Estimating the parameters of lithium-ion (Li-ion) batteries under dynamic working conditions is a critical challenge in the health management of electrical energy storage systems. This paper estimates the equivalent circuit model (ECM) parameters and analyzes the influence of different factors on the Li-ion batteries impedance using the electrochemical impedance spectroscopy (EIS) technique. Firstly, the influence of the temperature, state of charge (SOC) and number of charging/discharging cycles on the impedance spectrum was studied. Nyquist plots were used to perform this analysis. Subsequently, ECMs were used to characterize the Li-ion batteries under study at different operating conditions. Specifically, the ECM parameters were estimated using the gaining-sharing knowledge (GSK) metaheuristic algorithm, allowing the consequent estimation of the impedance spectra obtained by the EIS technique. Experimental results included 15 datasets measured experimentally at three temperature levels for five SOCs and different number of charging/discharging cycles. To investigate the variation of Li-ion battery parameters with cyclic aging 350 datasets from the literature were also used. The results showed that the impedance and model parameters depend considerably on the considered factors. The estimation of the ECM parameters demonstrated high accuracy and reliability (mean RMSE values of 4.32E-02 and 2.33E-02 and mean STD values of 3.38E-16 and 5.82E-13 with measured and literature datasets, respectively), allowing the robust determination of the numerous impedance spectra under study.

The scenario where the renewable generation penetration is steadily on the rise in an increasingly atomized system, with much of the installed capacity “sitting” on a distribution level, is in clear contrast with the “old paradigm” of a natural oligopoly formed by vertical structures. Thereby, the fading of the classical producer–consumer division to a broader prosumer “concept” is fostered. This crucial transition will tackle environmental harms associated with conventional energy sources, especially in this age where a greater concern regarding sustainability and environmental protection exists. The “smoothness” of this transition from a reliable conventional generation mix to a more volatile and “parti-colored" one will be particularly challenging, given escalating electricity demands arising from transportation electrification and proliferation of demand-response mechanisms. In this foreseeable framework, proper Hybrid Energy Systems sizing, and operation strategies will be crucial to dictate the electric power system’s contribution to the “green” agenda. This paper presents an optimal power dispatch strategy for grid-connected/off-grid hybrid energy systems with storage capabilities. The Short-Term Price Forecast information as an important decision-making tool for market players will guide the cost side dispatch strategy, alongside with the storage availability. Different scenarios were examined to highlight the effectiveness of the proposed approach.

This paper proposes a comprehensive modeling and parameters extraction method of solar photovoltaic module based on the Particle Swarm Optimization algorithm. The characteristic curves of photovoltaic panel are obtained by using only the information provided by the manufacturer data-sheet, avoiding the need to carry out experimental data. The performance and the accuracy of the proposed method are evaluated by applying the one-diode model and the results are compared with those obtained by the well-known Lambert W function. The proposed method shows a higher performance.