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Doxycycline (DOX) and zinc (Zn), are frequently detected in livestock manure. Untreated excrement carries a sizable load of DOX and Zn into the soil, exacerbating agricultural nonpoint source pollution. However, research on the effects of DOX and Zn on soil microbial diversity and the prevalence of resistance genes is limited. Through an 8-year field experiment coupled with 16S rRNA sequence analysis, we explored the dynamics of DOX and Zn contamination and their influence on soil microbial diversity and resistance gene expression. Our findings revealed a pronounced positive correlation between pig manure application and residual DOX and Zn levels in the soil, with the high pig manure) treatment resulting in increases of 16.42- and 1.14-fold in soil DOX and Zn levels, respectively, compared to those of the control. Soil DOX levels increased with pig and chicken manure application, whereas soil Zn levels showed divergent trends, increasing with pig manure application but decreasing with chicken manure application. As soil depth increased, the contamination risks from DOX and Zn in the soil decreased. Notably, soil DOX and Zn levels exhibited a significant positive correlation with Bacteroidota, Firmicutes, and Proteobacteria. The application of livestock and poultry manures amplified the abundance of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) in the soil, with higher ARG levels in pig manure-treated soil than in soil treated with chicken manure. Conversely, a greater abundance of MRGs was found in the soil treated with chicken manure than in the soil treated with pig manure. The results of this study on the DOX and Zn levels, microbial composition, and resistance genes with the continuous application of livestock and poultry manure in China will be highly valuable for the surveillance, maintenance, and biological removal of DOX and Zn composite pollution in manure.

Hybrid energy storage system (HESS) is an effective way to mitigate wind power fluctuations on multi-time scale, and can improve influence of large-scale grid-connected wind power on stability and reliability of power system. A novel methodology named zero-phase controlled auto-regressive integrated moving-average (CARIMA) filter is proposed to integrate HESS to smooth wind power fluctuations. First, a design method for zero-phase CARIMA filter is provided, and then used to determine grid-connected power for a wind storage system and size HESS. The reasons, direct current (DC) component caused by energy storage efficiency and grid-connected power delay caused by phase shift, for causing superfluous energy storage configuration are revealed. In addition, a nonlinear programming scheduling strategy considering battery degradation is proposed. Power imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is addressed. Furthermore, thermostatically controlled loads (TCLs) are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy storage. Finally, effectiveness of the proposed strategy is verified by a case study.

Frequency stability and security have been a vital challenge as large-scale renewable energy is integrated into power systems. In contrast, the proportion of traditional thermal power units decreases during the decarbonization transformation process, resulting in poor frequency support. This paper aims to explore the potential of frequency regulation support, dynamic assessment, and capacity promotion of thermal power plants in the transition period. Considering the dynamic characteristics of the main steam working fluid under different working conditions, a nonlinear observer is constructed by extracting the main steam pressure and valve opening degree parameters. The real-time frequency modulation capacity of thermal power units can provide a dynamic state for the power grid. A dynamic adaptive modification for primary frequency control (PFC) of power systems, including wind power and thermal power, is proposed and improved. The power dynamic allocation factor is adaptively optimized by predicting the speed droop ratio, and the frequency modulation capability of the system is improved by more than 11% under extreme conditions. Finally, through the Monte Carlo simulation of unit states of the system under various working conditions, the promotion of the frequency regulation capacity with high wind power penetration (WPP) is verified.

