| 引用本文: | 王浩元,别朝红.考虑不确定性物理边界的灵活爬坡备用分布鲁棒经济调度[J].电力自动化设备,2023,43(10):59-68 |
| WANG Haoyuan,BIE Zhaohong.Distributionally robust economic dispatch of flexible ramping reserve considering physical boundaries of uncertainty[J].Electric Power Automation Equipment,2023,43(10):59-68 |
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| 摘要: |
| 面对新能源不确定性的影响，需要充分挖掘电力系统的灵活调节能力。为此，提出了一种基于数据驱动分布鲁棒机会约束的灵活爬坡备用经济调度模型。考虑新能源不确定性功率波动的物理边界，利用Wasserstein距离构建模糊集，从而建立更加准确的不确定性模型。采用联合机会约束控制第二阶段安全越限风险，在保证安全鲁棒性的同时，避免过于保守的决策结果。基于仿射决策规则和条件风险价值理论，将两阶段分布鲁棒问题近似为线性模型，从而实现高效求解。以改进的IEEE 9节点系统为算例验证所提方法的有效性，探究了训练样本量对结果的影响，并将所提方法与鲁棒优化方法和随机优化方法进行对比。 |
| 关键词: 新能源不确定性 灵活调节能力 灵活爬坡服务 分布鲁棒优化 联合机会约束 经济调度 |
| DOI：10.16081/j.epae.202308032 |
| 分类号:TM712 |
| 基金项目:国家自然科学基金资助项目(U22B20103) |
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| Distributionally robust economic dispatch of flexible ramping reserve considering physical boundaries of uncertainty |
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WANG Haoyuan, BIE Zhaohong
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State Key Laboratory of Electrical Insulation and Power Equipment, Institute of Power System and Its Resilience, Xi’an Jiaotong University, Xi’an 710049, China
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| Abstract: |
| Facing the influence of renewable energy uncertainty, it is necessary to fully exploit the flexible adjustment ability of power system. Therefore, an economic dispatch model of flexible ramping reserve based on data-driven distributionally robust chance constraints is proposed. Considering the physical boun-daries of uncertain power fluctuation of renewable energy, the fuzzy set is constructed by using Wasserstein distance, and then a more accurate uncertainty model is established. The joint chance constraints are used to control the security violation risk in the second stage, which avoids overly conservative decision results while ensuring security robustness. Based on the affine decision rules and conditional value at risk theory, the two-stage distributionally robust problem is approximated as a linear model, which can be solved efficiently. An improved IEEE 9-bus system is taken as the example to verify the effectiveness of the proposed method. The influence of training sample size on the results is explored, and the proposed method is compared with the robust optimization method and stochastic optimization method. |
| Key words: renewable energy uncertainty flexible adjustment ability flexible ramping product distributionally robust optimization joint chance constraints economic dispatch |
