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Summary Wind power plant operators are often faced with extra charges when their power production does not match the forecasted power. Because the accuracy of wind power forecasts is limited, the use of energy storage systems is an attractive alternative even when large-scale aggregation of wind power is considered. In this paper, the economic feasibility of lithium-ion batteries for balancing the wind power forecast error is analysed. In order to perform a reliable assessment, an ageing model of lithium-ion battery was developed considering both cycling and calendar life. The economic analysis considers two different energy management strategies for the storage systems and it is performed for the Danish market. Analyses have shown that the price of the Li-ion BESS needs to decrease by 6.7 times in order to obtain a positive net present value considering the present prices on the Danish energy market. Moreover, it was found that for total elimination of the wind power forecast error, it is required to have a 25-MWh Li-ion battery energy storage system for the considered 2 MW WT. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract State of Health (SOH) of Lithium-ion (Li-ion) battery plays a pivotal role in the reliability and safety of the Battery Energy Storage System (BESS) in the power system. Utilizing the features from the terminal voltage response of the Li-ion battery under current pulse test, a new method is proposed in this paper by using the Support Vector Machine (SVM) technique for accurately estimating the battery SOH. Since the terminal voltage measured at the same condition varies with the battery aging process, the features for SOH estimation are extracted from the voltage response under a specific current pulse test. The benefit of the proposed method is that the features come from the short-term test, which is much convenient to be obtained in real applications. After applying the short term current pulse test (few seconds), the keen points and the slopes in the voltage response curve are selected as the potential candidate features. In order to find the most effective feature for SOH estimation, all the possible combinations of the features are investigated and compared. Afterwards, SVM is able to establish the optimal SOH estimator on the basis of the optimal feature combination and the battery SOH. A LiFePO4 battery is tested in the test station for 37 weeks to verify the validation of the proposed method.

This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell/panel in Standard Test Conditions (STC)1 are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence. Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested.

With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, 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.

Meeting ambitious goals of transition to distributed and environmentally-friendly renewable energy generation can be difficult to achieve without energy storage systems due to technical and economical challenges. Moreover, energy storage systems have a high potential of not only smoothing and improving the predictability of the intermittent renewables but also of providing the ancillary services in the future energy markets. However, this is currently difficult to achieve due to high prices of the energy storage systems and difficulties with accurate prediction of the energy storage systems lifetime, which introduces significant risk into the business model. This paper deals with the investigation of the lifetime of LiFePO4/C battery systems when they are used to provide primary frequency regulation service. A semi-empirical lifetime model for these battery cells was developed based on the results obtained from accelerated lifetime testing. The developed Li-ion battery lifetime model is later a base for the analyses of the economic profitability of the investment in the Li-ion battery energy storage system (BESS), which delivers the primary frequency regulation service on the Danish electricity market. Moreover, in this paper a possible improvement of the Li-ion BESS energy management strategy is shown, which allows for obtaining the higher NPV.

New and stronger power quality requirements are issued due to the increase amount of photovoltaic (PV) installations. In this paper different methods are used for continuous grid monitoring. By injecting a noncharacteristic harmonic current and measuring the grid voltage response it is possible to evaluate the grid impedance directly by the PV-inverter, providing a fast and low cost implementation. This principle theoretically provides a correct result of the grid impedance but when using it into the context of PV integration, different implementation issues strongly affect the quality of the results. This paper presents a new impedance estimation method including typical implementation problems encountered and it also presents adopted solutions for on-line grid impedance measurement. Practical tests on an existing PV-inverter validate the chosen solutions.

The development of Modular Multilevel Converter (MMC) technology is a breakthrough in the field of High and Medium Voltage applications. In general, simulations are needed to study a new application and to analyze the impact of a new converter in power systems. Thus, accurate models able to represent the dynamics present in the converter are required. This work aims to compare two MMC average models and one average model with three modulation strategies in order to propose high level models, able to represent the MMC dynamics, such as the injected active and reactive power, and the SMs capacitor voltages, without the necessity of switching models. A case study is presented considering a Double Star (DS) MMC, connected in a distribution system during an unbalanced voltage sag. The results shows that considering MMC average models only the steady state can be represented. On the other hand, when the modulation techniques are considered together with the MMC average models, it is possible to represent the capacitor voltage ripples and the grid current ripples increasing slightly the necessary processing time.