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.

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16Given the increasing penetration of renewable energy technologies as distributed generation 17 embedded in the consumption centres, there is growing interest in energy storage systems 18 located very close to consumers. These systems allow to increase the amount of renewable 19 energy generation consumed locally, they provide opportunities for demand-side 20 management and help to decarbonise the electricity, heating and transport sectors. 21In this paper, the authors present an interdisciplinary review of community energy storage 22 (CES) with a focus on its potential role and challenges as a key element within the wider 23 energy system. The discussion includes: the whole spectrum of applications and 24 technologies with a strong emphasis on end user applications; techno-economic, 25 environmental and social assessments of CES; and an outlook on CES from the customer, 26 utility company and policy-maker perspectives. Currently, in general only traditional thermal 27 storage with water tanks is economically viable. However, CES is expected to offer new 28 opportunities for the energy transition since the community scale introduces several 29 advantages for electrochemical technologies such as batteries. Technical and economic 30 benefits over energy storage in single dwellings are driven by enhanced performance due to 31 less spiky community demand profile and economies of scale respectively. In addition, CES 32 brings new opportunities for citizen participation within communities and helps to increase 33 awareness of energy consumption and environmental impacts. 34

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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.

Lithium-Sulfur (Li-S) batteries represent a perspective energy storage technology, which reaches very high theoretical limits in terms of specific capacity, specific energy, and energy density. However, Li-S batteries are governed by the polysulfide shuttle mechanism, which causes fast capacity fade, low coulombic efficiency, and high self-discharge rate. The self-discharge is an important characteristic of Li-S batteries for both practical applications and laboratory testing, which is highly dependent on the operating conditions. Thus, to map and to understand the Li-S self-discharge behavior under various conditions, such as depth-ofdischarge, temperature, and idling time, a set of experiments were performed in this work on 3.4 Ah Li-S pouch cells. The results are systematically presented in form of open-circuit voltages during idling and self-discharge separated into reversible and irreversible capacity loss. Furthermore, estimation of the actual high voltage plateau capacity based on a self-discharge constant was performed according to an earlier proposed methodology; however, the method needs further improvements in order to estimate this capacity accurately for all conditions.

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 classifies the recently proposed online SOC estimation methods 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.