem. You can also find all valid configuration parameters explained in the example file configs/config-all.yaml.

Contributing

If you wish to contribute, you can raise an issue or get in touch with the owners of the repository via email: [email protected], [email protected]