Current research on the participation of inverter-based air conditioners in demand response often prioritizes system performance during regulation periods yet frequently overlooks the prolonged high indoor temperatures that follow. Furthermore, oversimplified user comfort constraints limit the accurate evaluation of peak-shaving potential. To address these limitations, this paper proposes a novel control framework. First, a differential user comfort evaluation model is established to quantify the adjustable temperature range under varying scenarios. Second, an analog duty cycle grouped rotation control model is developed. By leveraging the variable-frequency characteristics of inverter ACs, this method optimized peak-shaving potential while preventing indoor temperatures from remaining at their upper limits for extended durations. Third, to ensure fairness, a user selection model incorporating a User Impact Factor is introduced as a dynamic ranking criterion for participation priority. Finally, to address the inevitable parameter mismatch in practical engineering, the control strategy is upgraded to a feedforward–feedback closed-loop framework. Simulation results demonstrate the superiority of the proposed ADC strategy over existing methods. Specifically, compared to existing methods, it achieved a 45–50% reduction in the high-temperature influence factor and a 67% decrease in the standard deviation of user impact, indicating significantly improved thermal comfort and fairness. Furthermore, the framework exhibits strong robustness; even under 20% parameter uncertainty, it restricted the duration of temperature exceedance to within 0.8%, strictly outperforming traditional open-loop approaches in preventing user discomfort. These improvements ensure a more uniform distribution of comfort impacts among users, thereby enhancing both the precision and sustainability of demand-side peak shaving.
Wu et al. (Mon,) studied this question.