Improved quadratic interpolation optimizer for stochastic short-term hydrothermal scheduling with integration of solar PV and wind power

The quadratic interpolation optimization (QIO) introduces a novel approach inspired by the generalized quadratic interpolation (GQI) with dual mechanisms. Initially, QIO employs GQI in its exploration strategy, updating populations based on two randomly selected individuals. Subsequently, it incorporates another exploration strategy, updating populations based on the best solution and two randomly selected individuals. Despite QIO's effectiveness in numerous optimization tasks, it exhibits limitations when addressing highly nonlinear and multidimensional problems, such as stagnation, susceptibility to local optima, low diversity, and premature convergence. In this study, we propose three enhancement strategies to refine traditional QIO, aiming to bolster its exploration and exploitation capabilities through Weibull flight motion, chaotic mutation, and PDO mechanisms. The resultant improved QIO (IQIO) is then applied to solve the short-term hydrothermal scheduling (STHS) problem, considering system uncertainties and the potential installation of PV and wind turbine generation units to reduce fuel costs and emissions. The STHS is solved with considering the system constraints including water discharge and reservoir storage, the generated powers by the hydro and thermal units as well as balanced powers. The dependent constraints are handled using weighted summation method. The efficacy of the proposed IQIO is demonstrated using the CEC 2022 test suite, and the obtained results are benchmarked against various competitive optimization methods. Statistical analysis of the results confirms a notable enhancement in the original QIO's performance upon applying the suggested IQIO. Furthermore, the inclusion of renewable generation units by IQIO yields maximum reductions of 23.73% in costs and 45.50% in emissions, underscoring its potential for sustainable energy management.