A Complete Computational Case Study Using Biogeography-Based Optimization for Load Dispatch in Combined Heat and Power Plants

This paper presents a complete computational case study demonstrating the application of biogeography-based optimization (BBO) to solve the load dispatch optimization problem in combined heat and power plants. The problem formulation incorporates multiple constraints including electricity demand, energy production costs, equipment capacity limitations, and operational boundaries. Through systematic analysis of these factors and implementation of the BBO algorithm, we derive an optimal load distribution scheme that maximizes economic benefits and resource utilization efficiency. The study elaborates on the algorithmic framework with detailed explanations of key components: immigration/emigration rates calculation using sinusoidal models, habitat suitability index (HSI) evaluation, and mutation operations with elitism preservation. Code implementation highlights include population initialization methods, fitness function design accommodating thermal-electrical balance constraints, and migration probability matrices for solution exchange between habitats. Results analysis section provides comparative performance metrics, convergence curves visualization, and sensitivity analysis of control parameters. Additionally, we explore potential applications and scalability prospects of BBO in related energy optimization domains, providing valuable references and insights for subsequent research in sustainable energy management systems.