Electronics

This study investigates two distributed control schemes for residential DC microgrids: an autonomous distributed cooperative control based on the state of charge (SoC) of battery-driven DC/DC converters acting as voltage sources, and an autonomous decentralized control based solely on local feeder voltage, enabled by a battery-integrated feeder configuration. A DC microgrid model consisting of five houses was constructed in MATLAB/Simulink, incorporating PV panels, small wind turbines, typical Japanese household load profiles, EV charging, and realistic meteorological data in Sendai, Japan, for the year of 2023. The cooperative control scheme based on the SoC effectively stabilized feeder voltage, suppressed surplus renewable generation, and reduced grid interaction. The decentralized scheme also maintained stable feeder voltage without device-to-device communication. In both schemes, houses near the grid interconnection point experienced more frequent battery cycling, indicating the need for larger battery capacities at those locations to equalize the overall battery lifetime. Simulations demonstrated that, under the specific configurations and meteorological conditions evaluated, installing a 3.5 kW PV system and a 20 kWh battery per house supplies over 80% of the annual consumption from renewable energy and enables sustained islanded operation during disasters under reduced household load conditions, although temporary energy shortages may occur under adverse winter conditions. These results offer valuable guidelines for designing environmentally sustainable and resilient residential microgrids.

Conventional street-lighting systems commonly operate on fixed schedules and remain at full power throughout the night, resulting in unnecessary energy consumption. Although sensor-activated streetlights can adjust their output in response to pedestrians or vehicles, most operate as independent luminaires and therefore do not illuminate the road ahead in advance. This study proposes a segment-synchronous smart street-lighting control system in which streetlights are divided into multiple segments and all luminaires within a segment operate synchronously. When no pedestrian or vehicle is detected, the streetlights remain in dim-light mode. Once a road user is detected, Wi-Fi communication switches the entire segment synchronously to strong-light mode; after the road user leaves and the hold interval expires, the luminaires return to dim-light mode. The system integrates Internet of Things (IoT) technology with a cloud platform for parameter configuration and remote monitoring. For a nominal 100 W streetlight, the model-based energy-saving rate reaches 90% under no-traffic conditions and is approximately 81.00% under the deterministic 12-trigger/h case and 81.44% under a homogeneous Poisson vehicle-arrival model with a mean rate of 12 vehicles/h. In the four-luminaire laboratory prototype, the measured synchronization latency was 120 ms and 199 of 200 trigger tests achieved segment-wide synchronization. These analytical and prototype-scale results support the feasibility of the segment-synchronous concept for low-traffic-road applications; larger multi-segment field validation remains necessary.

Accurate multi-horizon solar power forecasting is vital for modern power distribution networks, yet standard deep learning models face limitations due to weather stochasticity and prohibitive computational requirements on resource-constrained edge devices. To address these issues, this paper proposes a Physics-Informed Similar-Day Data Augmentation (PI-SDDA) framework coupled with a Pruning-Optimized Temporal Convolutional Network (POTCN). The PI-SDDA strategy integrates multi-variable meteorological dynamics with candidate cluster size optimization to capture diurnal thermal periodicity and transient weather variations. To enable lightweight deployment, POTCN incorporates Batch Normalization scaling factors for channel-level sparsification within dilated causal residual blocks, significantly reducing parameter redundancy while preserving temporal receptive fields. Experimental results demonstrate superior forecasting performance across fifteen-minute, one-hour, and two-hour prediction horizons under standard statistical metrics compared with existing benchmarks. Edge hardware deployment tests on an embedded edge computing platform further verify low inference latency and minimal power consumption. The proposed framework provides an accurate, resource-efficient solution for real-time edge intelligence in power systems.