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.