Energy storage systems (ESSs) can improve energy management in distribution grids, especially with the increasing penetration of home energy management systems (HEMSs) that schedule household appliances and render them as smart loads. A large number of uncoordinated HEMSs can result in significant changes to the aggregated load profile of the distribution system. This paper proposes a framework and mathematical model for integrating ESS in the distribution grid to minimize the operation cost of the local distribution company (LDC) and alleviate the impact of uncoordinated HEMS operation on the distribution grid. A novel neural network (NN) based state of health (SoH) estimator for a lithium-ion (Li-ion) battery based ESS is proposed, which is incorporated within the LDC's planning problem. The results show that the proposed estimation model is an accurate estimation of the SoH of the ESS. The LDC's planning decisions are also compared, considering SoH of the ESS vis-á-vis linear degradation and no-degradation models.

中文翻译：

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将电池SoH估算纳入具有储能系统的智能配电计划中

储能系统（ESS）可以改善配电网中的能源管理，尤其是随着家庭能源管理系统（HEMS）的普及，该系统可以调度家用电器并将其作为智能负载。大量不协调的HEMS可能会导致配电系统的总负荷曲线发生重大变化。本文提出了将ESS集成到配电网中的框架和数学模型，以最小化本地配电公司（LDC）的运营成本，并减轻不协调的HEMS运营对配电网的影响。针对基于锂离子（Li-ion）电池的ESS，提出了一种基于神经网络（NH）的健康状态估计器，该方法被纳入了LDC的计划问题中。结果表明，所提出的估计模型是对ESS的SoH的准确估计。还考虑了ESS相对于线性退化和无退化模型的SoH，比较了LDC的计划决策。