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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.