distopf 0.1.2

A package for solving unbalanced three-phase distribution system optimal power flow problems.

DistOPF

This DistOPF provides an open-source, three-phase, asymmetric, optimal power flow (OPF) tool for distribution systems to aid students and researchers. The tool aids users by providing:

• Asymmetrical 3-Phase OPF model generators usable with common Python solver packages such as CVXPY and SciPy;

• A platform for creating and benchmarking new algorithms on a set of standard test systems;

• An OpenDSS model importer allowing users to import power system models directly from the OpenDSS model format;

• Model validation with OpenDSS;

• Functions for visualizing results.

Currently, no Python package is designed for optimal power flow for distribution systems with asymmetric phases. The Python package, PandaPower provides tools for simulating distribution systems but requires all lines to be three-phase.

The tool is composed of four major parts, 1) model input system, 2) optimization model formulation, 3) OPF solver interface, and 4) solution output and visualization. Models are described using a set of CSV files that are read in as Pandas DataFrames. Buses are described in one CSV having columns for loads, base units, and voltage limits. Lines, switches, and transformers are described in a CSV having columns for each term in the upper diagonal impedance matrix. Regulators, capacitor banks, and generators each have their own CSV. To aid in model creation and validation, models can also be created using OpenDSS and converted to the tabular format. The tool provides classes and functions to make it easy to formulate and solve the power system for new users while being flexible for advanced users to create new models and algorithms. The tool has been used to solve a variety of problems including, conservative voltage reduction, power loss minimization, solar curtailment minimization, where either generator real or reactive power injections are controlled.

Installation

pip install

pip install distopf

Developer Installation

To install the latest version from github:

1. From the directory you want to keep your distopf files, run:

git clone https://github.com/nathantgray/distopf.git

3. Create or activate the python environment you want to use.
4. From the directory where the distopf package is stored, run:

pip install -e .

This installs your local distopf package the python environment you activated. The -e option enables editable mode, which allows you to directly edit the package and see changes immediately reflected in your environment without reinstalling.

Getting Started

Using provided cases:

Unconstrained Power Flow

import distopf as opf
case = opf.DistOPFCase(data_path="ieee123")
case.run_pf()  # run an unsconstrained power flow
case.plot_network().show(renderer="browser")

DER Curtailment Minimization

import distopf as opf
case = opf.DistOPFCase(
    data_path="ieee123_30der",  
    control_variable="P",  # Control DER active power injection
    objective_function="curtail_min",  # DER Curtailment Minimization
    v_max=1.05, v_min=0.95, 
    gen_mult=10  # multiply generator output by 10
)
case.run()
case.plot_network().show(renderer="browser")

Using a custom model.

Create CSVs formatted as shown below and store them in a single folder. The csv names must match exactly as shown. Column order is not important.

-your_model_directory
   -branch_data.csv
   -bus_data.csv
   -gen_data.csv
   -cap_data.csv
   -reg_data.csv

import distopf as opf
case = opf.DistOPFCase(
    data_path="path/to/your_model_directory",
)

Or load them as dataframes

import distopf as opf
import pandas as pd
branch_data = pd.read_csv("path/to/your_model_directory/branch_data.csv", header=0)
bus_data = pd.read_csv("path/to/your_model_directory/bus_data.csv", header=0)
gen_data = pd.read_csv("path/to/your_model_directory/gen_data.csv", header=0)
cap_data = pd.read_csv("path/to/your_model_directory/cap_data.csv", header=0)
reg_data = pd.read_csv("path/to/your_model_directory/reg_data.csv", header=0)
case = opf.DistOPFCase(
    branch_data=branch_data,
    bus_data=bus_data,
    gen_data=gen_data,
    cap_data=cap_data,
    reg_data=reg_data
)

branch_data.csv

• fb: From bus id number
• tb: To bus id number
• r: resistance in p.u.
• x: reactance in p.u.
• type: overhead_line, switch, transformer, etc.
• name: other name of line
• status: (for switches) OPEN or CLOSED
• s_base: base VA
• v_ln_base: base line-to-neutral voltage
• z_base: base impedance

bus_data.csv

• id: unique id for each bus (integer starting at 1)
• name: bus name
• pl_a, ql_a, pl_b, ql_b, pl_c, ql_c: active and reactive loads p.u.
• bus_type: SWING or PQ. SWING bus is voltage source
• v_a, v_b, v_c: voltage magnitude p.u. (input parameter for SWING bus. Other not used as input)
• v_ln_base: base line-to-neutral voltage (V)
• s_base: base power (VA)
• v_min, v_max: voltage magnitude limits (p.u.)
• cvr_p, cvr_q: conservative voltage reduction parameters; alternative to ZIP model for voltage dependant loads. (set to 0 for no voltage dependence) (see. [?])
• phases: phases at bus (e.g. "abc", "a", "ab", etc.)

gen_data.csv

• id: bus id
• name: generator name
• pa, pb, pc: active power output (p.u.)
• qa, qb, qc: reactive power output (p.u.)
• s_base: base power (VA)
• sa_max, sb_max, sc_max: rated maximum apparent power output (VA)
• phases: generator phases (abc string) (this IS implemented)
• qa_max, qb_max, qc_max: (not implemented) maximum reactive power output (p.u.)
• qa_min, qb_min, qc_min: (not implemented) minimum reactive power output (p.u.)

cap_data.csv

• id: bus id
• name: capacitor name
• q_a, q_b, q_c: nominal reactive power (p.u.)
• phases: capacitor phases (abc string)

reg_data.csv

• fb: From bus id number
• tb: To bus id number
• name: regulator name
• tap_a, tap_b, tap_c: tap position (p.u.) -16 to +16; 0 is no tap change

DistOPFCase Optimization Options

control_variable: str

• Control variable for optimization. Options (case-insensitive):
  • None or "": Power flow only with no optimization. objective_function options will be ignored.
  • "P": Active power injections from generators. Active power outputs set in gen_data.csv will be ignored and reactive power outputs set in gen_data static.
  • "Q": Reactive power injections from generators. Active power outputs set in gen_data.csv are constant and reactive power outputs set in gen_data.csv will be ignored.

objective_function: str or Callable

• Objective function for optimization. Options (case-insensitive):
  • "gen_max": Maximize output of generators. Uses scipy.optimize.linprog.
  • "load_min": Minimize total substation active power load. Uses scipy.optimize.linprog.
  • "loss_min": Minimize total line active power losses. Quadratic. Uses CVXPY.
  • "curtail_min": Minimize DER/Generator curtailment. Quadratic. Uses CVXPY.
  • "target_p_3ph": Substation load tracks active power target on each phase. Quadratic. Uses CVXPY.
  • "target_q_3ph": Substation load tracks reactive power target on each phase. Quadratic. Uses CVXPY.
  • "target_p_total": Substation load tracks total active power. Quadratic. Uses CVXPY.
  • "target_q_total": Substation load tracks total reactive power. Quadratic. Uses CVXPY.

OpenDSS Interface

You may also run using an OpenDSS model file as input.

import distopf as opf
case = opf.DistOPFCase(
    data_path="path/to/your_model_directory/model.dss",
)

Citing this tool

Paper coming soon.