Abstract Due to the generation variability, the growing capacity of renewable energy has posed unprecedented challenges to ensure the security of power system operation. Here, a two‐stage strengthened distributionally robust optimization (DRO) scheme is proposed for theself‐scheduling of a combined heat and power virtual power plant (CHP‐VPP) over a coupled electric power network (EPN) and district heating network (DHN). The CHP‐VPP operator maximizes its profits in the day‐ahead market and minimizes its cost in the real‐time market under the worst‐case realization of the uncertainties. Instead of assuming that the uncertainties follow known probability distributions or confidence bounds, a strengthened ambiguity set based on moment information and Wasserstein metric is built to provide more accurate characterizations of the true probability distribution of uncertainties. In addition, in order to enhance the flexibility of the system, a HOMIE model considering indoor activities and outside temperatures of each building is built to satisfy the comfortable indoor temperature. To make the whole problem tractable, linearisation and duality theory are adopted, and then a tailored column‐and‐constraint generation algorithm is developed to solve the problem. The validity and applicability of the strengthened DRO scheme are verified by an IEEE 33‐bus EPN and 14‐node DHN.

Wind power curve modeling is essential in the analysis and control of wind turbines (WTs), and data preprocessing is a critical step in accurate curve modeling. As traditional methods do not sufficiently consider WT models, this paper proposes a new data cleaning method for wind power curve modeling. In this method, a model-data hybrid-driven (MDHD) outlier detection method is constructed, and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model. Simultaneously, because the MDHD outlier detection method considers multiple types of operating data of WTs, anomaly detection results require further analysis. Accordingly, an expert system is developed in which a knowl-edgebase and an inference engine are designed based on the coupling relationships of different operating data. Finally, abnormal data are eliminated and the power curve modeling is completed. The proposed and traditional methods are compared in numerical cases, and the superiority of the proposed method is demonstrated.

To ensure carbon neutrality goal, coal-fired power plants will have to take responsibility of deep peak regulation, which is essential to the development of renewable energy power. The penalty for deep peak-regulating operation of coal-fired power units could be more than 40 g/kW h of the net coal consumption rate (NCCR), which is contracted to the adoption of renewable energy power. Hence, in order to identify promising energy saving directions, a distribution of exergy losses in thermodynamic systems of several typical large coal-fired power units is studied in this work. For reference, systematic investigations into related conventional energy saving technologies are conducted, which consider thermodynamic interactions among the components. As a result, three novel technologies are proposed to minimize the exergy loss of the units on the deep peak-shaving condition. Based on the performed feasibility study of these technologies, it is found that: (1) using novel steam governing method on the part-load conditions offers ∼5.5 g/kW h energy saving potential; (2) in the winter season, reducing the back pressure of a dry cooling unit from 15 to 4 kPa at partial load can reduce the NCCR by ∼15 g/kW h; and (3) adopting the efficient heat-supply technology based on pressure matching would provide a NCCR reduction potential of ∼6.2 g/kW h. These new solutions, due to their energy saving effect, positive environmental impact, and cost-effectiveness, forge a synergetic development path of coal-fired and renewable energy power generations under the carbon neutrality target.

With the increasing penetration of renewable energy in the power grid, which makes power plant equipment is always in changing operating conditions. The correlation between the main and auxiliary equipment of the unit is easy to lead a potential fault, therefore, the safety and reliability of the auxiliary equipment of thermal power units have become a more challenging issue in the case of large-scale renewable energy. Adaptive condition monitoring of the auxiliary equipment can reduce maintenance costs and improve reliability in the thermal power units. Most existing studies perform poorly at extracting features from distributed control systems data and make less use of time series data. A novel adaptive condition monitoring framework and early fault warning method based on long short-term memory and stack denoising auto-encoder network has been proposed for auxiliary equipment of power plant unit. The proposed framework has two main parts, which contain condition monitoring and adaptive early fault warning. The Mahalanobis distance of a reconstruction error is defined as the monitoring indicator to reflect the condition of the equipment. The Chebyshev inequality determines an adaptive threshold for early anomaly detection that applies to changeable working conditions. The effectiveness of the proposed method was verified by the actual case of the coal mill. The adaptive threshold method can obtain the advance time of 42s and 108s, respectively.

