Incorporating renewable energy resources (RER) into standard power flow schedules is a complex optimization issue with multiple objectives and nonlinear characteristics. This matter necessitates the careful assessment of a multitude of economic and environmental concerns. This matter requires the evaluation of many restrictions related to disparities between races. The primary goal of generation scheduling is to minimize pollution emissions and costs over a limited time frame. This must be accomplished while ensuring that all system restrictions are adhered to. The Crisscross optimization (CCO) algorithm is used in this research to provide a novel method for solving the short-term hydro-thermal power scheduling (ST-HTPS) and short-term hydro-thermal-wind power scheduling (ST-HTWPS) issues. The suggested CCO method is compared to previously implemented particle swarm optimization (PSO) algorithms, moth-flame optimization (MFO) algorithms, and genetic algorithms (GA). This strategy makes convergence happen faster and solutions more precise while retaining a balance between exploration and exploitation. The proposed model takes into account real-time operational limitations, such as water balance equations, ramp rate limits, and wind uncertainty, to make sure that scheduling is both practical and effective. The suggested systems serve as study examples to evaluate the actual enhancement of the proposed CCO compared to PSO, MFO, PSO, and GA. The simulation outcomes indicate that the recommended CCO modeling offers a more advantageous option than previous heuristic techniques regarding financial considerations (36389. 25 /day) and reduced emissions (9436. 29 lb/day). Despite considering the inclusion of several intricate constraints related to ST-HTWPS scenarios, these findings remain unaltered.
Choudhary et al. (2026) studied this question.