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Essential mineral elements such as zinc and iron play a crucial role in maintaining crop growth and development, as well as ensuring human health. Foxtail millet is an ancient food crop rich in mineral elements and constitutes an important dietary supplement for nutrient-deficient populations. The ZIP (ZRT, IRT-like protein) transporters are primarily responsible for the absorption, transportation and accumulation of Zn, Fe and other metal ions in plants. Here, we identified 14 ZIP transporters in foxtail millet (SiZIP) and systematically characterized their phylogenetic relationships, expression characteristics, sequence variations, and responses to various abiotic stresses. As a result, SiZIPs display rich spatiotemporal expression characteristics in foxtail millet. Multiple SiZIPs demonstrated significant responses to Fe, Cd, Na, and K metal ions, as well as drought and cold stresses. Based on homologous comparisons, expression characteristics and previous studies, the functions of SiZIPs were predicted as being classified into several categories: absorption/efflux, transport/distribution and accumulation of metal ions. Simultaneously, a schematic diagram of SiZIP was drawn. In general, SiZIPs have diverse functions and extensively involve in the transport of metal ions and osmotic regulation under abiotic stresses. This work provides a fundamental framework for the transport and accumulation of mineral elements and will facilitate the quality improvement of foxtail millet.

With the ever-increased installed capacity of renewable energy generation (REG) units, the power ramp of REG has become a common event, introducing an operational risk to the power system. In order to quantify the operational risk, this paper proposes a novel method for assessing the operational risk of power systems with a high proportion of renewable energy, taking into account the power ramp of REG. Firstly, a weather regionalization is proposed to divide the region of the power system into multiple areas, and then the weather data from the weather stations in these areas will be utilized to forecast the power of REG probabilistically. Moreover, an operational risk assessment model based on the cumulative prospect theory is built to reflect the irrational psychological factors of dispatchers, and two typical indices are introduced to show the operational risk of renewable energy curtailment and load curtailment caused by the power ramp of REG. Finally, numerical simulations are conducted in the actual CEPRI-RE power system in the northwest region of China, and the actual weather data from the meteorological agencies is used. The results show that the proposed method is able to assess the operational risk of the power system with a high proportion of renewable energy effectively, and achieve the early warning 30 min in advance. Besides, it can be found that the power system at the turning point between the power ramp and normal operation has a higher operational risk.

As the largest developing country in the world, China has promised to peak the carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060 to mitigate the greenhouse effect. In the meantime, the electric power industry is playing a key role in achieving this ambitious target through “electricity substitution” and “clean substitution” strategies and constructing the “new type of power system”. Given this background, a specific and detailed model for the electric power supply structure transformation (EPSST) model is proposed in this rapid communication for the first time, which helps to analyse the transformation more reasonably. The proposed EPSST model aims to minimize the total costs for electric power supply structure transformation with the carbon neutrality constraints considered. Besides, the other constraints associated with the construction and retirement of different types of power plants, the balance between power supply and demand, and carbon sinks are also considered. Two scenarios aiming to achieve carbon neutrality before 2060 and 2055 are respectively studied and corresponding analyses for the transformation are also performed. The results show the feasibility of the proposed EPSST model and finally a macroscopic forecast for the future electric power industry is given.

The current price guided demand response mechanism is difficult to accurately achieve the expected load reduction goal for the grid company, while the direct control demand response mechanism cannot meet the requirements of users for autonomous power consumption. Therefore, it is necessary to formulate an incentive compatible demand response mechanism to take the needs of demand-side users and the grid company into account. Based on the current bidding demand response program, a two-stage transaction mechanism of demand response quota based on blockchain is proposed in this work. Firstly, a two-stage transaction mechanism for demand response quota considering day-ahead bidding transaction and intra-day double auction transaction is designed. Then, the smart contract is introduced to realize the proposed blockchain-based two-stage transaction mechanism of demand response quota, and all the smart contract functions involved in each business link are customized. Finally, the effectiveness of the proposed transaction mechanism of demand response quota is illustrated by the simulation results on the platform of Remix IDE.

With the decrease in energy storage costs and the popularization of the shared economy principle, shared energy storage (SES) has been gradually deployed in the residential community to reduce energy costs. However, centralized trading platforms for SES trading are rare in the residential community, so it is difficult for users with the trading demands to search for appropriate trading partners and make a deal. To address the above problem, a decentralized trading mechanism of SES considering the preference of trading subjects is proposed to enable the users in the residential community to trade in the absence of centralized trading platforms. The cost-based price models are built to determine the acceptable price ranges for SES users, DG users and power consumers, which will be used as the basis for the quotation in the process of decentralized trading. Moreover, based on a multinomial logit model and an improved Rubinstein model, the trading partner selection method and the negotiation strategy are proposed to be adopted in the decentralized trading mechanism. Through the autonomous selection of trading partners and multiple rounds of negotiation and quotation, the trading probability of the users can be improved. Numerical simulation studies show that the proposed decentralized trading mechanism can achieve a higher sale rate of SES trading. All types of users can obtain revenue or reduce energy costs through SES trading.

