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Data-driven distributionally robust joint planning of distributed energy resources in active distribution network

机译：积极配送网络中分布式能源的数据驱动分布稳健的联合规划

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摘要

With the increasing penetration of distributed energy resources (DERs) in the active distribution network (ADN), how to enable joint planning of DERs under the uncertainty of distributed generations (DGs) has become a challenging problem. This study establishes a two-stage joint planning model considering doubly-fed induction generator, photovoltaics (PVs) with the ancillary services of PV inverter, distributed energy storage systems and different types of controllable loads in the ADN. To address the uncertainties of DGs, a two-stage data-driven distributionally robust planning model is constructed. The proposed model is solved in a 'master and sub-problem' framework by column-and-constraint generation algorithm, where the master problem is to minimise the total cost and find the optimal planning decision under the worst probability distributions, and the sub-problem is to find the worst probability distribution of given uncertain scenarios. Besides, the original mixed-integer non-linear planning problem is converted into a mixed-integer second-order cone programming problem through second-order cone relaxation, Big-M and piecewise linearisation method. The numerical results based on 33-bus system verify the effectiveness of the proposed model.
机译：随着分布式能源资源（DER）在主动分配网络（ADN）中的渗透率越来越多，如何在分布代世代（DGS）的不确定性下能够在分布式的不确定性下进行联合规划已成为一个具有挑战性的问题。本研究建立了一种两级联合规划模型，考虑了双馈诱导发电机，光伏（PVS），具有PV逆变器的辅助服务，分布式能量存储系统和ADN的不同类型可控负载。为了解决DGS的不确定性，构建了两阶段数据驱动的分布稳健规划模型。通过列 - 约束生成算法在“主问题”框架中解决了所提出的模型，其中主问题是最小化总成本并在最概率的概率分布下找到最佳规划决策，以及子问题是找到给定不确定场景的最糟糕的概率分布。此外，原始混合整数非线性规划问题通过二阶锥形弛豫，大M和分段线性方法转换为混合整数二阶锥形编程问题。基于33总线系统的数值结果验证了所提出的模型的有效性。

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