| 亓晓燕,李靖,秦子健.基于鲁棒优化和CVaR的新能源电网两阶段风险调度方法[J].电力需求侧管理,2026,28(4):37-44 |
| 基于鲁棒优化和CVaR的新能源电网两阶段风险调度方法 |
| Two-stage risk dispatch method for renewable energy power systems based on robust optimization and CVaR |
| 投稿时间：2026-02-25 修订日期：2026-04-28 |
| DOI：10.3969/j.issn.1009-1831.2026.04.006 |
| 中文关键词: 新能源电网 电解槽多物理场 条件风险价值 两阶段风险鲁棒 |
| 英文关键词: renewable power system multi-physics aware electrolyzer model conditional value at risk(CVaR) two-stage risk-robust |
| 基金项目:国网山东省电力公司科技项目（520612250001） |
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| 中文摘要: |
| 为应对规模化新能源接入对电氢耦合系统运行的影响，解决传统两阶段鲁棒优化在不确定集选择上的主观性及新能源概率预测信息利用不足的问题，提出了面向新能源电网的两阶段风险鲁棒调度方法。首先，建立整合动态制氢-储氢-用氢一体化的新能源电网系统模型，并提出耦合热力学和气泡动力学的电解槽多物理场模型，精细刻画电解槽动态特性，提升电解效率随环境、运行状态等的表征精度；然后，构建了结合鲁棒优化与条件风险价值（conditional value at risk，CVaR）的新能源电网两阶段风险鲁棒发电-备用协调调度方法，第一阶段依据新能源预测确定储能、可控机组及制氢系统功率基点与备用容量，量化弃风及切负荷风险，第二阶段基于新能源实际功率与预留备用再调度，满足可消纳区间内所有场景需求；最后，采用列约束生成（column-and-con- straint generation，C&CG）算法求解优化模型，通过两阶段模型协同求解确定最优不确定集。算例分析验证了所提模型的有效性。 |
| 英文摘要: |
| To address the impact of large-scale renewable energy integration on the operation of electro-hydrogen coupling systems, and to resolve the subjectivity in uncertainty set selection and insufficient utilization of renewable energy probability information in traditional twostage robust optimization, a two-stage risk-robust scheduling method tailored for renewable power systems is proposed. First, an integrated model for renewable power system that encompasses dynamic hydrogen production, storage, and utilization is established. A multi-physics aware electrolyzer model that couples the thermodynamics and bubble dynamics is proposed to precisely characterize the dynamic variation of electrolytic efficiency. Second, a two-stage risk-robust generation-reserve coordination scheduling method for the renewable power system is developed based on robust optimization and conditional value at risk（CVaR）. In the first stage, the output baselines and reserve capacities for energy storage, controllable units, and hydrogen production systems are determined based on renewable energy forecasts, and wind curtailment and load shedding risks are quantified. In the second stage, operations are rescheduled based on actual renewable energy output and reserved reserves to meet all scenario demands within the acceptable range. Finally, the column-and-constraint generation（C&CG）algorithm is adopted to solve the optimization model, and the optimal uncertainty set is determined through collaborative two-stage solution. Case studies validate the effectiveness of the proposed model. |
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