PMV-based HVAC control strategy for demand response: Optimizing energy savings and occupant comfort

The increasing integration of renewable energy sources poses challenges to grid stability due to their inherent variability, highlighting the need for effective demand response (DR) strategies. This study proposes a novel PMV-based DR strategy for heating, ventilation, and air conditioning (HVAC) systems, using the predicted mean vote (PMV) index to account for thermal comfort factors and introducing a comfort duration metric to quantify HVAC DR potential. A residential microgrid model is used to evaluate the proposed strategy, incorporating dynamic PMV thresholds and day-ahead scheduling for energy optimization. The method is implemented in summer and winter scenarios, simulating HVAC operations for 100 households with varying environmental conditions. The results show that the PMV-based strategy reduces HVAC energy consumption by 18.26 % in summer compared to the traditional temperature-based control, while simultaneously increasing DR participation by 24 %. The approach maintains indoor comfort within a PMV range of −1 to +1 and reduces daily microgrid operating costs by 43.56 %. In winter, HVAC flexibility is limited due to reduced thermal inertia, underscoring the need for additional strategies such as heating load aggregation or thermal storage. The PMV-based control demonstrates superior energy efficiency and occupant comfort compared to temperature-based methods, particularly during high-demand periods. This approach provides a robust framework for improving DR performance and microgrid cost-effectiveness, making it suitable for diverse applications.