Current research on air-conditioning system flexibility primarily evaluates the peak-shaving effect of individual strategies, lacking a comprehensive quantitative comparison of the benefits and costs of multi-strategy coordination. This paper proposes an integrated evaluation method for multiple flexible regulation strategies that incorporates the rebound effect as a demand response cost. Using an office building as a case study, the comprehensive performance of three standalone strategies (temperature adjustment, supply water temperature adjustment, and precooling) and their combinations under three typical response durations is compared. The results demonstrate distinct scenario applicability of the three strategies due to their inherent characteristics in regulation capacity, rebound costs, and timeliness. In the 60-minute demand response scenario, the indoor temperature increase strategy offers the strongest peak shaving (approximately 60%), but with a high load rebound rate of up to 85%, making it better suited for subsidy-driven demand response programs. The precool strategy provides moderate peak shaving (approximately 10%). Its key advantage is an extremely low rebound (approximately 0.4%), preserving arbitrage potential under time-of-use pricing and making it better suited to price-driven demand response programs. Featuring moderate rebound and minimal occupant impact, water supply temperature increase can improve system COP to achieve energy savings, making it ideal for efficiency-focused demand response. The integrated strategy does not significantly enhance overall performance, demonstrating equivalent peak shaving capacity to individual strategies but with more pronounced rebound. This study provides quantitative support and strategy optimization methods for flexible regulation of air conditioning loads in high-temperature regions.

Under the emerging trend of the new power systems, enhancing the energy flexibility of air conditioning loads to promote electricity demand response is crucial for regulating the real-time balance. As a typical temperature-controlled loads, air conditioning loads can generate rebound effect when participating in demand response, resulting in sudden load increases and posing risks to grid security. However, the existing research mainly focuses on the energy flexibility, which leads to an imperfect demand response mechanism and thus affects the optimal scheduling strategy. Therefore, the study proposes a comprehensive quantification method in view of rebound effect for the demand response performance of air conditioning loads, by using probability distribution, Latin hypercube sampling, Monte Carlo simulation, and scenario analysis methods. The demand response event was divided into response phase and recovery phase, and by considering energy flexibility during the response phase and rebound effect during the recovery phase, three dimensionless evaluation indexes for comprehensive demand response performance were constructed. Using this quantification method, the impact patterns of three types of random variables were compared, including meteorological, design variables, and control variables. Additionally, considering the differences in building types (office and hotel buildings) and building capacities (small, medium, and large), the effectiveness of air conditioning load participation in demand response measures in different building application scenarios was explored. The results show that the influence of the design variables on the response performance is less than that of the control variables, but significant, reaching 45% compared to the control variables. Moreover, the influence varies with building type, capacity and climate zone, and building demand response design has more potential in the following scenarios: the cold climate, the hot summer and cold winter climate, the medium building and the hotel building.

Given that the passive performance simulation of buildings based on typical meteorological year data and specific design schemes makes it challenging to respond to climate change and refine design requirements on time, this article established a passive performance prediction model for future buildings considering multi-dimensional variables including climate change, building design, and operational characteristics. For high thermal insulation buildings under future climates, the mild climate zone is more sensitive than the others, cooling energy demand is more sensitive than heating demand, apartments are more sensitive than office buildings, and passive survivability is more sensitive than energy performance; for buildings of the same type located in the same climate zone, thermal design solutions determine the increase rate of cooling demand. The potential benefits of climate warming on heating demand reduction are almost zero, but the cooling demand increases significantly, with apartments and office buildings increasing up to 22.1% and 5.0%, respectively. Buildings generally overheat in the future, and the increase rate of the mild zone far exceeds other zones with duration and severity being 3004.8% and 877.7% for apartments, and 884.3% and 288.9% for office buildings, respectively.

Evidence indicates that improvement of thermal performance of building envelope has the potential for aggravating the indoor overheating risk in summer. On the other hand, evolving building standards continue to strengthen the requirements for thermal performance to achieve the energy-saving target. Therefore, this study quantifies the interaction effect between building standards-oriented building design, heating energy demand in winter, and indoor overheating risk in summer. Building databases with different energy efficiency levels are generated using a randomly generated method. Uncertain variables include not only 13 design parameters but also the running state of natural ventilation and external shading. The indoor overheating risk is assessed in terms of severity and duration. Finally, a multi-objective optimization model integrating meta-models and the non-dominated sorting genetic algorithm is proposed to balance heating energy demand in winter and indoor overheating risk in summer. Results indicate that building standards tend to aggravate overheating risk in summer: the duration and severity of high-performance buildings increased by 40.6% and 24.2% than that of conventional-performance buildings. However, window ventilation could offset the adverse effect, and mitigation of duration and severity can be up to 85.2% and 62.1% for high-performance buildings. Window ventilation can weaken the conflict between heating energy demand in winter and overheating risk in summer. As heating energy demand increased from 6.1 to 67.3 kWh/m², the overheating risk changes little that the duration of overheating risk decreased from 17.5% to 15.6% and severity decreased from 8.7 °C to 8.3 °C.