arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1804.03038math.OC
收藏

考虑非高斯风功率不确定性的AGC系统最优控制

Optimal Control of AGC Systems Considering Non-Gaussian Wind Power Uncertainty

Xiaoshuang Chen, Jin Lin, Feng Liu, Yonghua Song

摘要:

风电功率的不确定性对自动发电控制(AGC)系统提出了重大挑战。在控制器设计中明确考虑风电功率的不确定性分布,可以提高控制性能。然而,广泛接受的风的不确定性通常遵循非高斯分布,这可能会导致复杂的随机AGC建模和高计算负担。针对这一问题,提出了一种基于伊藤理论的AGC系统随机控制问题(SCP)模型,将考虑非高斯风电功率不确定性的优化计算量降低到与确定性控制问题相同的规模。提出了一种精确描述非高斯风电功率不确定性的Ito过程模型,并基于随机评估函数的概念提出了一种SCP。基于SAF的收敛级数展开,SCP被重新表示为在非高斯风功率不确定性下不牺牲性能的确定性控制问题。证明了重新表达的控制问题是一个可以有效求解的凸优化问题。最后通过实例验证了该方法的有效性和准确性。

英文摘要:

Wind power uncertainty poses significant challenges for automatic generation control (AGC) systems. It can enhance control performances to explicitly consider wind power uncertainty distributions within controller design. However, widely accepted wind uncertainties usually follow non-Gaussian distributions which may lead to complicated stochastic AGC modeling and high computational burdens. To overcome the issue, this paper presents a novel Ito-theory-based model for the stochastic control problem (SCP) of AGC systems, which reduces the computational burden of optimization considering non-Gaussian wind power uncertainty to the same scale as that for deterministic control problems. We present an Ito process model to exactly describe non-Gaussian wind power uncertainty, and then propose an SCP based on the concept of stochastic assessment functions (SAFs). Based on a convergent series expansion of the SAF, the SCP is reformulated as a certain deterministic control problem without sacrificing performance under non-Gaussian wind power uncertainty. The reformulated control problem is proven as a convex optimization which can be solved efficiently. A case study demonstrates the efficiency and accuracy of the proposed approach compared with several conventional approaches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