Cross-Day Optimal Scheduling of Photovoltaic-Concentrated Solar Power Integrated Energy Systems Considering Weather Variation Probability

• • Beta distribution shape parameters for four weather types are empirically fitted as 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); these parameters directly govern the confidence interval width used for TES reserve sizing, with cloudy conditions exhibiting the highest relative uncertainty (192.10%) and coefficient of variation (0.5934), mandating wider probabilistic reserve margins than deterministic scheduling would allocate. • • Confidence interval sensitivity analysis quantifies relative uncertainty at 76.96% for clear-sky sparse-cloud, 192.10% for cloudy, 132.22% for overcast, and 184.71% for rain-snow, with corresponding variances of 0.0197, 0.0880, 0.0270, and 0.0350; these metrics establish that cloudy and rain-snow regimes require the most conservative TES reservation, directly impacting capital allocation for storage capacity and operational risk exposure. • • The 72 h–24 h bi-level PSO architecture treats the first 48 h as deterministic and the final 24 h as probabilistic interval data, yielding a TES energy reserve interval that is subsequently resolved into a minimum expected cost dispatch; this decomposition avoids the computational intractability of full-horizon stochastic rolling optimization while capturing cross-day state-variable coupling that day-ahead and intra-day scheduling cannot address. • • The proposed strategy achieves coordinated optimization of long-timescale TES energy configuration and dispatch cost across multiple uncertainty scenarios, outperforming conventional scheduling by avoiding both excessive TES reservation (which inflates storage cost) and insufficient reservation (which incurs loss-of-load shutdown risk); the minimum expected cost corresponds to the system optimal irradiance operating point, providing a quantifiable decision threshold for cross-day dispatch.