Demand side resources play an important role in dealing with the seasonal and intermittent demand–supply mismatch problem in the power system under the global goal of carbon neutrality. By integrating the uncertain load reduction capacity of small-scale demand resources, load aggregator (LA) participates in demand response (DR) programs to obtain profits, and the deviation between the actual and its scheduled load reduction quantity leads to the loss of revenue. In order to guide users to declare reasonable demand side resource capacity, a credit-based incentive mechanism (CBIM) is designed for LA in this paper, and the corresponding bi-level optimization model of demand side incentive price is established. In the upper-level model, the optimization objective is to maximize the DR revenue of a specified LA, in which the compensation price associated with the response credit is provided to users. In the lower-level model, the expected load reduction is declared by users whose goal is to maximize their revenues considering the comfort loss as well as the compensation price. Simulations illustrate that by properly determining the credit-based demand side incentive price, users are encouraged to feedback load reduction according to their actual capacity, thus improving the revenue of LA in integrating demand side resources.

Inspired from sharing economy and advanced energy storage technologies, hybrid shared energy storage (HSES), as an innovative business model, can provide flexible storage leasing services to new energy stations (NESs) and bring additional profits to the energy storage owner. Under this business model, pricing and planning issues are the main focus of the HSES operator to increase revenues but are rarely considered in current studies. Therefore, a Stackelberg game-based three-stage optimal pricing and planning strategy of HSES is formulated for the operator. First, an HSES model considering two leasing options is developed to provide two kinds of short-term use rights of energy storage resources for NESs. Then, the interactions between selfish NESs and the HSES operator are characterized as a Stackelberg game, and a bi-level pricing and planning strategy optimization model is developed to help the HSES operator make optimal decisions. Finally, considering different characteristics in each stage of the Stackelberg game, a three-stage solution method based on the genetic algorithm (GA) and mixed-integer linear programming (MILP) models is proposed to solve the optimization problem. Case studies on six NESs in a certain region are taken to verify the effectiveness of the proposed strategy. Simulation results show that the HSES operator can obtain maximum profit under the proposed pricing and planning strategy. In addition, the proposed HSES leasing model can provide additional benefits to both the operator and NESs.

The market-oriented transaction of distributed energy resources is regarded as an important way to solve the problem of local distributed energy consumption and improve the operation economy and security of the distribution network. However, the market for distributed power transactions in China is still in the early stage, and there is a lack of effective and reliable transaction mechanisms for standardizing users’ credit behaviors. Therefore, a blockchain-based distributed power transaction mechanism considering credit management is proposed. First, the framework of the distributed power transaction based on blockchain is constructed. On this basis, the process of distributed power transaction considering credit management is designed, which can manage users’ credit behaviors comprehensively from the aspects of market access, bidding mechanism, and trading settlement. Then, the smart contract of distributed power transactions is customized. The effectiveness of the proposed mechanism is illustrated by the simulation results on the platform of Remix IDE.

In future power systems with the characteristics of high-elastic, demand response (DR) is considered to be an essential way for improving system stability and awakening demand-side resources. However, all the DR programs in China are implemented in a centralized model, which is easier for supervision, but hard to deal with the challenges of data credibility, privacy protection, transaction efficiency, and so on. Therefore, a novel blockchain-based framework for the DR program is proposed to deal with the problems. On this basis, the bidding transaction process based on the repeated verification mechanism is designed, and the effectiveness of blockchain in DR is analyzed. Then, the smart contract functions involved in DR bidding transactions and subsidy settlement are customized. The feasibility of the DR bidding mechanism is illustrated by the simulation results on the platform of Remix IDE.

Abstract Demand response is recognized as an effective solution for eliminating power fluctuations and satisfying capacity constraints in power systems. A growing customer base equipped with energy storage and intelligent power meter on the demand side has resulted in the strong interest of China's power companies in demand response. However, most exiting demand response programs in China are based on a centralized framework, which is easier for management, but cannot support a large number of scattered small‐scale users' participation effectively. Therefore, taking the desired features of blockchain technology, such as decentralization, trustworthiness, trackability, and immutability into consideration, the applicability of blockchain in demand response is analyzed. On this basis, a new blockchain‐based framework of China's typical demand response programs is proposed, in which consensus mechanism, encryption algorithm, and smart contract of blockchain are applied to the process of invitation, bidding, and settlement in demand response. Furthermore, the development suggestions of China's demand response based on blockchain technology are put forward from the aspects of trading products, credit management, and platform construction at the end of this work.

Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, no enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs is characterized by their possibility distribution models with chance constraints, and the requirements, e. g., system observ-ability, for rapid back-up black-start measures are also considered. Then, a law of large numbers (LLN) based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

Medium-long term load forecasting is recognized as the basis of economic dispatch in power systems, and high-precision load forecasting results can provide effective support for power management. Therefore, a medium-long term load forecasting method based on the deep learning algorithm is proposed with industry correlation considered. First, the missing monthly load data is supplemented based on the iterative interpolation method, and min–max normalization method is used for data preprocessing. On this basis, the Pearson correlation coefficient is adopted for quantifying the industry correlation and selecting the strongly related industries for load forecasting. Then, a machine learning model long-short term memory (LSTM) is utilized to forecast the medium-long term industry load with the historical data of related industries. Finally, the feasibility and accuracy of the proposed method are illustrated by the case studies on 2 major industries and 30 related industries of Jiangsu Province, China. The load forecasting error shown in the simulation results of the method proposed is kept within 5%, which is much better than other traditional methods.

Nowadays, the construction of smart grids and new digital infrastructure is vigorously promoted by the government, so that the power infrastructure is gradually developing towards intelligence and digitization. However, various challenges such as the industry barrier difficult to remove, the unclear supervision policy, and unmatured technology specifications are brought into the present infrastructure construction. In order to improve the timeliness and accuracy of the power infrastructure fund payment, increase the credibility of data, and enhance the quality and effectiveness of supervision and review, a blockchain-based smart payment scheme for power infrastructure is proposed in this work. First, a smart payment business framework of power infrastructure based on blockchain technology is designed. On this basis, the overall framework of the smart payment scheme and the relationships between participants of the blockchain are analyzed. Then, the smart contract for power infrastructure fund payment based on Remix IDE is customized and the correctness and efficiency of the scheme are verified. Finally, the effectiveness and advantages of the scheme are analyzed from the perspective of reliability, security, and efficiency. The simulation result shows that the smart contract based payment process of power infrastructure funds can be optimized and simplified by the proposed scheme and the overall processing time can be reduced by more than 30%.

With the continuous expansion of power infrastructure scale and the ceaseless growth of project funding, problems such as the opaque flow of capital, the arrears of workers’ wages and project funds, and the lack of a sound management mechanism become more and more serious. Therefore, a credit management method for infrastructure contractors of power systems based on blockchain is proposed. First, a blockchain-based framework of contractor credit management is designed for solving the infrastructure problems above. On this basis, a credit evaluation system is constructed for infrastructure contractors of power systems. Then, a credit behavior analysis method based on cross-chain technology and the credit result query mechanism based on proxy signature is proposed to realize the data interaction and the credit result query. Finally, the effectiveness and accuracy of the proposed method are illustrated by the case study. The simulation results show that the method proposed in this work can effectively reflect the credit status of power infrastructure contractors and the wages payment has the highest weight of 0.2240 among all the third level indicators with its obvious important role in power infrastructure management.

With the promotion of carbon peaking and carbon neutrality goals and the construction of renewable-dominated electric power systems, renewable energy will become the main power source of power systems in China. Therefore, ensuring frequency stability and system security will emerge as pivotal challenges in the future development process. Created by combining a Li-ion battery and a supercapacitor, a hybrid energy storage system (HESS), which possesses robust power regulation capabilities and rapid response capabilities, holds promise for supporting the frequency stability of power systems. In this context, the assessment of the economic viability of HESSs providing multitype frequency response services becomes a critical factor in their deployment and promotion. In this paper, an economic analysis approach for a Li-ion battery–supercapacitor HESS towards a multitype frequency response is presented. First, a multitype frequency response-oriented operational mode for the HESS is designed, outlining the roles and functions of the Li-ion battery and the supercapacitor in delivering distinct services. Moreover, building upon the analysis of the power trajectory of Li-ion batteries, a lifetime model for the HESS is proposed based on the rain-flow counting method. Furthermore, considering the competitive landscape for the HESS in the frequency regulation ancillary service market, a full lifecycle economic assessment model is proposed. Finally, case studies on actual power system frequency data and PJM market data are performed to verify the effectiveness of the proposed method, and the simulation results confirm that the HESS exhibits robust performance and a competitive advantage in providing multitype frequency response services. Additionally, it demonstrates commendable economic benefits, establishing its potential as a valuable contributor to frequency response services.

