LMP_Toolkit

LMP_Toolkit is a Python based tool for efficiently computing Locational Marginal Prices (LMPs) from power system models exported from PowerWorld Simulator.

The tool allows users to modify electricity demand at individual buses and evaluate how LMPs change in response, using (approximate) Optimal Power Flow (OPF) analysis. Instead of manually running a single OPF case, LMP_Toolkit is designed for large scale scenario analysis, making it easy to run many load variations quickly and consistently.

The resulting outputs are structured text files that are easy to parse and well suited for:

• Data analysis
• Sensitivity studies
• Machine learning workflows

Features

1. Single OPF Analysis

Run an OPF for a specific load configuration to answer targeted "what if" questions.

• Modify load at one or more buses (absolute values or incremental changes)
• Compute locational marginal prices (LMPs) across all buses
• Results are printed to the console and optionally saved to text files

2. Multiple Load Scenarios (Batch Mode)

Automatically sweep across many load configurations to study price sensitivity.

• Define load ranges and step sizes for multiple buses
• Run OPF across all combinations of specified load values
• Analyze how LMPs change as bus level load varies
• Consolidated output for easy post processing
• Designed for fast, repeatable scenario sweeps and dataset generation

Setup

git clone https://github.com/wcolglazier/LMP_Toolkit.git
cd LMP_Toolkit
pip install -r requirements.txt

How to Use

Input File Format

The tool expects MATPOWER format (.m) case files exported from PowerWorld Simulator.

Single OPF Analysis

Step 1: Open single.py and set your configuration:

from opf.helper import get_current_load, run_opf_single, print_current_loads

file_name = "data.m"

# Print current loads
print_current_loads(file_path=file_name)

# Configuration - modify these values
bus_numbers_abs = [5, 3]        # Bus numbers to modify
new_loads_abs = [60, 55]        # New load values in MW

# Set to True to save results to .txt 
# Set to False to not save results
save_to_file = True

# Runs OPF
run_opf_single(file_path=file_name, save_to_file=save_to_file)

Step 2: Run the script:

python single.py

Multiple Load Scenarios

Step 1: Open multiple.py and configure your load ranges:

from opf.helper import print_current_loads, run_opf_loop

data_file = "data.m"

# Print current loads
print_current_loads(file_path=data_file)

# Configuration - modify these values
bus_numbers = [5, 3]             # Buses to modify
load_starts = [40, 20]           # Starting load values (MW)
load_ends = [50, 30]             # Ending load values (MW)
load_step_sizes = [1, 1]         # Step size for each bus (MW)

# Set to True to save results to .txt 
# Set to False to not save results
save_to_file = False

# Runs OPF
run_opf_loop(bus_numbers, load_starts, load_ends, load_step_sizes, file_path=data_file, save_to_file=save_to_file)

Step 2: Run the script:

python multiple.py

Example Outputs

Single OPF Analysis Output

Console/File Output:

Base Load Values:
========================================
Bus 2: 25.00 MW
Bus 3: 28.00 MW
Bus 4: 35.00 MW
Bus 5: 70.00 MW
========================================

Loads: Bus 3: 55.00 MW, Bus 5: 58.00 MW
Bus 1: $6.80/MWh
Bus 2: $7.26/MWh
Bus 3: $7.67/MWh
Bus 4: $7.72/MWh
Bus 5: $7.78/MWh

Multiple Load Scenarios Output

Console/File Output:

Base Load Values:
========================================
Bus 2: 25.00 MW
Bus 3: 28.00 MW
Bus 4: 35.00 MW
Bus 5: 70.00 MW
========================================

Loads: Bus 3: 20.00 MW, Bus 5: 40.00 MW
Bus 1: $6.61/MWh
Bus 2: $6.89/MWh
Bus 3: $7.08/MWh
Bus 4: $7.13/MWh
Bus 5: $7.16/MWh

Loads: Bus 3: 21.00 MW, Bus 5: 41.00 MW
Bus 1: $6.62/MWh
Bus 2: $6.91/MWh
Bus 3: $7.10/MWh
Bus 4: $7.15/MWh
Bus 5: $7.18/MWh

Loads: Bus 3: 22.00 MW, Bus 5: 42.00 MW
Bus 1: $6.64/MWh
Bus 2: $6.93/MWh
Bus 3: $7.12/MWh
Bus 4: $7.17/MWh
Bus 5: $7.21/MWh
...