Search

the complex mountainous environment, it is valuable to explore the variation rule of soil pH for accurately managing soil pH and the sustainable use of soil. In this study, we collected 394,736 field surface soil samples and recorded the paired environmental conditions in Guizhou Province. The spatial autocorrelation, semi-variance model and geographical detector were employed to analyze the spatial structure characteristics and distribution pattern of soil pH. The results showed that the surface soil pH varies from 3.11 to 8.99, with the variation coefficient of 17.14%. Soil pH shows a patchy mosaic distribution pattern. The structural factors of climate, parent material, and topography play a determinant role in controlling pH differentiation. Geo detector analysis showed that the explanatory power of environmental factors for the soil pH differentiation is in the order of parent rock (0.0976) > altitude (0.0144) > soil temperature regime (0.0112) > land use (0.00503) > soil moisture regime (0.000812). The reciprocal effect of parent rock and altitude is noticeable. [ABSTRACT FROM AUTHOR]

The high penetration rate of electric vehicles (EVs) will aggravate the uncertainty of both supply and demand sides of the power system, which will seriously affect the security of the power system. A microgrid (MG) system based on a hybrid energy storage system (HESS) with the real-time price (RTP) demand response and distribution network is proposed to deal with uncertainties. Through the guidance of RTP, the electricity consumption behavior of consumers and car owners is more adaptable to the output uncertainty of renewable energy source (RES) to ensure that the system has sufficient elastic capacity within the schedule cycle. The bi-level programming optimization model of HESS capacity is established, maximizing the MG's reliability and minimizing the comprehensive operating cost (COC). In addition, an EV's orderly charging/discharging strategy is formed, which effectively reduces operating costs and peak-to-valley load differences. The results show that EVs can effectively mitigate the peak-to-valley load difference by 20.5% under 100% participation in orderly charging/discharging. Under RTP-based demand response, MG can reduce the COC by 25.5%. In addition, the number of EVs participating in vehicle-to-grid also has different effects on scheduling. Based on the experimental data from Guizhou, results show that 200 electric private vehicles (EPVs) and ten electric buses (EBs) are the optimal number of vehicles to be solved; namely, the optimal capacity of the HESS is achieved. • Established a bi-level optimization model including capacity optimization and scheduling optimization. • Bootstrap elastic loads using real-time price-based demand-side response. • The orderly charging/discharging strategy of electric vehicles is adopted to exert the ability of mobile energy storage. • Narrows the peak-to-valley load difference, improves system operation reliability, and reduces overall operating costs. [ABSTRACT FROM AUTHOR]