Dielectric materials with superb thermal and electrical properties are highly desired for high-voltage electrical equipment and advanced electronics. Here, we propose a novel strategy to improve the performance of epoxy composites by employing boron nitride nanosheets (BNNSs) and γ-glycidyl ether oxypropyl sesimoxane (G-POSS) as functional fillers. The resultant ternary epoxy composites exhibit high electrical resistivity (1.63 × 10¹³ Ω·cm) and low dielectric loss (<0.01) due to the ultra-low dielectric constants of cage-structure of G-POSS. In addition, a high thermal conductivity of 0.3969 W·m⁻¹·K⁻¹ is achieved for the epoxy composites, which is 114.66% higher than that of pure epoxy resin. This can be attributed to the high aspect ratio and excellent thermally conductive characteristics of BNNSs, promoting phonon propagation in the composites. Moreover, the epoxy composite simultaneously possesses remarkable dynamic mechanical properties and thermal stability. It is believed that this work provides a universal strategy for designing and fabricating multifunctional composites using a combination of different functional fillers.

The increasing integration of distributed renewable energy sources into distribution networks results in significant voltage regulation challenges. To address these challenges, we introduce a novel data-driven approach for voltage regulation that utilizes predictive control mechanisms, specifically data-enabled predictive control (DeePC). This method exploits the capabilities of photovoltaic (PV) inverters and battery energy storage systems (BESS) to manage bus voltages within the distribution network. Unlike traditional model-based approaches that require a precise physical model of the network, the DeePC algorithm operates optimally by relying solely on historical data to predict and adjust bus voltages. By employing the DeePC algorithm, the proposed controller maintains voltage profiles and the state of charge (SoC) of BESSs within operational thresholds in an optimal and robust manner. To further reduce the computational complexity, a reformulation of DeePC is developed using scoring functions, where the DeePC algorithm is efficiently approximated via differentiable convex programming. We validate our approach through simulations on the IEEE 34-bus test system, demonstrating its efficiency in maintaining desired voltage levels without the need for a detailed physical system model.