China has become the world’s largest producer and consumer of energy, and ranks first in its wind and solar power installation capacity. However, serious wind and solar curtailment in China has significantly hindered the development and utilization of renewable energy. To address problems in the consumption of renewable energy, this paper analyzes four key factors affecting the capacity of power generated from renewable energy sources: power balance, power regulation performance, transmission capacity, and load level. Focusing on these bottlenecks, we propose seven solutions: centralized and distributed development of renewable energy, improving the peak-load regulation flexibility of thermal power, increasing the proportion of gas turbines and pumped-hydropower storage, construction of transmission channels and a flexible smart grid developing demand response and virtual power plants, adopting new energy active support and energy storage, and establishing appropriate policies and market mechanisms. The Chinese Government and energy authorities have issued a series of policies and measures, and in the past three years, China has had remarkable achievements in the adoption of renewable energy. The rate of idle wind capacity decreased from 17% in 2016 to 7% in 2018, and that of solar decreased from 10% in 2016 to 3% in 2018.

A power source–power grid coordinated typhoon defense strategy is proposed in this study to minimize the cost of power grid anti-typhoon reinforcement measures and improve defense efficiency. It is based on multiagent dynamic game theory. This strategy regards a typhoon as a rational gamer that always causes the greatest damage. Together with the grid planner and black start unit (BSU) planner, it forms a multiagent defense–attack–defense dynamic game model naturally. The model is adopted to determine the optimal reinforcements for the transmission lines, black start power capacity, and location. Typhoon Hato, which struck a partial coastal area in Guangdong province in China in 2017, was adopted to formulate a step-by-step model of a typhoon attacking coastal area power systems. The results were substituted into the multiagent defense–attack–defense dynamic game model to obtain the optimal transmission line reinforcement positions, as well as optimal BSU capacity and geographic positions. An effective typhoon defense strategy and minimum load shedding were achieved, demonstrating the feasibility and correctness of the proposed strategy. The related theories and methods of this study have positive significance for the prevention of uncertain large-scale natural disasters.

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated, leading to great challenges in power systems. The renewable power curtailment is especially numerous in the integrated electricity-heat energy system (IEHES) on account of electricity-heat coupling. The flexible resources (FRs) on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper. On the energy supply side, three kinds of FRs based on electricity-heat coordination are modeled and discussed. On the load side, the shiftable electricity demand resource is characterized. On this basis, the solution for FRs participating in IEHES dispatch is given, with goals of maximizing the renewable penetration ratio and lowering operation costs. Two scenarios are performed, and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES. The contribution degrees of different FRs for renewable integration are also explored.

With the application of advanced information and communication technology in building cluster energy system (BCES), energy management based on two-way interaction has become an effective method to improve its operation efficiency. BCES can quickly respond to the mismatch between supply and demand by adjusting flexible load and system operation strategy, which can improve operation reliability and reduce energy cost. This paper proposes an energy management and pricing framework of BCES based on two-Stage optimization method. First, on the basis of profit-seeking modeling of energy service provider (ESP) and building clusters (BCs), a dynamic pricing decision-making framework for energy management in a hierarchical energy market is proposed, which considers both ESP’s energy supply income and BCs’ comprehensive benefit. The dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning algorithm is adopted to solve the MDP problem. Moreover, an operation optimization model of the BCES based on the obtained optimal price decision is established, and the established model is solved by the alternating direction multiplier method algorithm (ADMM). Through numerical simulation case studies, it is demonstrated that the proposed method can achieve the optimal pricing decision-making closer to the psychological needs of ESP and BCs, and can significantly reduce the cost of BCs and improve the operational efficiency of BCES.

To realize the lower carbon and more efficient operation of energy hubs in the joint electricity and carbon market, a day-ahead bidding strategy is proposed for the energy hub operator (EHO). Considering the uncertainties of prices, demands, and renewable energy sources, this strategy is formulated as a novel two-stage distributionally robust joint chance-constrained optimization problem. A total distance-based ambiguity set is proposed to preserve the mean value of uncertain factors. By introducing this indicator function, this problem is further reformulated as a mixed-integer linear programming (MILP) problem. Simulations are performed based on the electricity and carbon prices in Europe, and the relation between the carbon emission and operational cost is further investigated in the case studies.