China has promised to peak carbon emission before 2030 and to achieve carbon neutrality before 2060 (i.e., “double carbon” goal). Under this background, the emission reduction transformation path of China's electric power industry is studied in this paper. First, several boundary conditions (i.e., assumptions) of electric power structure transformation (i.e., the costs of power generations, the costs of energy storage systems, the developments of carbon sinks, the emission factors, and the quotas of carbon sinks) are given considering the whole society electricity consumption in the future. Second, a transformation path optimization model is established aim to minimize the total cost in the electric power industry. Then, according to the optimization results, the transformation predictions for the power industry under the ''30·60 scenario'' (i.e., the scenario that can achieve carbon peak and carbon neutrality before 2030 and 2060) are analyzed in detail, and are compared with the ones of ''2 °C scenario'' and ''1.5 °C scenario'' defined by IPCC. Furthermore, the influence of different carbon prices on the transformation path is also analyzed. It can be concluded from the simulation results that the ''30·60 scenario'' is a scenario between ''2 °C scenario'' and ''1.5 °C scenario'', and carbon emission can be reduced rapidly under the guidance of high carbon prices.

The long-term three-phase imbalance in distribution systems can lead to increased energy losses during transmission, reduced efficiency in energy utilization, and have serious implications for power supply security, power quality, and economic operations of the power system. The automatic load commutation device is an automatic device designed to address the issue of three-phase load imbalances in low-voltage distribution networks. By adjusting the phase of users without interrupting the power supply, this automatic device evenly distributes the load across all phases and effectively resolves the three-phase imbalance problem. Therefore, aiming at the above issue, a robust optimization method is adopted to address the control problem of automatic load commutation devices for a low-voltage distribution network. First, using historical data from photovoltaic and wind power generation systems as well as user load, a forecast analysis is conducted and uncertainty models for renewable energy and load demand are established. Then, a robust control strategy for the automatic load commutation device is proposed, which considers uncertainties of both source and load using the robust optimization method. The proposed model is then linearized using second-order cone technology and strong dual theory, and the column-and-constrained generation (C&CG) algorithm is employed to solve the problem iteratively. Finally, a modified IEEE 33-bus system is taken to verify the effectiveness of the proposed strategy. The simulation results show that the proposed load commutation device robust control model can enhance the ability of the distribution network to respond to load demand and renewable energy fluctuations while ensuring the economic operation of the distribution network. In addition, by adjusting the deviation amount and uncertainty parameters of load demand and renewable energy, a good balance between the robustness and economy of the proposed model can be achieved.

Abstract Xinjiang Uygur Autonomous Region (XUAR) covers one of the largest drylands in the world, while the relative effects of different environmental factors on plant diversity are poorly understood. We sampled 66 sites in a typical dryland of XUAR, which covers more than 450,000 km2, to evaluate the relative influences of different factors on the patterns of local plant species richness (LPSR). We found that overall and herbaceous LPSR were positively correlated with water availability, soil nutrients but negatively correlated with energy availability, while the shrub LPSR showed the opposite response. Climate, soil attributes together explained 53.2% and 59.2% of the variance in overall and herbaceous LPSR, respectively; revealing that LPSR patterns were shaped by abiotic and underground biotic factors together. Only 31.5% of the variance in the shrub LPSR was explained by soil attributes, indicating that shrub LPSR was mainly limited by non-climatic factors. There findings provide robust evidence that relative contribution of climate and soil attributes differ markedly depending on the plant functional group. Furthermore, we found the different relationship between microbes and plant diversity, indicating that the linkages between soil microbial diversity and plant diversity may vary across functional groups of microbes and plant. These findings provide robust evidence that the relative roles of climate, soil and microbes differ markedly depending on the plant functional group. Microbial richness showed a significantly pure influence on the LPSR of all groups, suggesting that microbes play a non-negligible role in regulating plant diversity in dryland ecosystems.

