Nylon 6 Nanofiber Web-Based Signal Transmission Line Treated with PEDOT:PSS and DMSO Treatment

Highly conductive nylon 6 nanofiber webs, incorporating poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and dimethyl sulfoxide (DMSO), were prepared for textile-based signal transmission lines. To improve the electrical performance of the textiles, they were optimized by the number of coating cycles and the solvent treatment step. The nanofiber web coated four times with PEDOT:PSS showed a six-times reduction in sheet resistance compared to that of once. In addition, the sample treated with both adding and dipping of DMSO showed a significant decrease of 83 times in sheet resistance compared to the sample without treatment of DMSO. Using samples with excellent electrical conductivity, the waveforms of the signal in the time domain were analyzed and shown to have an amplitude and phase almost identical to that of the conventional copper wire. As a result of the S21 characteristic curve, selected textiles were available up to the 15 MHz frequency bandwidth. In the FE-SEM image, it was observed that the surface of the coated sample was generally covered with PEDOT:PSS, which was distinguished from the untreated sample. These results demonstrate that the nanofiber web treated with the optimized conditions of PEDOT:PSS and DMSO can be applied as promising textile-based signal transmission lines for smart clothing

Nylon 6 Nanofiber Web-Based Signal Transmission Line Treated with PEDOT:PSS and DMSO Treatment

Highly conductive nylon 6 nanofiber webs, incorporating poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and dimethyl sulfoxide (DMSO), were prepared for textile-based signal transmission lines. To improve the electrical performance of the textiles, they were optimized by the number of coating cycles and the solvent treatment step. The nanofiber web coated four times with PEDOT:PSS showed a six-times reduction in sheet resistance compared to that of once. In addition, the sample treated with both adding and dipping of DMSO showed a significant decrease of 83 times in sheet resistance compared to the sample without treatment of DMSO. Using samples with excellent electrical conductivity, the waveforms of the signal in the time domain were analyzed and shown to have an amplitude and phase almost identical to that of the conventional copper wire. As a result of the S21 characteristic curve, selected textiles were available up to the 15 MHz frequency bandwidth. In the FE-SEM image, it was observed that the surface of the coated sample was generally covered with PEDOT:PSS, which was distinguished from the untreated sample. These results demonstrate that the nanofiber web treated with the optimized conditions of PEDOT:PSS and DMSO can be applied as promising textile-based signal transmission lines for smart clothing

Optimal Scheduling for the Complementary Energy Storage System Operation Based on Smart Metering Data in the DC Distribution System

The increasing penetration of distributed generation (DG) sources in low-voltage grid feeders causes problems concerning voltage regulation. The penetration of DG sources such as photovoltaics (PVs) in the distribution system can significantly impact the power flow and voltage conditions on the customer side. As the DG sources are more commonly connected to low-voltage distribution systems, voltage fluctuations in the distribution system are experienced because of the DG fluctuation and uncertainty. Therefore, the penetration of DGs in distribution systems is often limited by the required operating voltage ranges. By using an energy storage system (ESS), voltage fluctuation can be compensated for, thus satisfying the voltage regulation requirements. This paper presents an ESS scheduling algorithm based on the power injection data obtained from a smart metering system. The proposed ESS scheduling algorithm is designed for use within a direct current (DC) distribution grid, which comprises customers, each with a PV and an ESS system. The purpose of this ESS scheduling algorithm is to optimize the ESS scheduling by considering the complementary operation among all the ESSs

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values