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The popularity of electric vehicles (EVs) brings environmental benefits, but their hard-to-estimate stochastic charging behaviors places additional diversity on grid load management. This paper proposes a procedure to identify typical charging load profiles (CLPs) via large scale charging session data from charging stations (CSs). The daily CLPs are computed from charging sessions, and a comprehensive similarity metric based on the weighting of Euclidean and Pearson correlation coefficient is proposed to achieve better clustering. The clustering is performed using the Clustering LARge Applications (CLARA) algorithm to accommodate large sample scenarios. Subsequently, hierarchical clustering of CSs is performed based on possible CLPs，and their CLPs are estimated by Monte Carlo simulation. The performance of the proposed method is tested and evaluated with over 340,000 charging sessions from 109 CSs in Wuhan at central China, and seasonal differences in CLPs are explored. The results show that the method of mining typical CLPs from charging sessions is effective, 17 typical CLPs are identified in different seasons, which provide effective information on the fluctuation and magnitude of daily power demand, the charging power demand also shows significant seasonal differences, and good accuracy is achieved by dividing the CSs into different groups for load estimation. • A method for identifying typical charging load profiles from charging session data is proposed. • 17 typical charging load profiles for charging stations in Wuhan are reported. • Monte Carlo simulation combined with typical charging load profiles enables good estimation of charging loads. • Seasonal differences in charging loads are compared. [ABSTRACT FROM AUTHOR]

Pertya multiflora (Asteraceae) is a rare wild plant species narrowly distributed in Zhejiang province, China. In our present study, we assembled its complete chloroplast genome using high-throughput sequencing data. The results indicated that the whole chloroplast genome of P. multiflora was 153,396 bp in length. Its large single copy, small single copy, and inverted region sequences were 84,575 bp, 18,451 bp, and 25,185 bp. The P. multiflora chloroplast genome was composed of 134 genes, including 87 protein-coding genes, 37 tRNA genes, eight rRNA genes, and two pseudogenes. Phylogenetic analysis results showed that P. multiflora was grouped with Gerbera jamesonii, with 100% bootstrap support. [ABSTRACT FROM AUTHOR]

Urban form significantly influences building energy consumption. However, most research has primarily focused on quantifying the relationship between the two factors, with limited exploration of optimizing urban form to enhance building energy performance. Moreover, studies often fail to consider multiple performance objectives simultaneously. This study proposes a multi-objective optimization framework for early urban design stage to improve energy and environmental performance in residential block layouts. The performance objectives include energy consumption, photovoltaic energy potential, and sunlight hours. The framework is implemented using the parametric platform Rhino & Grasshopper, where a parametric model controls the layout of blocks. Ladybug Tools plugin is employed for performance simulation, and Wallacei is used for optimization. An ideal residential block in Jianhu City, China, is taken as a case study. The study generates a total of 1896 valid solutions, including 58 Pareto solutions. The performance of the Pareto solutions demonstrates considerable improvements, indicating the effectiveness of optimizing urban form for enhancing energy and environmental performance. Compared to the initial solution, a typical Pareto solution showcases a 1.5% reduction in energy consumption, a 52.7% increase in photovoltaic energy potential, and a 50% increase in sunlight hours. These findings underscore the pivotal role of urban block morphology in influencing energy consumption, photovoltaic potential, and daylighting. The proposed multi-objective framework in this study enhances and facilitates sustainable block form design, which is expected to provide technical support for energy-efficient or low-carbon urban design. • A multi-objective optimization framework for designing urban block forms is proposed. • Constructing parametric urban block models based on typical local building types. • Daylighting, energy consumption, and PV energy potential are selected as objectives. • The performance of the Pareto solutions is improved compared to the initial solution. [ABSTRACT FROM AUTHOR]