High-efficiency stochastic optimization for Unit Commitment with personalized demand response and wind power uncertainty

Traditional demand response (DR) strategies often overlook flexible user utility, resulting in low engagement and underutilization of DR’s capacity to manage high-penetration wind power effectively. To address these issues, this brief introduces the variable user utility functions formed by two types of individual adjustment factors (IAF) and proposes personalized DR (PDR) strategies that deeply refine user utility, taking into account users’ inherent response preferences and environmental dynamic responses. Furthermore, by introducing an improved multi-visual mechanism (MVM) and parallelization techniques, this paper presents a powerful Parallel Group Search Optimizer (PGSO) algorithm to address the complexities of large-scale power systems. Ultimately, the efficiency of the proposed methods is validated through modeling the IEEE 118 system that includes the uncertainty of wind power, as a unit commitment with PDR (UCPDR) optimization model. The simulation results for four scenarios show that after the development of the PDR strategies, not only has the average utility sacrifice decreased by approximately 9.70% compared to the baseline scenario, but the fuel cost has also been reduced by 0.66%. In addition, compared to traditional stochastic optimizers, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES), the PGSO algorithm performs better in avoiding local optima and improving various system indicators. It is worth noting that, among up to 3.15 × 10⁹ evaluations, the PGSO algorithm completed its computation in just 8.01 s, demonstrating computational speed improvements of 98.53%, 98.15%, 99.23%, and 99.98%. In conclusion, the proposed approach offers more flexible and user-centric energy management, effectively addressing real-world challenges.