A Genetic Algorithm for Optimizing the Lifetime of Power Generation Equipment

In this study, we numerically solve an optimization problem of power generation equipment dismantling dynamics. The mathematical modeling aims to make a long-term forecast that determines the most efficient strategy for commissioning new capacities. This approach minimizes total costs, ensuring the required level of electricity demand. The mathematical formulation of the problem is represented through the Volterra integral equation of the first kind with variable limits. A key feature of the problem is determining the required parameter within the integration limits of both the functional and the constraints. The developed approach to finding an approximate solution to this problem relies on a genetic algorithm and factors in the constraints on commissioning capacity during the forecast period and on extending the equipment lifetime. The effectiveness of the proposed approach is illustrated through its comparison with the existing methods.