Carbon-Aware Stochastic Scheduling of Power Systems with High-Penetration Renewable Energy and Multi-Timescale Energy Storage

This paper presents a carbon-aware scheduling method for power systems with high-penetration renewable energy (RE). To deal with the uncertainty and volatility of RE generation, the multi-timescale hybrid energy storage system is analytically modeled and scheduled. On the one hand, the battery energy storage stations are utilized to handle the short-term randomness. On the other hand, the hydrogen energy storage and pumped storage hydro plants are synergistically operated for attaining extra flexibility under a long-run perspective. Moreover, a scenario-based two-stage stochastic programming model is formulated to capture the uncertainties, which aims to minimize the expected cost of RE-based power systems subject to multi-timescale operational constraints. Besides, the post-assessment of carbon emission flows (CEF) is implemented to calculate the carbon intensity of storage-integrated nodes for quantifying their carbon mitigation contributions. Comparative Case studies on a modified 30-bus power system validate the effectiveness of the proposed method, highlighting the economic and carbon reduction benefits of multi-timescale storage coordination.