Water sources from the lower reaches of the Heihe River northwest China, located in an arid area impacted by environmental stresses, have promoted changes to the local soil and plant conditions; however, our understanding of variations and drivers of soil bacterial communities in an arid inland river basin remains unclear. Therefore, we collected 39 soil samples from a riparian oasis zone (ROZ) to the circumjacent desert zone (CDZ) at the lower reaches of Heihe River to evaluate bacterial communities based on the 16S rRNA gene data. We found that the bacterial community composition differed between ROZ and CDZ habitats, with significantly higher relative abundance of the phyla Gemmatimonadetes and Acidobacteria in ROZ, whereas the abundance of the phyla Actinobacteria and Deinococcus–Thermus was greater in CDZ. The difference in the bacterial community was almost entirely generated by the species turnover rather than the nestedness among all samples. In addition, we found that bacterial α-diversity index showed no significant difference between ROZ and CDZ habitats. The distance-decay analysis showed that spatial distance, plant community, soil property, and plant functional trait were correlated with bacterial community variations. However, the variation partition analysis (VPA) revealed that both soil properties and plant community strongly explained the difference [such as soil water content (WC), soil silt content, and plant community structure] compared with plant functional traits in bacterial β-diversity and species turnover. Based on a co-occurrence network analysis, we found that the bacterial network of ROZ, which had more negative correlations, higher average connectivity, shorter average path length, and smaller modularity, was more complex than the network of CDZ. This suggested that the bacterial community was more stable and less vulnerable to change in the ROZ habitat than in the CDZ habitat. Overall, our findings suggest that the heterogeneity of soil properties and plant community collectively affect the structure of the soil bacterial community in an arid inland river basin. However, the influence of plant functional traits on the variation of the bacterial community depends on soil properties and plant community.

A large amount of wind turbine power and photovoltaic power is abandoned in many areas with abundant renewable energy due to thermal-electric coupling, inadequate local consumption capacity, and limited capacity of transmission lines, etc. To solve the above problems, a coordinated dispatching method for integrated energy systems is proposed in this paper. Firstly, the spatiotemporal characteristics of diversified loads in multiple functional areas are introduced, including the inertia and elasticity of heating/cooling loads, the spatiotemporal distribution of electric vehicles, and the optimum transmission distance of diversified loads, etc. Secondly, a coordinated dispatching model of integrated energy systems is proposed, which considers the differences of multiple functional areas and various forms of energy systems. Finally, an actual distribution system in Jianshan District, Haining, Zhejiang Province of China is investigated for demonstrating the effectiveness of the proposed model. The results illustrate that the proposed model could effectively improve the consumption rate of renewable energy and reduce the volatility of renewable energy by considering the coordination of electric vehicles, tie lines, and heating/cooling systems in multiple functional areas.

A novel inwardly off-center shearing jet-stirred reactor is proposed and examined computationally. The inwardly off-center shearing jet-stirred reactor is compared with two traditional jet-stirred reactor designs, the outward cross-injector jet-stirred reactor and the concentric inward and outward jet-stirred reactor. The results show that the present inwardly off-center shearing jet-stirred reactor has significant improvement in terms of mixture uniformity and residence time distribution. Numerical results show the distributions of residence time in the two traditional jet-stirred reactors are wide and long tailed because of the formation of large and stable vortices, and the corresponding mean residence time of the two classical jet-stirred reactors deviates from the theoretical value by about 20%, while the new inwardly off-center shearing jet-stirred reactor has a much narrower residence time distribution and reduces the deviation to 8%, mainly attributed to the smaller vortices generated by an optimized jet arrangement. Moreover, the new inwardly off-center shearing jet-stirred reactor provides a fully optic-accessible platform for kinetic studies of alternative and real jet fuels. [ABSTRACT FROM AUTHOR]

Dryland ecosystems cover more than 30% of the terrestrial area of China, while processes that shape the biogeographic patterns of bacterial and fungal α-diversity have rarely been evaluated synchronously. To compare the biogeographic patterns and its drivers of bacterial and fungal α-diversity, we collected 62 soil samples from a typical dryland region of northwest China. We assessed bacterial and fungal communities by sequencing bacterial 16S rRNA gene and fungal ITS data. Meanwhile, the α-diversity was decomposed into two components: species replacement (species turnover) and nestedness to further explore the bacterial and fungal α-diversity patterns and its causes. The results show that both bacterial and fungal α-diversity were derived almost entirely from species turnover rather than from species nestedness. Distancedecay relationships confirmed that the geographic patterns of bacterial and fungal α-diversity were significantly different. Environmental factors had the dominant influence on both the bacterial and fungal α-diversity and species turnover, however, the role of geographic distance varied across bacterial and fungal communities. Furthermore, both bacterial and fungal nestedness did not significantly respond to the environmental and geographic distance. Our findings suggest that the different response of bacterial and fungal species turnover to dispersal limitation and other, unknown processes may result in different biogeographic patterns of bacterial and fungal α-diversity in the drylands of northwest China. Together, we highlight that the drivers of α-diversity patterns vary between bacterial and fungal communities, and microbial α-diversity are driven by multiple factors in the drylands of northwest China. [ABSTRACT FROM AUTHOR]