Microgrids (MGs) often integrate various energy sources to enhance system reliability, including intermittent methods such as solar panels and wind turbines. Consequently, this integration contributes to a more resilient power distribution system. Additionally, battery energy storage system (BESS) units are connected to MGs to offer grid-supporting services such as peak shaving, load compensation, power factor quality, and operation during source failures. In this context, an energy management system (EMS) is necessary to incorporate BESS in MGs. Consequently, state-of-charge (SoC) equalization is a common approach to address EMS requirements and balance the internal load among BESS units in MG operation. This paper presents a comprehensive review of EMS strategies for balancing SoC among BESS units, including centralized and decentralized control, multi-agent systems, and other concepts such as designing non-linear strategies, optimal algorithms, and categorizing agents into clusters. Moreover, the paper discusses alternatives to improve EMS and strategies regarding the topology of power converters, including redundancy-based topology, modular multilevel converter (MMC), cascaded-based converter, and hybrid-type systems. Additionally, the paper explores optimization processes aimed at reducing operational costs while considering SoC equalization. Finally, second-life BESS (2-BESS) units are explored as an emerging topic, focusing on their operation within specific power converters topologies to achieve SoC balance.

This article proposes a coordinated and decentralized control structure for a dc microgrid (MG) powered by fuel cells (FCs) and energy storage units (ESUs). With the employment of ESUs in the dc MG, the balancing among their states of charge (SoCs) increases their lifetimes and improves the dc MG reliability. Assuming the FC as the main source, fast transients of load on the dc link can damage its membrane. In this context, the FC and ESUs show a complementary behavior during the transitory, while the possibility to change the energy shared between the sources at a steady state promotes the energy management system. The SoCs of all ESUs connected to the MG are balanced using a fuzzy controller. Furthermore, a low-pass filter in association with a droop controller can mitigate the stress caused by the fast transients on the FC terminals. The main benefits of using the fuzzy controllers are related to the practical simplicity of implementation and the fact that a high-speed link of communication between the FC and the ESUs is not needed. Additionally, Lyapunov’s indirect method performs the stability analysis of the dc MG. Finally, a laboratory-scale prototype evaluates the effectiveness of the proposed approach.

In this article, a weighted-consensus strategy for state-of-charge (SoC) balancing, voltage regulation and accurate current sharing in a dc microgrid (MG) composed of battery energy storage systems (BESSs) is presented. The consensus strategy is applied in association with a S-shape function known as sigmoid function, a common activation function in neural networks. By a neighbor-to-neighbor communication, each BESS exchange its SoC information to achieve faster SoC balancing and uses only its capacity for accurate current sharing. The sigmoid functions ensure continuity and differentiability of the system around the charging/discharging of the BESSs and guarantee the SoC balancing even if the graph becomes disconnected by communication failures or if the consensus is switched-off. As each BESS uses its own information in the weighted-consensus, the resilience against attacks and communication time-delays is increased. When all SoCs are within operational limits, the strategy avoids circulating currents in the MG by forcing all BESSs either charge or discharge simultaneously. However, if the SoC limits are violated, simultaneous charge and discharge among BESSs in a low rate is allowed to satisfy the limits. Experimental tests show the feasibility of the proposed approach and its faster SoC balancing in relation to advanced methods in the literature.

This paper presents a method for Energy Storage Systems (ESSs) equalization and energy management in dc microgrids (MGs) with slow dynamic sources, such as Fuel Cells (FCs). The three main features of this method are the ESSs equalization, the energy management between the ESSs and the FC, and the ability to suppress fast transients of load, preventing damages in the FC. The equalization is performed using a State of Charge (SoC)-Sharing Function, which is based on a Sigmoid Function (SF). The SoC-Sharing Function is designed similarly to the droop controller, therefore, it is suitable for operation in droop based MGs. Additionally, the ability to suppress fast transients of load is performed by adding a low pass filter in the FC control loop. The main advantages of this method are the SF smooth behavior and the lack of need for a high-speed link of communication between the sources. To evaluate the MG stability, the Lyapunovb’s Indirect Method is applied in the MG model considering different scenarios varying the SoC and load connected to the dc-link. Finally, the model is validated by comparing simulation and experimental results supplied by a lab-scale prototype.

DC microgrids (MGs) represent self-contained systems for distributing and managing electricity locally, offering advantages in terms of efficiency and control. This article proposes the optimization of redundancy-based dc MG, emphasizing the integration of an intelligent algorithm to minimize operational costs under real-time pricing conditions and also increase the efficiency operation. The redundancy-based dc MG consists of a cascaded bidirectional Cuk converter (CBC) linked to a cascaded bidirectional boost converter, sharing two battery energy storage system (BESS) units as common inputs. Thereby, two boost converters interface the dc utility and fuel cell (FC) to the dc-link of the CBC. The redundancy-based dc MG operates with a coordinated and decentralized control among the dc utility, FC and two BESS units. The energy management system (EMS) employs a droop control for the dc utility and FC, while BESS units adhere to a state-of-charge (SoC)-sharing function. In addition, the particle swarm optimization dynamically adjusts EMS parameters for optimal real-time performance. Then, a stability analysis determines MG operational boundaries, and the effectiveness of the method is confirmed through experiments using Typhoon HIL and dSPACE setups. Finally, comparative evaluations against traditional SoC-based droop and genetic algorithms demonstrate the cost reduction of the proposed approach.

This article proposes a dc microgrid (MG) composed of a cascaded bidirectional Cuk converter (CBC) connected to a cascaded bidirectional boost converter (CBB) sharing two battery energy storage systems (BESSs) as common inputs. Then, the dc-link from CBC also receives a boost converter, which interfaces the dc utility. Thus, this configuration can coordinate the BESSs and the dc utility by employing the state-of-charge (SoC)-sharing function and the droop controller. In this context, the combination of CBC and CBB can improve the reliability of the SoC-sharing function with the battery-to-battery equalization on CBB and can enhance the availability of power production on the CBC dc-link. Therefore, the main advantage of the proposed method is to ensure the SoC equalization even when the MG is under maintenance or electrical fault. Additionally, the dc MG involves a modified energy management system while Lyapunov's indirect method proves the stability analysis. Finally, the experimental setup validates the feasibility of the proposed approach.