With rapid deployments of photovoltaic (PV) systems, imbalances between energy supply and demand become increasingly pronounced. Air conditioning is a major consumer of electricity and a key energy flexible resource to improve PV onsite consumption. This study developed and evaluated four easy-to-deploy indoor temperature reset strategies for air-conditioning systems, including a time-of-use strategy and three adaptive strategies, based on a building simulation platform of a typical office building in Guangzhou. Results showed that the adaptive indoor temperature setpoint reset strategies effectively alleviated mismatches between PV power and electric load of air-conditioners. The adaptive strategies increased PV self-consumption and self-sufficiency by 5.4%–14.3% and 14.7%–17.7%, respectively, compared to a fixed-setpoint baseline case. The building envelope thermal mass provided inherent storage capacity, allowing load shifting by lowering temperature setpoints in PV-surplus periods and increasing setpoints in PV-deficit periods without compromising thermal comfort. Detailed energy flow analysis of the external wall demonstrated that the adaptive control strategies improved the efficiency of energy storage and release in a range of 5.6%–55.6%, as compared to the baseline. Incorporating fan speed regulation extended the feasible range of temperature setpoint reset, thereby enabling a more effective balance between grid independence and thermal comfort. The economic assessment showed 28.8%–30.7% annual reductions in operational expenditures under the adaptive control strategies. The shortest payback period for the PV-driven air-conditioning system was 5.4 years. The proposed control strategies provide an easy-to-deploy approach to promote solar energy utilization in buildings and to further reduce urban building carbon emissions.

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.

Urban building energy modeling (UBEM) plays a crucial role in analyzing building energy use and has shown that large-scale UBEM can drive energy efficiency and sustainable development through urban retrofitting. However, large-scale UBEM presents challenges, including data acquisition workload, frequent parameter adjustments, and long simulation times. Moreover, the workflow connection between UBEM and urban retrofitting pathways remains unclear. Thus, this study proposes a framework that combines a fast, large-scale UBEM method in a Python environment with renewable energy integration to create energy demand-supply networks. The proposed UBEM method utilizes R-tree for geometric repairs, while EPPY efficiently batch-sets simulation parameters based on building function and performs batch simulations with EnergyPlus to quantify energy demand. Energy demand-supply networks are constructed through an improved gravitational model that considers location and functional mix, along with social network analysis. The framework was applied to Nanjing's historic city center in Jiangsu, China, covering 23,279 buildings across 551 blocks with six functional categories, totaling 54,232,464 m² of building area. The energy use map reveals that high energy use intensity blocks (over 175 kWh/(m²·year)) are distributed in the southern, particularly in commercial and old residential areas, while educational blocks have the highest photovoltaic (PV) potential. The simulation time using the multi-threaded EPPY method was only 14.1% of that with the conventional Ladybug tool for 75 buildings, and about 46.2% for ten urban blocks. Even with PV potential considered, 84.2% of blocks have energy demand exceeding supply, necessitating additional retrofitting. Combined retrofits are more effective than individual retrofits, achieving up to a 16.7% energy savings. This study provides new insights into large-scale UBEM and offers valuable decision-making support for energy-efficient urban retrofitting.

The increasing integration of renewable energy sources highlights the urgent need for grid flexibility, with buildings serving as key controllable loads. In this context, accurately quantifying building flexibility is essential for enabling effective demand-side management and ensuring reliable grid operations. However, several challenges hinder this quantification. To address these issues, this study proposes a comprehensive flexibility quantification framework. First, a novel RC-Mapping model incorporating an Enumerate-Comparison Method is proposed. The RC-Mapping model can capture the thermal behavior of both the building and the air conditioning system, while the Enumerate-Comparison Method can initialize state parameters in the RC-Mapping model. Compared with the conventional approach, as validated by the experiment, the proposed method can substantially improve RMSE for indoor temperature prediction from 0.542 ℃ to 0.266 ℃, and the MAPE for flexibility quantification from 27.58% to 10.98%. Second, the study introduces the power reduction-duration curve and temperature variation curves to characterize flexibility from both grid and building perspectives. Specifically, based on the analysis of the power reduction-duration curve, this study provides a systematic analysis of four sources of flexibility and their underlying mechanisms, including the thermal storage of the building, the thermal storage of the HVAC system, the increase of coefficient of performance (COP), and the reduction in cooling load. Finally, the study investigates the impact of uncertainties in COP and internal heat gains on flexibility quantification. According to the result, it is recommended to slightly underestimate the COP and overestimate the internal heat gain schedule to improve the accuracy of flexibility quantification.

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.

Typical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model uncertainty. To solve it, this study proposes a decision support framework that integrates a hybrid urban building energy model (UBEM) method, NSGA-Ⅱ, and TOPSIS to obtain rapidly the optimal energy-efficient retrofit solutions that take into account model uncertainty. The study took the building groups in Sipailou campus as a case study and identified 76 “stable solutions” and 149 “active solutions” that minimize energy consumption, carbon emission, and life-cycle cost (LCC) over 30 years from 40,353,607 retrofit schemes. Key findings include that when considering model uncertainty, the quantities, types, and ranks of optimal retrofit solutions have changed. When the error of baseline UBEM validation is within ±5% and considering uncertainty transmission from energy simulation to ANN model, the energy-saving potential of optimal retrofit schemes has expanded from [63.78, 65.05]% to [60, 68.75]%, carbon-saving potential has shifted from [63.69, 64.09]% to [59.92, 67.79]%, and the LCC has changed from [–40.68, 14.59] × 10⁶ to [–38.25, 16.97] × 10⁶ Yuan. This study provides decision makers with a scientific approach to consider the potential uncertainties and risks associated with optimal retrofit solutions.

The increasing applications of net-zero energy buildings (NZEBs) will lead to more frequent and larger energy interactions with the connected power grid, thereby being able to result in severe grid overvoltage risks. Control optimization has been proven effective to reduce such risks. Existing controls have oversimplified the overvoltage quantification by simply using the aggregated power exchanges to represent the connected grid overvoltages. Ignoring the complex voltage influences among the grid nodes, such oversimplification can easily result in low-accuracy impact evaluations of the NZEB-grid energy interactions, thereby causing non-optimal/unsatisfying overvoltage mitigations. Therefore, this study proposes a novel coordinated control method in which a power-distribution-network model has been adopted for more accurate overvoltage quantification. Meanwhile, the battery operations of individual NZEBs are iteratively coordinated using a sequential optimization approach for achieving the global optimum with substantially reduced computation complexity. For verifications, the proposed coordinated control has been systematically compared with an uncoordinated control and a conventional coordinated control in grid overvoltage minimization. The study results show that the overvoltage improvements can reach 23.5% and 12.3% compared with the uncoordinated control and the conventional coordinated control, respectively. The reasons behind the improvements have also been analyzed in detail. The proposed coordinated control can be used in practice to improve NZEB-clusters' grid friendliness.