Coal mills have a significant influence on the reliability, efficiency, and safe operation of a coal-fired power plant. Coal blockage is one of the main reasons for coal mill malfunction. It is highly essential to accurately detect the critical blockage in coal mills to ensure a safe and stable operation of the unit. Taking advantage of unsupervised learning methods and historical process data from distributed control systems (DCS), a stacked denoising autoencoder (SDAE) network-based approach for monitoring critical blockage in a coal mill is proposed in this work. The SDAE model with optimized parameters is applied to reconstruct the operating data during normal operating conditions. The intrinsic relationship between all input variables was captured by training a multilayer network model. The monitoring indicator was obtained from the reconstruction errors, and the threshold for monitoring indicators was obtained using kernel density estimation (KDE) during normal operation. The proposed network is independent of fault data, requires a reduced on-line calculation, and demonstrates a better real-time performance compared to conventional methods. The abnormal variables analysis may provide a theoretical evidence for critical blockage. The effectiveness of the proposed method is validated using operating data collected from an actual coal-fired power plant in China. The results demonstrated that the proposed method can effectively detect critical blockage in a coal mill and issue a timely warning, which allows operators to detect potential faults.

Coal particle size has a great influence on the flow field and combustion characteristics of a swirl burner, which is widely used in coal-fired boilers. As the combustibility and slagging tendency of a swirl burner has great impacts on the efficiency and safety of a boiler, a full burner complexity was considered in a computational fluid dynamics (CFD) research. As concluded from the reasearch results, the coal particle size affects the particle phase flow field conspicuously through in a complicated way. Not only the continuous phase flow is influenced, but the particle concentration and the temperature distribution are also influenced, which is thought to be important for the slagging tendency and corrosion. Besides, the studied swirl burner is found to have a very strong asymmetrical performance. The increment of coal particle size has a negetive effect on the following-up performance of the particle and the combustion performances of the burner, which declines the overall temperature by around 50 °C, consequently, the combustion efficiency and flame stability might also be degra

Background: Arsenic, a global pollutant and a threshold-free primary carcinogen, can accumulate in rice. Previous studies have focused on arsenic poisoning in drinking water and the effects on gut microbes. The research on arseniasis through food, which involves the bio-transformation of arsenic, and the related changes in gut microbiome is insufficient. Method: Mice were exposed from animal feed prepared with four arsenic species (iAsIII, iAsV, MMA, and DMA) at a dose of 30 mg/kg according to the arsenic species proportion in rice for 30 days and 60 days. The levels of total arsenic (tAs) and arsenic species in mice feces and urine samples were determined using ICP-MS and HPLC-ICP-MS, respectively. 16S rRNA and ITS gene sequencing were conducted on microbial DNA extracted from the feces samples. Results: At 30 days and 60 days exposure, the tAs levels excreted from urine were 0.0092 and 0.0093 mg/day, and tAs levels in feces were 0.0441 and 0.0409 mg/day, respectively. We found significant differences in arsenic species distribution in urine and feces (p

With the increase of wind power penetration rate in the power grid, power dispatching including wind curtailment often happens to guarantee the safety of the power system. Considering the fatigue load of wind turbines during de-loading operation, how to optimally dispatch them along with the required power instructions becomes a hot issue. Under this background, this paper mainly studies the active power dispatching based on fatigue load optimization. In order to reduce the calculation complexity of the fatigue load, this paper proposes a simplified active power dispatching model for wind turbines via a look-up table. Subsequently, the ultra-short-term wind speed prediction is performed using the least squares support vector machine for the dynamic's prediction of wind turbines, which will be applied in fatigue load pre-calculation. Moreover, in order to fast solve the optimal dispatching problem, the accelerated particle swarm optimization algorithm is adopted. Under different de-loading scenes, the simulation experiments show that the fatigue load of the wind turbines has certain regularity while different de-loading strategies yield different effects on the fatigue load. Finally, based on the evaluation of damage equivalent load (DEL), the proposed optimal dispatching strategy in this paper is proved to be effective to meet the external dispatching from the power grid while reducing the total fatigue load level of wind farm via optimizing the power instruction to each wind turbine.

