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Summary: In battery swapping station (BSS), the battery swapping of electric vehicle (EV) is not synchronous with the centralized charging of BSS. Based on new energy electricity generation and battery swapping demand information of EVs, how to formulate charging–discharging strategies of centralized charging stations is essential in BSS energy management system. Different from traditional BSS scheduling issues, considering that the supply–demand balance of power in isolated microgrid (IMG) could be regulated by the discharging of backup batteries, a service model of EV BSS is proposed with strong constraints, coupling relationships and randomness. Because the microgrid‐BSS capacity configuration optimization issue is nonlinear and multivariable, if traditional optimization algorithms are utilized, it would take a long time and the optimal solution may not be obtained. Moreover, there is no guarantee of complete convergence even with the standard particle swarm optimization (PSO) algorithm. In this paper, the quantum behavior particle swarm optimization (QPSO) algorithm is used to handle operation control issues of BSS. As shown in case results, with the QPSO algorithm proposed, both the reliability of battery swapping service and the maximum profit of BSSs could be implemented. [ABSTRACT FROM AUTHOR]

Summary: The distributed generations (DGs) of an islanded microgrids (MGs) comprises of interconnecting primary and secondary control layers. The interactions among these controllers may yield new oscillatory modes with low damping, which decrease the stability margins. As the distribution network is dynamic in nature, the maintenance of system stability is of great concern, mainly while operating in autonomous mode. This paper proposes the optimal internal model control (IMC)‐based droop control scheme to improve the small‐signal stability and transient‐response of an inverter‐based MGs (IMGs) under different operating conditions. The proposed optimal scheme is achieved with the following approach: (a) first, comprehensive small signal model is prepared for the study system, and the interaction of the control parameters which influence the system stability are investigated through eigenvalue analysis, (b) the critical values of the significant parameters of the IMC and droop controllers are sorted out, and their corresponding stability domain is formulated, (c) based on the stability domain, PSO optimization technique is employed to generate optimal values, which delivers effective coordination among the crucial control parameters to improve the stability, (d) the developed optimal control scheme is cross‐validated by comparing with the existing methods from the literature. The effectiveness of the proposed optimal scheme is subsequently assessed quantitatively with the help of time‐domain specifications. [ABSTRACT FROM AUTHOR]