风电全消纳下虚拟电厂内部资源鲁棒调度策略

曾鉴, 刘俊勇, 杜新伟, 朱嘉远, 程超, 陶宇轩, 胥威汀

曾鉴, 刘俊勇, 杜新伟, 等. 风电全消纳下虚拟电厂内部资源鲁棒调度策略[J]. 现代电力, 2019, 36(3): 80-87.
引用本文: 曾鉴, 刘俊勇, 杜新伟, 等. 风电全消纳下虚拟电厂内部资源鲁棒调度策略[J]. 现代电力, 2019, 36(3): 80-87.
ZENG Jian, LIU Junyong, DU Xinwei, et al. Robust Scheduling Strategy of the Internal Resources in VPP Based on Wind Power Completely Consumed[J]. Modern Electric Power, 2019, 36(3): 80-87.
Citation: ZENG Jian, LIU Junyong, DU Xinwei, et al. Robust Scheduling Strategy of the Internal Resources in VPP Based on Wind Power Completely Consumed[J]. Modern Electric Power, 2019, 36(3): 80-87.

风电全消纳下虚拟电厂内部资源鲁棒调度策略

基金项目: 国网四川省电力公司科技项目(SGSCJY00JHJS 201700009)
详细信息
    作者简介:

    曾鉴(1970—),男,硕士,工程师,从事电网规划方面的工作和研究,E-mail:scpgwenz@foxmail.com;
    刘俊勇(1963—),男,教授,博士生导师,研究方向为电力系统分析及电力市场;
    杜新伟(1980—),男,博士,高级工程师,从事电网规划方面的工作和研究,E-mail:351479318@qq.com。

  • 中图分类号: TM732

Robust Scheduling Strategy of the Internal Resources in VPP Based on Wind Power Completely Consumed

  • 摘要: 虚拟电厂运行机制为可再生能源以及需求侧分布式电源并网提供了技术支撑。针对含风、火、水以及柔性负荷的虚拟电厂,构建了两阶段鲁棒优化模型。该模型在对风电出力全部消纳的情况下,优化其内部各单元出力,缓解风电的不确定性和随机性,保证虚拟电厂满足对某些负荷稳定供电的情况下,达到运行经济性最优。针对min-max-min型两阶段优化问题,将其分解为混合整数特性的主问题以及计及风电出力不确定性的线性优化子问题。然后引入线性优化强对偶理论与列约束生成算法对主问题与子问题进行迭代求解,与此同时,在迭代过程中采用Big-M法将子问题的对偶模型线性化。最后通过算例仿真验证了所提两阶段鲁棒模型的有效性。
    Abstract: The operation mechanism of the virtual power plant can provide the technical support for the integration of renewable energy and distributed power on the demand side. In this paper, a two-stage robust optimization model is built for virtual power plant (VPP) which includes wind power units, thermal power units, hydro units and flexible loads. Based on wind power completely consumed, the operation plan of each unit is optimized to alleviate the uncertainty and randomness of wind power, and guarantee the optimal operation efficiency of the virtual power plant. To solve the two-stage problem with min-max-min structure, it is decomposed into a main mixed integer problem and a linear optimization sub-problem which considers the uncertainty of wind power output. Then, the linear optimization strong duality theory and the column constraint generation algorithm(C&CG) are introduced to solve the main problem and the sub-problem by iteration. In the iteration process, the dual model of the sub-problem is linearized by the Big-M method. Finally, the effectiveness of the proposed method is verified by experiment results.
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出版历程
  • 收稿日期:  2018-05-09
  • 发布日期:  2019-06-08

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