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A Bilevel Voltage Regulation Operation for Distribution Systems With Self-Operated Microgrids

机译:用于具有自操作微电网的配电系统的双电平电压调节操作

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The emerging of microgrids in distribution systems has significantly enhanced the resilience of power grids. However, the operators of a distribution system and microgrids therein can be different and have accessibility to different devices. To model the operation of such a grid, this paper proposes a bilevel formulation and probes into the voltage regulation operation, considering the interaction between different systems. The proposed bilevel formulation considers the cooperation of active energy resources (AER), transformer tap-changers, and capacitor banks that are controlled by different operators. To facilitate the solution time of the target bilevel optimization, the lower-level problems with different objectives are modeled using deep neural networks (DNNs) which are then converted into a set of constraints. Hence, the bilevel problem can be reformed to a single-level problem. Lastly, the proposed solution procedures are validated using a customized joint system constructed by the IEEE 123-bus system and a real distribution system in Iowa. According to the numerical validation results, the solution time of the proposed nonlinear activation function based DNN model is 69 times faster than other methods in solving voltage regulation with a bilevel structure.
机译:配电系统中微电网的出现显著增强了电网的弹性。然而,配电系统及其微电网的运营商可以不同,并且可以访问不同的设备。为了模拟这种电网的运行,本文提出了一个双电平公式,并考虑了不同系统之间的相互作用,探讨了电压调节操作。所提出的双电平公式考虑了由不同运营商控制的有源能源 (AER)、变压器分接开关和电容器组的合作。为了缩短目标双层次优化的求解时间,使用深度神经网络 (DNN) 对具有不同目标的较低级别问题进行建模,然后将其转换为一组约束。因此,双层问题可以改革为单层问题。最后,使用IEEE 123总线系统构建的定制联合系统和爱荷华州的真实配电系统验证了所提出的解决方案程序。根据数值验证结果,所提出的基于非线性激活函数的DNN模型求解双层结构调压的求解时间比其他方法快69倍。

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