At high latitudes, offshore wind turbines often face unfavorable loads by severe ice-induced vibrations during winter season, which may endanger facilities available on the platforms and degrade operating performance of wind turbine. Hence, it is vital to analyze the influences caused by ice loads. In this paper, based on a real-time simulation model, quantifiable analyses of performance losses due to ice creep loads are firstly studied deeply. Under ice creep loads, reliable pitch control is necessary to ensure safety and high-level power-tracking capability of modern offshore wind turbine. However, uncertain influences of ice creep loads are coupled with wind turbine operation and make it a challenge for wind turbine pitch control using traditional Proportional-Integral (PI) controller from the view of industry. As a result, improved pitch control using optimal gain-scheduling strategy is proposed to alleviate impacts of ice loads where the support vector regression algorithm is adopted to represent the strong nonlinear relationship among PI parameters under different operation conditions. For each operation point, PI parameters are optimally tuned by the particle swarm optimization algorithm. Finally, the presented nonlinear optimal gain-scheduling PI (OGS-PI) controller is applied on regulating generation power and reducing tower top displacement caused by ice creep loads based on software of Fatigue, Aerodynamics, Structures, and Turbulence, a high-fidelity wind turbine simulator. Simulation results show that unfavorable influence of ice creep loads to wind turbine operation can be significantly alleviated by the OGS-PI controller, which performs much better than the traditional PI controller.

For distributed energy system, multi-energy sources and their comprehensive utilization are popularly concerned nowadays. In this paper, optimal operation of a multi-energy complementary distributed combined cooling, heating and power (CCHP) system is deeply studied. This system is applied on a commercial building in Beijing and it has complex configuration including natural gas energy source, renewable energy source and different energy conversion devices. Meanwhile, a separated cooling, heating and power (SCHP) supply system is also proposed. Their optimal operation strategies and performance are compared to show the advantages of multi-energy complementary system. Through simulation, the results show that the multi-energy complementary system operates with great performance and the optimal operation strategy is effective.

The improvement of the operating flexibility of a thermal power unit has always been an essential issue due to its substantial support to power system stability. In addition, this issue has become increasingly important for the large-scale integration of fluctuant wind energy. The feedwater bypass (FWB) is an effective method for raising the load-following capability of a power unit by quickly activating its thermal storage system. However, understanding the dynamic characteristics of the FWB and designing appropriate control strategies for this method remain as challenges. In this paper, mathematical models that describe the static and dynamic characteristics of the FWB in unit power output are presented and discussed. Then, an optimized control strategy for improving a power unit's load-following capability is developed by combining FWB and the traditional coordinated control strategy (CCS). Last, field tests on a 300-MW power unit show that the unit ramp rate can be raised to twice that in traditional CCS using the improved strategy. Moreover, the strategy can be extensively used in coal-fired units for the flexible power regulation and the primary frequency control of power grids.

Owing to the advantages of burning low-quality coal (coal slime and coal gangue), furnace desulfurisation, low [inline-formula] emission and deep load adjustment, the circulating fluidised bed (CFB) combustion technology becomes one of the few fossil fuel utilisation technologies funded continuously by the Chinese government. However, compared with the pulverised coal boiler, the combustion process of CFB boiler is more complicated because of the larger time delay, significant uncertainty and more coupled variables. In this study, a data-driven proportional–integral-derivative (DD-PID) control strategy is presented for the combustion control of CFB boiler to improve the operating performance under full operating conditions. By analysing the running mechanism of combustion process, an inverse decoupler is introduced to transfer the combustion object to the generalised controlled object, which has relatively independent input–output relationship. After that, a normative procedure of DD-PID, including PID-parameter database establishment, information-vector neighbourhood selection, active PID-parameter determination, database update, and redundant vector deletion, is given. Finally, a series of case study, including numerical tests applied to the proposed combustion model and application test employed on 330 MW CFB simulation platform proves the feasibility of DD-PID control strategy.

