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Stochastic Optimal Dispatch of Virtual Power Plant considering Correlation of Distributed Generations

机译：考虑分布式发电相关性的虚拟电厂随机最优调度

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摘要

Virtual power plant (VPP) is an aggregation of multiple distributed generations, energy storage, and controllable loads. Affected by natural conditions, the uncontrollable distributed generations within VPP, such as wind and photovoltaic generations, are extremely random and relative. Considering the randomness and its correlation of uncontrollable distributed generations, this paper constructs the chance constraints stochastic optimal dispatch of VPP including stochastic variables and its random correlation. The probability distributions of independent wind and photovoltaic generations are described by empirical distribution functions, and their joint probability density model is established by Frank-copula function. And then, sample average approximation (SAA) is applied to convert the chance constrained stochastic optimization model into a deterministic optimization model. Simulation cases are calculated based on the AIMMS. Simulation results of this paper mathematic model are compared with the results of deterministic optimization model without stochastic variables and stochastic optimization considering stochastic variables but not random correlation. Furthermore, this paper analyzes how SAA sampling frequency and the confidence level influence the results of stochastic optimization. The numerical example results show the effectiveness of the stochastic optimal dispatch of VPP considering the randomness and its correlations of distributed generations.
机译：虚拟电厂（VPP）是多个分布式发电，能源存储和可控负载的集合。受自然条件的影响，VPP中不可控制的分布式发电，例如风力发电和光伏发电，是极其随机和相对的。考虑到不可控制的分布式发电的随机性及其相关性，本文构造了包含随机变量及其随机相关性的VPP机会约束随机最优调度。通过经验分布函数描述了独立风能和光伏发电的概率分布，并通过Frank-copula函数建立了联合概率密度模型。然后，应用样本平均近似（SAA）将机会约束随机优化模型转换为确定性优化模型。仿真案例是基于AIMMS计算的。将本文数学模型的仿真结果与没有随机变量的确定性优化模型的结果进行比较，并且将随机变量而不是随机相关性考虑为随机优化。此外，本文分析了SAA采样频率和置信度如何影响随机优化的结果。数值算例结果表明，考虑分布代数的随机性及其相关性，VPP随机最优调度的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|135673.1-135673.8|共8页
  • 作者

    Yu Jie; Feng Qizhi; Li Yang; Cao Jinde;

  • 作者单位

    Southeast Univ, Sch Elect Engn, Nanjing 210096, Jiangsu, Peoples R China.;

    Southeast Univ, Sch Elect Engn, Nanjing 210096, Jiangsu, Peoples R China.;

    Southeast Univ, Sch Elect Engn, Nanjing 210096, Jiangsu, Peoples R China.;

    King Abdulaziz Univ, Dept Math, Fac Sci, Jeddah 21589, Saudi Arabia.;

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