Optimal power flow via CVXPY, supporting AC-OPF (nonconvex, DNLP), lossy DC OPF (convex QP), and single-node DC dispatch (convex QP).

Grid resiliency events rarely happen in an instant. The most dangerous scenarios unfold over days: solar suppressed by sustained weather systems, load elevated beyond seasonal norms, and battery storage depleted by controllers that optimize for the current hour. Studying the behavior of the modern grid under these conditions and developing optimal control policies requires an optimization framework that is simultaneously time-aware and physically grounded, that is able to plan dispatch strategy across a full multi-day horizon and able to enforce the AC network constraints that determine whether a plan is actually executable.

cvxopf is designed with this application in mind. It formulates optimal power flow problems using CVXPY, supports full AC-OPF, a convex lossy DC relaxation, and single-node economic dispatch from a single entry point (with more to come), and handles multi-step optimization with time-varying load, battery storage, and nondispatchable generation (wind, solar, hydro) natively. The intended use case is resiliency research: studying how battery controllers should behave under adverse multi-day conditions, how much temporal foresight matters, and how well convex approximations track AC feasibility across extended horizons.

Because it is built on CVXPY, the problem structure is transparent and composable. Researchers can modify objectives, add contingency constraints, or experiment with formulations — including multi-forecast Model Predictive Control — without rewriting solver interfaces.

cvxopf formulates optimal power flow problems using CVXPY and solves them with appropriate solvers. It is designed to:

• Run MATPOWER/Pypower test cases out of the box
• Support multiple OPF formulations from a single entry point
• Support single-shot optimization over multiple time steps
• Accept time-varying nodal load as pandas DataFrames
• Model energy storage with state-of-charge dynamics
• Model nondispatchable generators (wind, solar, run-of-river hydro) with curtailable output and reactive power support

The "singlenode_dc" formulation collapses the whole network to a single bus: no branch flows, no transmission limits, no losses, no reactive power — just total generation equals total load. It is the classic economic dispatch problem, useful as a fast baseline and for large-horizon energy planning.

The intended workflow for large-scale resiliency studies is hierarchical: solve the convex lossy_dc formulation over the full planning horizon to obtain a globally optimal battery SoC trajectory and dispatch plan, then use the AC formulation over a short receding horizon to verify and correct for true network physics, with SoC targets inherited from the convex layer as boundary constraints.

References:

• AC OPF: Disciplined Nonlinear Programming, https://stanford.edu/~boyd/papers/dnlp.html, https://github.com/cvxgrp/dnlp-examples/blob/main/nlp_examples/power_flow.ipynb
• Lossy DC OPF: Convex Optimization with Smart Grid Examples, https://doi.org/10.2172/3018252

cvxopf requires the IPOPT nonlinear solver system library. This must be installed before running pip install cvxopf.

Ubuntu / Debian

sudo apt-get update
sudo apt-get install -y coinor-libipopt-dev liblapack-dev libblas-dev gfortran

Note: On Linux, coinor-libipopt-dev alone is not sufficient. liblapack-dev, libblas-dev, and gfortran are also required because IPOPT's internal linear solver (MUMPS) links against them at build time. Without these, pip install cvxopf will fail when building cyipopt with a linker error (cannot find -llapack, cannot find -lblas).

macOS

brew install ipopt

Windows (conda recommended)

conda install -c conda-forge ipopt

Full IPOPT installation documentation: https://coin-or.github.io/Ipopt/INSTALL.html

Once the IPOPT system library is installed:

pip install git+https://github.com/cvxgrp/cvxopf.git

This will automatically install all Python dependencies including cyipopt (the Python interface to IPOPT), cvxpy, numpy, and pandas.

When the package is published to PyPI, installation will simplify to:

pip install cvxopf  # coming soon

AC-OPF:

from cvxopf.testcases import case9
from cvxopf.problem import build_opf
from cvxopf.results import extract_results

build = build_opf(case9(), formulation="ac")
build.solve()
results = extract_results(build)
print(f"Objective: {results['objective']:.2f} $/hr")
print(f"Pg (MW):   {results['Pg']}")

Lossy DC OPF:

from cvxopf.testcases import case14
from cvxopf.problem import build_opf
from cvxopf.results import extract_results

build = build_opf(case14(), formulation="lossy_dc")
build.solve()
results = extract_results(build)
print(f"Objective:  {results['objective']:.2f} $/hr")
print(f"Pg (MW):    {results['Pg']}")
print(f"Flows (MW): {results['p_flows']}")

Single-node DC dispatch:

