We are still at the early stage of the implementation. There will be more functionalities and flexible I/Os coming in the future. Please watch us progress to have the latest update.

This repo contains some basic power system operations written in Python and formulated by cvxpy, such as:

• Network Constrained Unit Commitment (with/out integer variables as QP/MIQP)
• Economic Dispatch (as QP, ongoing)
• Stochastic Unit Commitment (ongoing)

cvxpy: is an open source Python-embedded modeling language for convex optimization problems. It lets you express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.

PyPower: is a power flow and Optimal Power Flow (OPF) solver. It is a port of MATPOWER to the Python programming language.

However, you may also need to have Gurobi, Mosek or other optimization software to efficiently solve the optimization problems, especially if integers are included. Please refer here for details.

The implementation of this repo follows the online cource here and the textbook Power System Operations here, both by Prof. Antonio Conejo.

The package requires a .csv file to contain the configuration of power system operation. The basic format follows the MatPower and PyPower styles. An example file can be found here.

We recommend to construct the .csv file from the basic PyPower file to avoid errors. To do so, you also need to provide another .json file to specify configs that are not covered by the PyPower. Please have a look here. The detailed description on how to construct the extra config file can be found here.

The functions in test/standard_form.py are developed to reformulate the UC/ED in cvxpy form into the correspinding standard form. This conversion is general in addition to the UC/ED. Therefore it can be used outside power system operation. In this sense, you can "standardize" your problem by leveraging the descriptive power of cvxpy.

For a genenal QP without integer variable, it transforms into: $$ \begin{array}{rl} \min & (1/2) x^TPx + q^Tx \ \text{s.t.} & Ax = b \ & Gx \leq d \end{array} $$

For a general MIQP, it transforms into: