Overview

The project implements a Locational Marginal Pricing (LMP) model to estimate electricity prices based on generator costs, demand, and transmission constraints. It also integrates real-world market data (EIA) and forecasting models (ARIMA, Machine Learning) to enhance price predictions.

Project Structure

📦 lmp_project

• 📂 data (Contains historical and real-time LMP data)
  • 📜 historical_prices.db (SQLite database storing LMP records)
• 📂 scripts
  • 📜 network_analysis.py (Models power grid and transmission constraints)
  • 📜 lmp_model.py (Runs LMP calculations using linear programming)
  • 📜 db_utils.py (Stores and retrieves LMP data from SQLite)
  • 📜 forecast_lmp.py (Forecasts future LMP using ARIMA)
  • 📜 run_all.py (Automates all steps in one script)
  • 📜 alert_script_hrly_tx.py (Alert system for hourly demand data in Texas)

Key Features

• Locational Marginal Pricing (LMP) Model: Calculates LMP based on supply, demand, and transmission constraints.
• Multi-Time Interval Support: Has the option to obtain EIA data for validation.
• Forecasting (ARIMA, Machine Learning): Predicts future LMPs based on historical trends.
• Database Storage (SQLite): Saves and retrieves LMP calculations.
• Data Visualization: Generates interactive plots for price trends.

Installation and Setup

1. Clone the Repository

   git clone https://github.com/daniela1484/Locational_Marginal_Pricing_Project.git
   cd Locational_Marginal_Pricing_Project
   

2. Create a Virtual Environment

   python -m venv env
   source env/bin/activate # Mac/Linux
   env\Scripts\activate    # Windows
   

3. Set Up the Database

   python scripts/db_utils.py
   

   This will create historical_prices.db and initialize the table.

4. Run the Full Model

   python scripts/run_all.py
   

   This will:

   • Model the power grid and constraints,
   • Compute LMPs,
   • Obtain real market data,
   • Forecast future LMPs,
   • Store and visualize results.

Example Output

LMPs: {'Total_Demand': 30.0, 'GenA_Capacity': -10.0, 'GenB_Capacity': 0.0}
GenA Output: 60.0 MW, GenB Output: 20.0 MW

Interpreting Results

• LMP at Demand Bus = $30/MWh (Cost of supplying 1 extra MW)
• GenA is at max capacity (-10.0 shadow price), which means that it is not a limiting factor.
• GenB has an available capacity (0.0 shadow price), it is not a limiting factor.

API Integration

Obtaining EIA Historical Price Data

EIA_API_URL = "https://api.eia.gov/series/?api_key=YOUR_EIA_KEY&series_id=ELEC.PRICE"

Forecasting Future LMPs

For time series forecasting, ARIMA will be used:

from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(df["lmp_value"], order=(2, 1, 2))
forecast = model.fit().forecast(steps=24)

Outputs a 24-hour price forecast.

Visualization

Use matplotlib to plot price trends:

import matplotlib.pyplot as plt
plt.plot(df.index, df["lmp_value"], label="Historical LMP")
plt.plot(forecast_dates, forecast, label="Forecasted LMP", linestyle="dashed")
plt.legend()
plt.show()