Any MATPOWER case works (the branch table is ignored), or use the make_singlenode_case convenience constructor to build a minimal economic-dispatch problem without a full network:

from cvxopf.testcases import make_singlenode_case
from cvxopf.problem import build_opf
from cvxopf.results import extract_results

case = make_singlenode_case(
    P_load_MW=250.0,
    generators=[
        {"P_max_MW": 200.0, "cost_coeffs": (0.0, 10.0, 0.05)},  # (c0, c1, c2)
        {"P_max_MW": 150.0, "cost_coeffs": (0.0, 15.0, 0.08)},
    ],
)

build = build_opf(case, formulation="singlenode_dc")
build.solve()
results = extract_results(build)
print(f"Objective: {results['objective']:.2f} $/hr")
print(f"Pg (MW):   {results['Pg']}")

The examples/case14_formulation_comparison.py script solves the IEEE 14-bus case with all three formulations side by side and contrasts their dispatch and implied losses.

We recommend using uv (https://docs.astral.sh/uv/) to run the notebooks. From the project root:

uv run --extra notebook marimo run notebooks/cvxopf_demo.py

Select a test case (case9 through case118), choose AC-OPF or lossy DC OPF, adjust generator limits, branch flow limits, and load scale interactively. Results update automatically after each solve.

uv run --extra notebook marimo run notebooks/benchmark_opf.py

Select number of repetitions and run a timing study across all test cases and OPF configurations. The results should look something like this:

Time-varying load is passed as a DataFrame — one row per timestep, one column per bus. This is the foundation for resiliency studies: feed in a multi-day solar and load profile and the optimizer plans dispatch across the full horizon in a single solve.

import numpy as np
import pandas as pd
from cvxopf.testcases import case9
from cvxopf.problem import build_opf_multistep
from cvxopf.results import extract_results

ppc     = case9()
T       = 3
Pd_base = ppc["bus"][:, 2]
Qd_base = ppc["bus"][:, 3]

# Three time steps at 80%, 100%, 120% of base load
scales  = [0.8, 1.0, 1.2]
df_P    = pd.DataFrame(np.outer(scales, Pd_base))
df_Q    = pd.DataFrame(np.outer(scales, Qd_base))

build   = build_opf_multistep(ppc, df_P, df_Q, T=T, formulation="ac")
build.solve()
results = extract_results(build)
print(f"Total objective: {results['objective']:.2f} $/hr")
print(f"Pg per step (MW):\n{results['Pg']}")

Battery state-of-charge evolves across timesteps, coupling decisions made at hour 1 to feasibility at hour 72. This intertemporal coupling is why multi-step optimization matters for resiliency: the optimizer can see that conditions worsen on day 3 and hold reserves accordingly rather than depleting storage on day 1.

import numpy as np
import pandas as pd
from cvxopf.testcases import case9
from cvxopf.problem import build_opf_multistep, StorageUnitIdeal
from cvxopf.results import extract_results

ppc     = case9()
T       = 3
Pd_base = ppc["bus"][:, 2]
Qd_base = ppc["bus"][:, 3]

scales = [0.8, 1.0, 1.2]
df_P   = pd.DataFrame(np.outer(scales, Pd_base))
df_Q   = pd.DataFrame(np.outer(scales, Qd_base))

unit = StorageUnitIdeal(
    bus=5,
    apparent_power_rating=50.0,  # MVA
    capacity=100.0,              # MWh
    initial_soc=50.0,            # MWh
    aging_weight=1e-2,           # $/MW
)

build = build_opf_multistep(
    ppc, df_P, df_Q, T=T, formulation="ac",
    storage=[unit], delta=1.0,
)
build.solve()
results = extract_results(build)
print(f"Total objective: {results['objective']:.2f} $/hr")
print(f"Storage real power (MW): {results['b']}")
print(f"State of charge (MWh):   {results['soc']}")

Nondispatchable generators (wind turbines, PV arrays, run-of-river hydro) produce up to a time-varying available power R_t determined by ambient conditions. The OPF can curtail freely; there is no cost for generation or curtailment. In AC, the inverter also provides reactive power support within its apparent power rating. Passing a multi-day solar availability profile via df_nd lets the optimizer account for sustained generation shortfalls across the full planning horizon.

import numpy as np
import pandas as pd
from cvxopf.testcases import case9
from cvxopf.problem import build_opf_multistep, NondispatchableUnit
from cvxopf.results import extract_results

ppc     = case9()
T       = 3
Pd_base = ppc["bus"][:, 2]
Qd_base = ppc["bus"][:, 3]

scales = [0.8, 1.0, 1.2]
df_P   = pd.DataFrame(np.outer(scales, Pd_base))
df_Q   = pd.DataFrame(np.outer(scales, Qd_base))

