With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty.
Chu et al. (Fri,) studied this question.