Power Grid Planning Using Genetic Algorithms

The research on applying genetic algorithms for power grid planning is highly significant. By implementing genetic algorithms in transmission network planning, we developed a specialized model that utilizes chromosome structures to represent potential network configurations. The core algorithm minimizes the objective function combining new line investment costs and annual system operational expenses through selection, crossover, and mutation operations. Our mathematical model integrates N-1 contingency checking procedures to validate planning scheme robustness, implemented through constraint handling techniques in the fitness function. To demonstrate practical implementation, we conducted optimization planning using the Garver-6 bus system as a test case, where population initialization and generation evolution processes were coded to iteratively improve solutions. These investigations provide deeper insights into leveraging genetic algorithms' global search capabilities for optimizing power grid layouts, thereby enhancing power system efficiency and reliability through computationally intelligent planning approaches.