Filtration-based (FB) power/current allocation of battery-supercapacitor (SC) hybrid energy storage systems (HESSs) is the most common approach in DC microgrid (MG) applications. In this approach, a low-pass or a high-pass filter is utilized to decompose the input power/current of HESS into high-frequency and low-frequency components and then assign the high-frequency parts to SC. Moreover, to avoid the state of charge violation (SoC) of SC, this approach requires a rulebased supervisory controller which may result in the discontinuous operation of SC. This paper first provides a small-signal stability analysis to investigate the impact of an FB current allocation system on the dynamic stability of an islanded DC MG in which a grid-forming HESS supplies a constant power load (CPL). Then, it shows that the continuous operation of SC is essential if the grid-forming HESS is loaded by large CPLs. To address this issue, this paper proposes a model predictive control (MPC) strategy that works in tandem with a high-pass filter to perform the current assignment between the battery and SC. This approach automatically restores the SoC of SC after sudden load changes and limits its SoC variation in a predefined range, so that ensure the continuous operation of SC. As a result, the proposed FB-MPC method indirectly enables the MG's proportional-integral (PI) voltage controller to operate with higher gain values leading to better transient response and voltage quality. The performance of the proposed approach is then validated by simulating the system in MATLAB/Simulink. INDEX TERMS Filtration-based power/current allocation systems, battery/supercapacitor hybrid energy storage systems, model predictive control, stability analysis, state of charge recovery. NOMENCLATUREA.

This paper presents an adaptive control framework for the flexible and effective management and control of clustered DC nano-grids (NGs) in an islanded DC microgrid system. It is assumed that each NG contains a photovoltaic (PV) system, a battery energy storage system (BESS), local loads, and a gateway (GW) module. Each NG has a hierarchical control system consisting of a decision-making module and low-level controllers. The decisionmaking module ensures various desirable features including plug-and-play operation of NGs, maximum utilization of PV power generations, and avoiding state of charge (SoC) violation of batteries. Moreover, an adaptive model predictive control (AMPC) strategy is proposed to regulate the voltage of the NG local DC buses in the presence of nonlinear loads. This approach improves the performance of the NG voltage control system and reduces the current ripples of BESSs, thereby enhancing the lifetime of the batteries. In addition, a smart switching consensus-based control strategy is designed that provides flexible power sharing among the NGs to balance the SoC of BESSs in which the BESSs altogether imitate the behaviour of a single cloud energy storage system (ESS). Finally, the performance of the proposed control system is verified by simulating the DC microgrid in MATLAB/Simulink.

This paper proposes a distributed rule-based power management strategy for dynamic power balancing and power smoothing in a photovoltaic (PV)/battery-supercapacitor hybrid energy storage system. The system contains a PV system, a battery-supercapacitor hybrid energy storage system (HESS), and a group of loads. Firstly, an active compensation technique is proposed which improves the efficiency of the power smoothing filter. Then, a distributed supervisory control technique is employed that prevents the BESS and SC from SOC violation while maintaining the balance between generation and load. To this end, the system components are divided into three different reactive agents including an HESS agent, a PV agent, and a load agent. These agents react to the system changes by switching their operational mode upon satisfying a predefined rule. To analyse the hybrid dynamical behaviour of the agents and design the supervisory controllers, the agents are modelled in hybrid automata frameworks. It is shown that the proposed distributed approach reduces the complexity of the supervisory control system and increases its scalability compared to its equivalent centralized method. Finally, the performance of the proposed approach is validated using a test system simulated in MATLAB/Simulink.

This paper proposes and develops the idea of using a community supercapacitor (SC) in an islanded DC multiple nano-grids (MNG) system. In the proposed structure, the community SC works in tandem with the community/cloud battery energy storage system (CBESS) of the DC MNG. This combination forms a grid-forming battery-supercapacitor cloud hybrid energy storage system (CHESS), which is responsible for maintaining the voltage stability and power balance at the common DC bus of the MNG system. Also, to effectively utilize the SC capacity, this paper proposes a modified control structure for each DC nano-grid enabling the local BESS units to coordinate with the community SC. Then, it is shown that, in the proposed grid-forming CHESS technology, the output power of all the local and community BESS units has significantly smoother power variations leading to a higher battery lifetime. Additionally, it is shown that the proposed CHESS technology can improve the voltage stability of the system leading to higher voltage quality. Moreover, it is discussed analytically that the proposed CHESS technology requires less energy storage capacity for the community SC compared to its equivalent MNG with a distributed SC architecture. Finally, these results are verified by simulating two case-study MNGs in MATLAB/Simulink.

No abstract

No abstract

No abstract

No abstract