Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.
A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.
h i g h l i g h t sA reduced low-temperature electrothermal coupled model is proposed. A novel frequency-dependent equation for polarization parameters is presented. The model is validated under different frequency and lowtemperature conditions. The reduced model exhibits a high accuracy with a low computational effort. The adaptability of the proposed methodology for model reduction is verified. g r a p h i c a l a b s t r a c t a b s t r a c t A low-temperature electro-thermal coupled model, which is based on the electrochemical mechanism, is developed to accurately capture both electrical and thermal behaviors of batteries. Activation energies reveal that temperature dependence of resistances is greater than that of capacitances. The influence of frequency on polarization voltage and irreversible heat is discussed, and frequency dependence of polarization resistance and capacitance is obtained. Based on the frequency-dependent equation, a reduced low-temperature electro-thermal coupled model is proposed and experimentally validated under different temperature, frequency and amplitude conditions. Simulation results exhibit good agreement with experimental data, where the maximum relative voltage error and temperature error are below 2.65% and 1.79°C, respectively. The reduced model is demonstrated to have almost the same accuracy as the original model and require a lower computational effort. The effectiveness and adaptability of the proposed methodology for model reduction is verified using batteries with three different cathode materials from different manufacturers. The reduced model, thanks to its high accuracy and simplicity, provides a promising candidate for development of rapid internal heating and optimal charging strategies at low temperature, and for evaluation of the state of battery health in on-board battery management system.
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