PV driven air conditioning (PVAC) systems integrated with building integrated photovoltaics (BIPV) are critical for achieving nearly zero energy buildings (NZEBs). Building design parameters simultaneously affect both PV generation and building load demand, yet the complex interactions among these parameters and their collective impact on dynamic energy matching are not well understood. This oversight frequently leads to suboptimal system performance. This study proposes a dynamic energy matching evaluation and optimization framework for PVAC-BIPV systems, incorporating physical models of their energy interactions and employing a systematic, multi-parameter approach to optimize building design parameters in different climatic regions. The maximum relative error of the simulation model for buildings integrated with various BIPV types is 9.80%. Univariate analysis reveals a clear hierarchy of influence, identifying shape factor as the most dominant parameter, followed by window-to-wall ratio (WWR) and then orientation. More importantly, the interaction between shape factor and WWR was identified as the most critical synergistic pairing, governing both available PV area and cooling demand. Through individual adjustment, synergistic combinations, and full-parameter optimization, the system achieved maximum SS improvements of 33.58% in Guangzhou and 24.21% in Shanghai, yielding ultimate SS values of 76.26% and 83.84%, respectively. The final designs for each region are characterized by their optimal parameter sets (45°, 0.555, 0.2) in Shanghai and (60°, 0.555, 0.2) in Guangzhou. The framework has great significance for the climate-adaptive design of PVAC integrated with BIPV types to achieve nearly ZEBs targets.

Proper co-optimization of photovoltaic driven air conditioning (PVAC) systems with load flexibility and batteries is pivotal for achieving zero energy buildings (ZEBs). However, practical implementation faces challenges from separate optimization with conflicting objectives, neglect of spatial-temporal occupancy features, and limited consideration of energy, economic, and environmental performance. This study proposes a hierarchical multi-objective co-optimization framework for capacity design and control strategy of the PVAC coupling systems, with the two optimization layers sharing the same multi-objective function. The optimization method balances energy, economic, environmental performance by key metrics including thermal comfort satisfaction ratio (TCSR), grid cumulative action power (GP_total), net present value (NPV) and emission reduction (ER). The optimal capacity optimization of PV and batteries for PVAC systems was solved by the NSGA-II and TOPSIS algorithms. Based on the case study of a multi-functional academic building, the optimization results of the off-grid system and the grid-connected system were calculated under different configuration of PV and battery capacity, and the relationship between the indicators was discussed. The optimization method of off-grid PVAC systems achieves 24 % reduction in PV capacity while maintaining 85.85 % TCSR, 123,800 CNY of NPV, and 167.26 tons of ER. Grid-connected systems with 165.88 kW PV capacity and 71.26 kWh battery capacity can achieve 100 % of TCSR, 1861.8 kW of GP_total, 148,300 CNY of NPV, and 174.70 tons of ER. The study provides an innovative and practical method for capacity design and energy control of PVAC coupling systems to achieve zero energy buildings.