Cross-Day Optimal Scheduling of Photovoltaic-Concentrated Solar Power Integrated Energy Systems Considering Weather Variation Probability
Cross-day scheduling of photovoltaic-concentrated solar power (PV-CSP) integrated energy systems is constrained by low solar irradiance forecast accuracy and high data volatility over long time horizons. This study proposes a bi-level particle swarm optimization (PSO) scheduling method predicated on a weather probability model to secure system stability and economic performance. A Beta distribution model is constructed to characterize solar irradiance uncertainty, with shape parameters fitted for four weather types: clear-sky sparse-cloud (α=6.5, β=2.3), cloudy (α=β=0.92), overcast (α=3.8, β=4.4), and rain-snow (α=1.5, β=3.5). A 72 h–24 h bi-level PSO architecture is designed: the upper level reserves thermal energy storage (TES) capacity to hedge against weather uncertainty, while the lower level performs economic dispatch based on weather transition probabilities. Confidence interval sensitivity analysis reveals relative uncertainties of 76.96% (clear-sky sparse-cloud), 192.10% (cloudy), 132.22% (overcast), and 184.71% (rain-snow), with coefficients of variation ranging from 0.1901 to 0.6236. Case studies demonstrate that the synergistic mechanism between the probability model and bi-level optimization identifies the optimal irradiance operating point across weather types. Compared with conventional deterministic scheduling, the proposed strategy achieves coordinated optimization of long-timescale TES energy allocation and dispatch cost under multiple uncertainty scenarios, mitigating both over-conservative storage reservation and loss-of-load risk.