# Solar unit on bus 5: available output ramps down over three steps,
# simulating a multi-day irradiance suppression event
unit  = NondispatchableUnit(
    bus=5,
    p_available=100.0,            # MW — fallback for single-step
    apparent_power_rating=120.0,  # MVA — inverter nameplate
)
df_nd = pd.DataFrame({5: [100.0, 75.0, 50.0]})  # MW available per step

build = build_opf_multistep(
    ppc, df_P, df_Q, T=T, formulation="ac",
    nondispatchable=[unit], df_nd=df_nd,
)
build.solve()
results = extract_results(build)
print(f"Total objective:      {results['objective']:.2f} $/hr")
print(f"ND real power (MW):   {results['p_nd']}")
print(f"ND reactive (MVAr):   {results['q_nd']}")
print(f"Curtailment (MW):     {results['curtailment']}")

HVDC links are controllable point-to-point power transfers between two buses, modelled as signed nodal injections (Convention B: positive = injection into the grid) subject to capacity limits. On fixed-direction links the converter loss is proportional (p_out = -(1 - loss_frac) * p_in). Links can be built directly as HVDCLink objects or imported from a MATPOWER dcline table via hvdc_from_dcline. The MVP is unity power factor and drops fixed converter loss (loss0, emitting a UserWarning); it applies to both the AC and lossy DC formulations.

from cvxopf.testcases.case9_dcline import case9_dcline
from cvxopf.problem import build_opf
from cvxopf.hvdc import hvdc_from_dcline
from cvxopf.results import extract_results

ppc   = case9_dcline()
links = hvdc_from_dcline(ppc["dcline"])  # three in-service DC links

build = build_opf(ppc, formulation="ac", hvdc=links)
build.solve()
results = extract_results(build)
print(f"Objective:          {results['objective']:.2f} $/hr")
print(f"HVDC in  (MW):      {results['p_hvdc_in']}")
print(f"HVDC out (MW):      {results['p_hvdc_out']}")
print(f"HVDC loss (MW):     {results['hvdc_loss']}")

src/cvxopf/           Core package
  problem.py          Public API: build_opf, build_opf_multistep
  ac_problem.py       AC-OPF helpers (DNLP formulation)
  dc_problem.py       Lossy DC OPF helpers (convex QP)
  singlenode_dc_problem.py  Single-node DC dispatch helpers (convex QP)
  network.py          Ybus, incidence matrices, reindexing
  cost.py             Generator cost expression builders
  data.py             Input validation and time-series handling
  results.py          Result extraction and comparison utilities
  storage.py          StorageUnitIdeal dataclass and helpers
  nondispatchable.py  NondispatchableUnit dataclass and helpers
  testcases/          Built-in MATPOWER test cases (case9 — case118)
tests/                Pytest test suite
tests/fixtures/       Committed Pypower reference outputs (static)
scripts/              Fixture and test case generation scripts
notebooks/            Interactive marimo notebooks
examples/             Runnable example scripts

Clone the repository and install in editable mode with development dependencies:

git clone https://github.com/cvxgrp/cvxopf.git
cd cvxopf

If you have uv installed, that's it. Just run things with uv from the project root.

If you are managing your own virtual environment, install the development dependencies with pip:

pip install -e ".[dev]"

If you want to run the Marimo notebooks, you'll want the notebook dependencies as well:

pip install -e ".[dev,notebook]"

To run the test suite:

# If using uv (recommended)
uv run --extra dev pytest tests/ -v

# If installed directly with pip
pytest tests/ -v

To regenerate the Pypower reference fixtures (requires uv):

uv run scripts/generate_pypower_fixtures.py

Note: the fixture script runs in an isolated environment with pinned pypower==5.1.19 and numpy==2.2.6. It does not affect the main package environment.

• Repository skeleton
• Port and modularize working code
• Pypower fixture generation and validation tests
• Multi-step problem builder
• Branch flow limits (AC)
• Battery/storage model
• Lossy DC OPF and multi-formulation architecture
• HVDC transmission links (lossless + fixed-direction proportional loss) with reactive power suppport
• Full lossy HVDC (sign-switching converter losses via charge/discharge split)
• Nondispatchable generators
• Sparse P/Q variables for AC-OPF
• Single-node equivalent "copper plate" model
• SOCP network model
• Extend battery parameters: final SoC, penalty vs constraint
• Implement cvxpy parameters for problem data
• Vectorize time constraints (currently built with iterative loop)
• Unify grid component model patterns (dispatchable generators, storage, nondispatchable → first-class composable components)