Economic Load Dispatch Using Genetic Algorithm Optimization

Research on Economic Load Dispatch Using Genetic Algorithm

Economic load dispatch represents a critical challenge in power systems, aiming to optimally distribute electrical load to achieve the best balance between economic efficiency and reliability. To address this optimization problem, we employ genetic algorithms as the primary optimization tool, enabling more precise and efficient load distribution. The genetic algorithm mimics biological evolutionary processes through operations such as genetic inheritance, crossover, and mutation to continuously refine power load allocation strategies. In our implementation, we encode generator output levels as chromosomes and use fitness functions that incorporate fuel cost equations and system constraints.

In this research, we integrate genetic algorithms with economic load dispatch problems in power systems. By considering multiple factors including generation capacity, power generation costs, and load demand requirements, we search for optimal load distribution solutions. The algorithm implementation features population initialization with feasible solutions, tournament selection mechanisms, arithmetic crossover operations for solution exploration, and mutation operators to maintain diversity. Through genetic algorithm optimization, we can identify optimal solutions while handling multiple variables and constraints such as generator limits, ramp rate constraints, and power balance equations, providing comprehensive and practical load dispatch strategies.

The outcomes of this research contribute significantly to power system planning and operation, enhancing energy utilization efficiency, reducing operational costs, and ensuring system reliability and stability. The MATLAB-based implementation includes penalty functions for constraint handling and convergence criteria for termination. By employing genetic algorithms for economic load dispatch research, we support sustainable development in the power industry through computationally efficient optimization techniques that scale well for large-scale power systems.