Large-scale integration of renewable energy sources in power system leads to the replacement of conventional power plants (CPPs) and consequently challenges in power system reliability and security are introduced. This study is focused on improving the grid frequency response after a contingency event in the power system with a high penetration of wind power. An energy storage system (ESS) might be a viable solution for providing inertial response and primary frequency regulation. A methodology has been presented here for the sizing of the ESS in terms of required power and energy. It describes the contribution of the ESS to the grid, in terms of inertial constant and droop. The methodology is applied to a 12-bus grid model with high wind power penetration. The estimated ESS size for inertial response and primary frequency regulation services are validated through real-time simulations. Moreover, it is demonstrated that the ESS can provide the response similar to that provided by the CPPs.

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is developing rapidly. To ensure the battery safe usage and to reduce its average lifecycle cost, an accurate State of Charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper reviews recently proposed online SOC estimation methods and classifies them into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

Because of their characteristics, which have been continuously improved during the last years, Lithium-ion batteries have been proposed as an alternative viable solution to present fast-reacting conventional generating units to deliver the primary frequency regulation service. However, even though there are worldwide demonstration projects, where energy storage systems based on Lithium-ion batteries are evaluated for such applications, the field experience is still very limited. In consequence, at present, there are no very clear requirements on how the Lithium-ion battery energy storage systems should be operated, while providing frequency regulation service and how the system has to reestablish its state of charge (SOC) once the frequency event has passed. Therefore, this paper aims to investigate the effect on the lifetime of the Lithium-ion batteries energy storage system of various strategies for reestablishing the batteries' SOC after the primary frequency regulation is successfully delivered.