Multi-Time-Scale Power and Energy Balance Optimization Method for Power Systems Considering Hydrogen Energy Storage Failures Under Dynamic Carbon Quota Allocation

Abstract

High renewable energy penetration significantly increases source-side uncertainty and makes power and energy balancing more difficult under carbon emission constraints. To improve the multi-time-scale power and energy balance of highly uncertain power systems, this paper proposes a low-carbon coordinated optimization method considering wind–photovoltaic output uncertainty, hydrogen energy storage availability uncertainty, and carbon quota constraints. Wind and photovoltaic uncertainty is described by joint scenarios, while the available capacities of electrolyzers and hydrogen fuel cells are characterized using Markov-chain-based capacity coefficients. On this basis, a multi-time-scale power and energy balance optimization framework is established by combining medium- and long-term stochastic energy task decomposition, day-ahead two-stage stochastic optimization, and intra-day model predictive control rolling correction. The medium- and long-term layer coordinates energy tasks and carbon budgets, the day-ahead layer forms a baseline power and energy balance plan, and the intra-day layer corrects real-time power deviations. In addition, a dynamic carbon quota mechanism related to load level, renewable energy penetration, and hydrogen energy storage system availability is introduced to reflect the impact of operating conditions on low-carbon scheduling. Case-study results show that the proposed method can maintain power and energy balance under renewable fluctuations and hydrogen energy storage availability variations. Compared with a static carbon quota scheme, the dynamic carbon quota mechanism reduces total operating cost by 8.94% and carbon emissions by 8.77%. The intra-day rolling correction reduces the RMSE of residual power deviations to 9.39 kW, indicating that the proposed optimization method can improve multi-time-scale balancing robustness and low-carbon operation capability of highly uncertain power systems.