Enhancing renewable energy utilization through solar, wind, and electric vehicles in the grid using a chaotic-based prairie dog optimization algorithm

This study aims to determine the most effective operational strategy for economic load dispatch (ELD). The practical ELD problem can be difficult to solve because of its non-smooth cost function and nonlinear constraints; specifically, electric power generation's nonlinear multi-objective economic emission dispatch (EED) problem with valve point loading is solved using the prairie dog optimization algorithm-based optimization (PDO). The problem considers nonlinear generator aspects such as valve point effect, ramp rate limits, and restricted operating zones. The PDO technique identifies the optimal solution without requiring any prior knowledge of the gradient of the objective function. The application of the PDO algorithm to solve nonlinear ELD problems appears to be an effective and reliable optimization technique. Three test cases are considered. For example, 40 units with losses (thermal units and renewable energy), 140 units (renewable energy), and 160 units (thermal units and renewable energy) are used for executing and evaluating the suggested algorithm. The outcomes demonstrate the suggested algorithm's potential and efficacy in comparison to other alternative techniques, and the suggested approach outperforms other established algorithms in terms of proficiency and robustness, as demonstrated by numerical data.