Heating extraction steam (HEXTR) flow rate is the key parameter to determine the heat load of a combined heat and power (CHP) plant and the safe operation area of the steam turbine of CHP plant. Due to the difficulty of direct measurement, a soft measurement method of this flow rate is proposed. First, three calculation methods based on different principles are given: the Flügel formula of the steam turbine method, the butterfly valve flow characteristics method, and the improvement of heat balance characteristic of the turbine method. Then, a soft-sensing method through frequency complementary information fusion is proposed to combine the advantages of the three methods. The specific fusion algorithm uses Flügel formula of the turbine as a static model, the heat balance characteristic of the turbine to correct the coefficient in the model, and the butterfly valve characteristic to realize dynamic compensation. Finally, the proposed soft sensor is applied in the monitoring system of a typical 330 MW CHP plant. The actual operating data shows that the relative static measurement error of the soft sensor is less than 1% and the dynamic response is as fast as power load change.

In this paper, a wind-turbine active power control strategy is proposed from the cluster level to optimize the active power set-point for each wind turbine in a specific cluster. The wind turbine power tracking characteristic is described as an inertial link to establish the power tracking predictive model, and the model predictive control (MPC) method is used to optimize the cluster power demand and output. Time-varying constraints are proposed to coordinate the power output for different time-scale wind turbines and sustain that the cluster has enough fast-tracking capacity when the cluster power demand changes. Under different scenarios, the proposed strategy is tested to verify the effectiveness in improving the power output stability and frequency support ability.

This paper presents a non-linear dynamic model of a combined heat and power (CHP) unit with two-stage bypass for the first time. This model is derived through an analysis of the material and energy balance of the CHP unit. The static parameters are determined via the design data of the CHP unit, and the dynamic parameters refer to model parameters of same type of units in other references. Based on the model, an optimized control scheme for the coordination system of the unit is proposed. This scheme introduces a stair-like feedforward-feedback predictive control algorithm to solve the control problem of large delays in boiler combustion, and integrates decoupling control to reduce the effect of external disturbance on the main steam pressure. Simulation results indicate that the model effectively reflects the dynamics of the CHP unit and can be used for designing and verifying its coordinated control system; the control scheme can achieve decoupling control of the CHP unit; the fluctuation of main steam pressure is considerably reduced; and the adjustment of coal feed flow is stable. In this case, the proposed scheme can guarantee the safe, stable and flexible operation of the unit and lay the foundation for decoupling the heat load-based constraint of CHP units, thereby expanding the access space of wind power in northern China.

An optimal capacity allocation of large-scale wind-photovoltaic- (PV-) battery units was proposed. First, an output power model was established according to meteorological conditions. Then, a wind-PV-battery unit was connected to the power grid as a power-generation unit with a rated capacity under a fixed coordinated operation strategy. Second, the utilization rate of renewable energy sources and maximum wind-PV complementation was considered and the objective function of full life cycle-net present cost (NPC) was calculated through hybrid iteration/adaptive hybrid genetic algorithm (HIAGA). The optimal capacity ratio among wind generator, PV array, and battery device also was calculated simultaneously. A simulation was conducted based on the wind-PV-battery unit in Zhangbei, China. Results showed that a wind-PV-battery unit could effectively minimize the NPC of power-generation units under a stable grid-connected operation. Finally, the sensitivity analysis of the wind-PV-battery unit demonstrated that the optimization result was closely related to potential wind-solar resources and government support. Regions with rich wind resources and a reasonable government energy policy could improve the economic efficiency of their power-generation units.