Dynamic economic dispatch with uncertain wind power generation using an enhanced artificial hummingbird algorithm

The optimum scheduling of the conventional thermal generators for three different dynamic test systems is percolated in this article. In pursuit of this objective, a developed version of a recent optimization algorithm, denoted as the leader artificial hummingbird algorithm, is introduced. The profile with the largest penetration of wind energy is obtained by calculating wind power from hourly wind speed using the Weibull distribution density function. After that, the test system and the wind profiles were connected to carry out dynamic economic dispatch (DED). The DED problem with wind uncertainty poses important challenges because of its complication, considered by multiple constraints including ramp rate limits and the valve-point effects (VPEs), nonconvexity, and nonlinearity, as well as the uncertainty of the wind energy. These complications make it critical to discover innovative optimization algorithms to find optimum solutions for the DED problem. First, in order to demonstrate the validity of the suggested LAHA approach in comparison with four contemporary techniques, simulations are run on 23 benchmark functions. Next, the 5-unit, 10-unit with/without transmission losses, 15-unit, modified 10-unit with transmission losses, and wind power test systems are used to evaluate the LAHA’s performance. The numerical results demonstrate how competitive the suggested approach is in reaching reduced total generation cost when compared to the other documented optimization algorithms.