Hydrogen storage capacity planning and system balance analysis based on multi time scale adjustment of demand probability distribution.

As the penetration rate of renewable energy continues to rise, power systems face the dual challenges of heightened uncertainty in wind and solar output and cross-seasonal energy imbalances. Short-term energy storage struggles to meet long-term regulation demands, prompting widespread interest in hydrogen as a potential long-duration storage solution. However, challenges remain in addressing the coupled relationship between multi-timescale regulation requirements in hydrogen planning and the insufficient system robustness under deterministic methodologies. To address this, this paper first constructs a Gaussian mixed Copula probabilistic model accounting for wind and solar output correlations. Opportunity constraints are employed to define the feasible range of reserve capacity meeting wind and solar output regulation requirements. Subsequently, a multi-timescale stochastic optimization planning framework is proposed, integrating ‘monthly capacity allocation with hourly sequential operation’. This leverages the cross-seasonal energy storage characteristics of hydrogen systems to achieve power and energy balance. Simulation results demonstrate that the proposed method reduces total costs by 1.55% compared to traditional deterministic planning, achieving a system balancing probability of 99.21%. This represents improvements of 0.08% and 0.40% over single-stage planning and conventional energy storage planning respectively, validating its effectiveness in enhancing operational reliability. Sensitivity analysis further indicates that reduced hydrogen storage costs and enhanced hydrogen production efficiency contribute to higher system balancing probabilities. Beyond a 95% confidence level for wind and solar resources, the rate of improvement in balancing probability slows. wind-dominated systems exhibit a 0.26% higher balance probability than Photovoltaic -dominated systems. These findings provide crucial technical guidance for stochastic planning in high-penetration renewable electricity systems.