计及风电时间相关性的鲁棒机组组合
Robust Unit Commitment Considering Temporal Correlation of Wind Power
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摘要: 鲁棒优化是解决大规模新能源接入后电力系统调度的重要手段。相比于基于场景的随机规划、带有风险约束的机组组合等,鲁棒机组组合的结果往往偏于保守。鲁棒优化的保守性直接受到不确定集合的影响。研究了风电预测误差时间相关特点,提出了基于自相关性的时间相关性约束。并利用不确定集合的离散性特点,将该约束近似简化为可以用于实际鲁棒优化问题的线性约束。在列与限制生成(C&CG)算法的基础上,改进了Bender’s分解后子问题的求解算法,提出了一种适合离散型不确定集合的鲁棒优化求解方法。最后,以真实风电数据进行了大量的仿真实验。结果表明,提出的算法能够在不影响机组组合可靠性的前提下,降低鲁棒优化保守性。Abstract: Robust optimization（RO）is an important tool to solve dispatch problem for power system with large scale renewable energy.Comparing with stochastic optimization and risk-constrained unit commitment,over-conservativeness is a major shortcoming for RO.The conservativeness of RO is directly infected by uncertainty set.The temporal correlation characteristics of wind farm output are studied and a time-correlation constraint based on Pearson correlation coefficient is established.Then,the constraint is simplified and applied in robust unit commitment（RUC）by using discrete characteristics of uncertainty set.The solution of sub problem in Bender’s decomposition is improved.Based on column and constraint generation algorithm,a new solution for RO with discrete uncertainty set is developed.Numerical experiments are performed with the actual data of wind farms.The results show that the proposed algorithm can improve RUC performance without jeopardize system robustness.
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