论文标题
通过战略性的可再生能力削减和决策依赖性不确定性，强大的生成派遣
Robust Generation Dispatch with Strategic Renewable Power Curtailment and Decision-Dependent Uncertainty
论文作者
论文摘要
随着可再生能源取代传统的电源（例如热发电机），不确定性会增长，而可控单元较少。为了降低操作风险并避免频繁的实时应急控制，需要削减可再生生成的准备时间表。本文提出了一种新颖的两阶段鲁棒生成调度（RGD）模型，其中预削减时间表在截止阶段进行了优化。然后，减少时间表将影响实时可再生能力输出的变化范围，从而导致决策依赖性不确定性（DDU）集。在重新划分阶段，可控单元在储备容量内调整其输出，以保持电源平衡。为了克服用DDU求解RGD的困难，开发了自适应柱和约束生成（AC \＆CG）算法。我们证明所提出的算法可以在有限迭代中生成最佳解决方案。数值示例显示了所提出的模型和算法的优势，并验证了它们的实用性和可扩展性。
As renewable energy sources replace traditional power sources (such as thermal generators), uncertainty grows while there are fewer controllable units. To reduce operational risks and avoid frequent real-time emergency controls, a preparatory schedule of renewable generation curtailment is required. This paper proposes a novel two-stage robust generation dispatch (RGD) model, where the preparatory curtailment schedule is optimized in the pre-dispatch stage. The curtailment schedule will then influence the variation range of real-time renewable power output, resulting in a decision-dependent uncertainty (DDU) set. In the re-dispatch stage, the controllable units adjust their outputs within the reserve capacities to maintain power balancing. To overcome the difficulty in solving the RGD with DDU, an adaptive column-and-constraint generation (AC\&CG) algorithm is developed. We prove that the proposed algorithm can generate the optimal solution in finite iterations. Numerical examples show the advantages of the proposed model and algorithm, and validate their practicability and scalability.
