In this paper, a novel approach is presented for optimizing economic dispatch (ED) in power systems featuring solar farms and flexible loads. The ED problem is formulated as a multiobjective optimization task with stochastic characteristics. To address this challenge, a hybrid multiobjective algorithm named hybrid differential evolution algorithm (hDE)‐multiobjective flower pollination algorithm (MOFPA) is proposed, which integrates principles from the differential evolution algorithm and the MOFPA, yielding enhanced performance. Two weighted objective functions are introduced to represent operation cost and the power system generation flexibility index (GFI) based on carefully selected scenarios. The occurrence probability of each scenario influences the objective function’s final value. Monte Carlo simulation (MCS) is utilized for scenario selection, enabling a comprehensive assessment of system performance. We first evaluate the proposed algorithm by optimizing standard benchmark functions and comparing results against those of other state‐of‐the‐art algorithms. The outcomes demonstrate the superior accuracy and efficiency of the hDE‐MOFPA algorithm. In the subsequent simulation phase, we optimally solve the ED problem, considering uncertainties and the involvement of flexible loads. A sensitivity analysis is conducted to examine the impact of uncertainties and flexible loads on both cost and emissions. The results reveal that the combined uncertainty of load and photovoltaic (PV) significantly influences the system. By adopting this novel approach, our proposed method offers valuable insights into optimizing the ED problem in power systems with solar farms and flexible loads, considering uncertainties and various scenarios. The sensitivity analysis indicates that considering uncertainties leads to a 4.9% increase in operational expenditures and a 4.1% decrease in GFI. Also, uncertainties in load and irradiation range from 5% to 20%, GFI experiences a decline from 4.08% to 20.18%, while costs undergo an increase from 4.92% to 18.93%.
