Research on flexibility-enhanced planning for renewable energy systems with supply-demand uncertainties - Tianhe Sun; Xilai Bai; Xiaoyi Qian; Baoshi Wang (PLoS ONE, 2025)
Due to the rapid development of intermittent renewables and emergence of new types of load, flexibility becomes a crucial element for reliable and cost-effective power system operation. This paper proposes a dual-index flexibility evaluation metric (incorporating flexibility margin and insufficiency probability) that considers the dynamic supply-demand balance under net load uncertainty. Additionally, a robust two-stage power planning model is presented to enhance system flexibility, utilizing the proposed metric. The model is solved iteratively using the column generation algorithm and strong duality theory. Case studies on a Northeast China power grid demonstrate that, by optimally configuring generation and storage capacity guided by flexibility and other indicators, the proposed method reduces curtailment/load shedding costs and system flexibility insufficiency probability by 45% and 4.3% respectively. Furthermore, incorporating energy storage planning achieves additional significant reductions of 27% and 1.1% in these metrics, verifying its effectiveness. Comparative analysis confirms the superiority of the proposed robust-probabilistic hybrid model over traditional uncertainty quantification methods in balancing computational efficiency, risk control, and curtailment reduction.