A Novel Multiobjective Optimization Algorithm for Home Energy Management System in Smart Grid

Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS) in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user’s comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource), and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user’s comfort level simultaneously. Simulation results indicate that the algorithm can reduce user’s electricity cost significantly, ensure user’s comfort level, and take a tradeoff between the cost and comfort level conveniently

Observed Changes of Rain-Season Precipitation in China from 1960 to 2018

Precipitation during the main rain season is important for natural ecosystems and human activities. In this study, according to daily precipitation data from 515 weather stations in China, we analyzed the spatiotemporal variation of rain-season (May–September) precipitation in China from 1960 to 2018. The results showed that rain-season precipitation decreased over China from 1960 to 2018. Rain-season heavy (25 ≤ p < 50 mm/day) and very heavy (p ≥ 50 mm/day) precipitation showed increasing trends, while rain-season moderate (10 ≤ p < 25 mm/day) and light (0.1 ≤ p < 10 mm/day) precipitation showed decreasing trends from 1960 to 2018. The temporal changes of precipitation indicated that rain-season light and moderate precipitation displayed downward trends in China from 1980 to 2010 and rain-season heavy and very heavy precipitation showed fluctuant variation from 1960 to 2018. Changes of rain-season precipitation showed clear regional differences. Northwest China and the Tibetan Plateau showed the largest positive trends of precipitation amount and days. In contrast, negative trends were found for almost all precipitation grades in North China Plain, Northeast China, and North Central China. Changes toward drier conditions in these regions probably had a severe impact on agricultural production. In East China, Southeast China and Southwest China, heavy and very heavy precipitation had increased while light and moderate precipitation had decreased. This result implied an increasing risk of flood and mudslides in these regions. The advance in understanding of precipitation change in China will contribute to exactly predict the regional climate change under the background of global climate change

Spatial Distribution of Permafrost in the Xing’an Mountains of Northeast China from 2001 to 2018

Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km2), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 105 km2 and 6.81 × 105 km2, respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale

Structure Manipulation of C₁N₁‑Derived N‑Doped Defective Carbon Nanosheets to Significantly Boost K‑Storage Performance

Nanocarbon materials demonstrated huge advantages for K-storage applications due to their wide range of structural tunabilities. However, their K-storage performance was still limited by the underutilization of disordered and ordered carbon structures simultaneously. Here, we developed a C1N1-based reconstruction strategy to fabricate N-doped defective carbon nanosheet (NdC) materials for K-storage. The disordered carbon defects and ordered carbon interlayers were well balanced via choosing suitable precursors for self-condensation generation of the C1N1 skeleton as well as subsequently regulating the high-temperature reconstruction process, resulting in a significantly enhanced intercalation-adsorption K-storage mechanism. As a result, the optimized G-NdC materials delivered a high reversible discharging capacity of 620 mA h/g at 50 mA/g and 241 mA h/g even at 1000 mA/g as well as 210 mA h/g after 300 cycles at 500 mA/g. These excellent K-storage properties should be ascribed to the unique order–disorder balanced microstructures with fast surface capacitive-controlled reaction kinetics. This study emphasized the important roles of carbon defects in the K-storage process and provides a deep insight into the understanding of nanocarbon-based K-storage mechanisms