The increasing penetration of distributed energy resources (DERs) may cause security and economic risks in the distributed network. In this paper, the optimal allocation of battery energy storage systems (BESS) is proposed to mitigate the risks in the radial distribution network by considering future uncertainties, such as the uncertainties of load and renewable energy. The interacting levels of the proposed hierarchical planning framework are (1) determination of the BESS location based on the calculated adjusted voltage violation risk; (2) obtaining the capacity of the BESS by solving an optimization problem assisted by supervised learning. In the previous works, the steady-state is evaluated by the DistFlow equations in the distribution system. In our paper, we have utilized a data-driven method to calculate the power flow and the voltage, thus leading to higher accuracy. Through case studies, the effectiveness of the proposed method is verified. Furthermore, the data-driven assisted optimization model reduces the computational burden to a large extent because massive state variables, the power flow constraints and voltage constraints are substituted.

中文翻译：

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数据驱动的电池储能系统分层优化配置

分布式能源（DER）的日益普及可能会导致分布式网络的安全和经济风险。在本文中，通过考虑未来的不确定性，如负载和可再生能源的不确定性，提出了电池储能系统（BESS）的优化配置，以减轻径向配电网络中的风险。所提出的分层规划框架的交互级别是（1）根据计算出的调整后的电压违规风险确定 BESS 位置；(2) 通过求解由监督学习辅助的优化问题来获得 BESS 的容量。在以前的工作中，稳态是通过分配系统中的 DistFlow 方程来评估的。在我们的论文中，我们利用数据驱动的方法来计算潮流和电压，从而导致更高的准确性。通过案例研究，验证了所提出方法的有效性。此外，数据驱动的辅助优化模型在很大程度上减少了计算负担，因为大量的状态变量、潮流约束和电压约束被替代。