Electrical microgrids (EMGs) are positioned to play an important role in the future distribution grid as technology advances and distributed energy resources (DERs) emerge. In the event of major natural disasters, the functioning capabilities of active distribution systems (DS) face ongoing problems. To assess the resilience of a distribution system, a thorough methodology must be established. This study proposes a methodology that demonstrates how the use of EMGs and DERs, in conjunction with line hardening, can improve resilience in extreme operating situations. The framework examines four separate scenarios, each with its own set of restoration procedures and critical loads. A combination of battery electric vehicles (BEVs), solar photovoltaic distributed generation (SPV-DG), battery energy storage systems (BESS), and distribution static compensators (DSTATCOMs) is being integrated into practical Indian distribution systems consisting of 28 and 52 buses to improve resilience. The goal is to improve the newly specified resilience indices and restore all of the loads that have been affected by the faults. The bald eagle search algorithm (BESA) is used to identify the appropriate allocation of SPV-DG and solve the objective function within the system. The results of our tests show that our proposed technique has the capacity to enhancement the resilience and successfully restore all damaged loads in a distribution system.

In this paper, an AI-enhanced FPGA-based smart energy management system (SEMS) is implemented for optimal autonomous microgrid economic operation. The main objective of this study is to increase the reliability of the isolated microgrid and minimize the operating cost. The proposed system is prototyped using an Altera Cyclone IV EP4CE6 FPGA Development Kit, which is an extremely flexible and reprogrammable logic device. The results were taken for 4 continuous hours to clarify the reliability of the model. The results show that the SEMS has achieved high performance, a reliable and affordable energy supply, and a quick response to unexpected changes in the loads or power produced. In addition, the use of optimization techniques to enhance the efficiency of the FPGA makes the microgrid more attractive as it increases its flexibility, controllability, and ability to efficiently manage energy while reducing the overall cost. It succeeds in minimizing operating costs by 26.43% compared to the experimental case that did not utilize optimization techniques.

The cascaded H-bridge (CHB) is an attractive converter topology because it can provide voltage and power scalability. However, due to the difference in electrical and thermal characteristics of power devices, the CHB can be composed of cells with unequal remaining useful lifetime (RUL). In this paper, a PWM strategy is proposed to manage the lifetime of individual cells within a single-phase CHB by controlling the thermal stress of power devices. The proposed PWM has two variants of modulation signals. The discontinuous modulation (DM) which reduces the thermal stress of a cell and the compensating modulation (CM) for removing the offset voltage introduced by DM. Based on the accumulated damages of cells, the modulation signals are actively updated to achieve equalized RUL. The benefit of the proposed PWM is that the power transmission of cells is not affected by the clamping region of the DM. This is achieved by introducing a factor C during the unclamped region which reduce the fundamental voltage by the same amount the clamping region causes it to increase. The experimental result verifies the impact of the proposed PWM on the power transmission of cells. Furthermore, a long-term study is provided in simulation to validate the lifetime equalization of cells.

In response to the growing need to reduce greenhouse gas emissions and achieve a decarbonized society, this study focuses on the optimal design of electric vehicle (EV) charging stations powered by renewable energy sources. Battery electric vehicles (BEVs), which emit no CO2 during operation, are particularly effective when combined with Park and Ride (P&R) systems that mitigate urban traffic congestion. This paper investigates the optimal operation and configuration of P&R BEV charging stations in Okinawa, Japan, comparing two power supply methods: on-grid and off-grid systems using photovoltaic (PV), wind power generation (WG), and battery energy storage systems (BESS). A multi-objective optimization model is developed to minimize the annual operating cost while meeting EV charging demand. To identify the optimal configuration, an exhaustive search of equipment unit combinations is conducted. The resulting pareto front reveals trade-offs between total annual EV charging energy and parking operator profit. In addition, three representative charging levels are analyzed in detail. Results show that on-grid systems achieve higher profit margins under moderate charging demand, leveraging grid power to maintain stable operations. In contrast, off-grid systems, while requiring higher initial investment, exhibit greater self-sufficiency and perform better in high demand scenarios or in remote areas with limited grid access. Thus, the optimal power supply method should be selected based on site-specific conditions, such as EV charging scale, regional infrastructure, and resilience requirements. Among the options, the on-grid system is identified as the most practical and economically viable solution under current technological and institutional conditions.