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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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.

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

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. I.978-1-4799-5776-7/14/$31.00 ©2014 IEEE