Optimizing Power Generation in Bangladesh’s National Grid: A Mixed-Integer Linear Programming Approach for Cost Minimization and Efficiency Maximization in Mixed Energy Source Plants.

Title: Optimization Data for Power Generation in Bangladesh’s National Grid: MILP Model Results (2024)

Purpose:
This dataset supports the research study "Optimizing Power Generation in Bangladesh’s National Grid: A Mixed-Integer Linear Programming Approach for Cost Minimization and Efficiency Maximization in Mixed Energy Source Plants." It provides empirical evidence of the cost savings and efficiency gains achieved by applying a Mixed-Integer Linear Programming (MILP) model to Bangladesh’s power grid. The data validates the model’s ability to eliminate load shedding while minimizing generation costs.

Nature of the Data:
Format: Structured tabular data (PDF/CSV-ready).
Variables:
Date: Timestamp of observed demand (36 days in 2024).
Demand (MW): Total electricity demand on the grid.
Manual Generation (MW): Actual power supplied without optimization.
Manual Load Shedding (MW): Unmet demand due to inefficiencies.
MILP Generation (MW): Optimized power supply (load shedding = 0).
Costs (BDT): Comparison of manual vs. MILP-optimized production costs.
Savings (BDT): Cost reductions achieved by the MILP model (total: 3.042 billion BDT).

Scope:
Geographical Coverage: Bangladesh’s national grid.
Temporal Coverage: 36 days spanning January–June 2024.
Technical Scope: Covers 145 power plants with diverse fuel sources (e.g., gas, coal, renewables) and operational constraints.

Key Findings:
The MILP model eliminated load shedding entirely while reducing generation costs by $25.037 million (3.042 billion BDT).
Largest single-day savings: 272.36 million BDT (30 June 2024).
Demonstrates scalability for real-world grid optimization.

Potential Applications:
Policy design for cost-effective energy generation.
Benchmarking for future optimization models.
Academic research on energy economics and operational efficiency.