With the advancement of energy transition, residential photovoltaic (PV) systems face intermittency challenges that impact grid stability. While battery integration enhances resilience, existing approaches exhibit critical gaps: (1) underdeveloped hybrid modeling frameworks balancing physical interpretability and data-driven accuracy; (2) reinforcement learning (RL) strategies prioritizing economic gains over grid stability, risking localized fluctuations; and (3) performance evaluations lacking systematic assessment across varying PV-battery capacities. To bridge these gaps, this study proposes a hybrid framework combining physical energy flow constraints with XGBoost-based machine learning for robust forecasting. Two optimization strategies, proximal policy optimization (PPO) and rule-based control (RBC), are developed for charge-discharge scheduling, explicitly incorporating grid stability metrics. Multi-scenario analysis evaluates performance under varying capacities and initial states of charge (SOC). Results demonstrate the hybrid model’s superiority over physics-based benchmarks, significantly improving prediction accuracy, with R² increasing from 0.70 to 0.95 for SOC and from 0.83 to 0.98 for grid power. Both PPO and RBC enhance efficiency and stability versus baseline: the energy self-sufficiency rate rises from 10.6% to 79.3% (PPO) and 82.4% (RBC), while grid power fluctuations decrease from 2.6 kWh to 1.66 kWh (PPO) and 1.38 kWh (RBC). Crucially, RBC achieves higher stability and interpretability near boundaries, whereas PPO excels in long-term optimization but exhibits boundary-condition sensitivity. Results further reveal that PV-battery capacity and initial SOC influence strategy performance. This study establishes a structured technical pathway encompassing hybrid forecasting model development, stability-oriented optimization design, and scenario-based performance evaluation, providing an integrated solution to enhance grid resilience and energy autonomy in residential PV-battery systems.

The morphology of urban areas plays a crucial role in determining solar potential, which directly affects photovoltaic capacity and the achievement of net-zero outcomes. This study focuses on the City of Melbourne to investigate the utilization of solar energy across different urban densities and proposes optimized morphologies. The analysis encompasses blocks with diverse population densities, examining medium and high-density areas. By utilizing a multi-objective genetic optimization approach, the urban morphology of these blocks is refined. The findings indicate that low-density blocks exhibit photovoltaic potential ranging from 1 to 6.6 times their total energy consumption. Medium and high-density blocks achieve photovoltaic potential levels approximately equivalent to 40%–85% of their overall energy consumption. Moreover, significant variations in photovoltaic potential are observed among different urban forms within medium and high-density blocks. An “elevated corners with central valley” prototype is proposed as an effective approach, enhancing the overall photovoltaic potential by approximately 14%. This study introduces novel analytical concepts, shedding light on the intricate relationship between urban morphologies and photovoltaic potential.