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.

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.

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-of-discharge, 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.

Lithium-ion batteries have developed into a popular energy storage choice for a wide range of applications because of their superior characteristics in comparison to other energy storage technologies. Besides modelling the performance behavior of Lithium-ion batteries, it has become of huge interest to accurately diagnose their state-of-health (SOH). At present, Lithium-ion batteries are diagnosed by performing capacity or resistance (current pulse) measurements; however, in the majority of the cases, these measurements are time consuming and result in changing the state of the battery as well. This paper investigates the use of the electrochemical impedance spectroscopy (EIS) technique for SOH diagnosis of Lithium-ion battery cells, instead of using the aforementioned techniques, since this new method allows for online and direct measurement of the battery cell response in any working point. For the investigation, a 2.5 Ah Lithium-ion battery cell based on lithium iron phosphate and graphite (LiFePO ₄ /C), as active material, was used. The obtained results at different battery SOH levels have been analyzed in detail and are suggesting that the EIS technique represents a promising tool for diagnosis the lithium-ion battery cells' SOH; meaningful results regarding the pulse power capability decrease of the tested Li-ion battery cells were obtained by applying the EIS technique.

CubeSats and small satellite solutions are increasing in popularity as they enable a fast, cheap, and agile way for satellite applications. An essential component of nearly every satellite is the energy storage device, which is practically equal to a battery. Consequently, an overview of past, present, and future battery technologies for CubeSats is presented. CubeSats use typically commercial off-the-shelf (COTS) batteries. They are not primarily dedicated to space, so their suitability to the space environment needs to be evaluated. Batteries are also considered as potentially dangerous goods. Thus, there are guidelines and standards that specify safety criteria and tests for the batteries in order to be allowed for transportation and launch. Furthermore, the character of satellites’ missions determines their demand on batteries in terms of current rates, depth-of-discharge, and lifetime. Thus, these expectations are discussed. A market survey was also carried out to identify currently available commercial battery solutions and their parameters. This work summarizes the status, requirements, and the market situation of batteries for CubeSats.

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV