重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (7): 256-264.

• 电气·电子 • 上一篇    下一篇

考虑需求灵活性的含风电配电网调度优化

苏 俊,陈政宇,刘涵涵,丁 宁   

  1. (1.厦门理工学院 电气工程与自动化学院,福建 厦门 361024; 2.西南石油大学 电气信息学院,成都 610500)
  • 出版日期:2023-08-15 发布日期:2023-08-15
  • 作者简介:苏俊,男,博士,讲师,主要从事电力系统低碳优化运行、新能源并网技术等方面研究,Email:junsu@xmut. edu.cn。

Scheduling optimization of distribution network with wind power considering demand flexibility

  • Online:2023-08-15 Published:2023-08-15

摘要: 风电供电有较强的不稳定性和随机性,为经典配电网调度运行体系带来了极大挑 战。为了弥补现有研究中忽视不确定集外风险、无法充分考虑需求灵活性的不足,在客观量化 风电不确定集外期望风险的基础上,建立基于需求灵活性的含风电配电网调度优化模型。为求 解所建复杂混合整数非线性规划模型,基于协调分解思路,提出了一种改进蚁狮算法与分支定 界算法相协调的双层优化算法,充分利用人工智能算法和经典数学算法的优势,快速求解所建 复杂模型。算例结果表明:与现有先进调度方法相比,所提出方法能充分利用需求灵活性,有效 降低配电网调度的风险,合理权衡调度计划的经济性和风险性;在实际配电网调度中,可为实现 更加高效、稳定的含风电配电网调度提供指导。

关键词: 配电网, 需求灵活性, 风险度量, 蚁狮算法, 双层优化

Abstract: The inherent strong instability and randomness of wind power supply pose a great challenge to the classic distribution network scheduling and operation system. In order to deal with the deficiency of the existing studies that ignore uncertain out-of-set risks and fail to fully consider demand flexibility, this paper establishes a demand flexibility-based distribution grid scheduling optimization model with wind power based on the objective quantification of the uncertain out-of-set expected risk of wind power. In order to solve the complex mixed integer nonlinear programming model, this paper proposes a two-layer optimization algorithm based on the coordination of the improved antlion algorithm with the branch-and-bound algorithm, and makes full use of the advantages of artificial intelligence algorithms and classic mathematical algorithms to solve the complex model rapidly. The case study results show that, compared with the existing advanced scheduling methods, the proposed method can make full use of demand flexibility, effectively reduce the risk of distribution network scheduling, and reasonably weigh the economy and risks of the scheduling plan. In the actual distribution network scheduling, this research can provide important guidance for realizing more efficient and stable distribution network scheduling with wind power.

中图分类号: 

  • TM731