grid-feedback-optimizer 0.1.4

Optimization methods for voltage and congestion management in modern power distribution grids.

Grid Feedback Optimizer

Overview

Grid Feedback Optimizer is a Python package that uses feedback optimization to optimize generator and device setpoints in electrical distribution grids. It reads a JSON/EXCEL network description and iteratively computes optimal setpoints.

This package is designed for experimenting with voltage regulation and congestion management, providing a flexible framework for feedback-based grid optimization.

Features

• Load and simulate networks from JSON/EXCEL files.
• Iterative feedback optimization using:
  • gradient projection (GP) algorithm,
  • primal-dual (PD) algorithm.
• Structured input and output data.
• Modular design (models, engine, utils) for extensions.

Repository Structure

grid_feedback_optimizer/
src/
    grid_feedback_optimizer/
        model/        # Loaders and I/O
        engine/       # Power flow / optimization logic
        utils/        # Helper functions
        main.py
examples/           # Example JSON/EXCEL network files
tests/              # Tests
requirements.txt    # Python dependencies
README.md

⚙️ Installation

Install from PyPI

pip install grid-feedback-optimizer

Usage

Python usage example:

from grid_feedback_optimizer.models.loader import load_network
from grid_feedback_optimizer.engine.solve import solve
from grid_feedback_optimizer.engine.powerflow import PowerFlowSolver

# Load network from example JSON
network = load_network("../examples/simple_example_with_transformer.json")

# Initialize and check power flow
power_flow_solver = PowerFlowSolver(network)

# Run optimization using the Gradient Projection (GP) algorithm
res_gp = solve(network, algorithm="gp")

# Display and store results
res_gp.print_summary()
res_gp.plot_iterations()
res_gp.save("gp_result.json")

Grid components

Follow power-grid-model for definition of buses (nodes), lines, transformers, and sources.

RenewGen

RenewGen models controllable generators and power-consuming devices.

• Generator: p_max > 0 and p_min >= 0
• Load: p_max < 0 and p_min <= 0
• Flexible device: p_min < 0 < p_max (can generate or consume)

Key attributes:

• index, bus: identifiers
• p_max, p_min: active power limits
• s_inv: apparent power rating
• p_norm: normal active power (auto-computed if not set)
• q_norm: normal reactive power (0.0 if not set)
• c1_p: linear active power cost coefficients
• c2_p: quadrtic active power cost coefficients for deviation from p_norm
• c1_q: linear reactive power cost coefficients
• c2_q: quadrtic reactive power cost coefficients for deviation from q_norm

Minimization cost function:

Cost = c1_p × p + c2_p × (p - p_norm)² + c1_q × q + c2_q × (q - q_norm)²

where p and q are the actual active and reactive power outputs.

p_norm is computed automatically:

• Generator → p_norm = p_max
• Load → p_norm = p_min
• Flexible → p_norm = 0

Load

Load models non-controllable units, either a generator or a load.

• Load: p_norm >= 0
• Generator: p_norm < 0

Key attributes:

• index, bus: identifiers
• p_norm, q_norm: active and reactive power

License

This project is licensed under the MIT License.

Author

Developed and maintained by Sen Zhan
📧 Email: [e-mail]