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A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

The accumulation of snow is one of the essential reasons for the significant decrease in the power generation efficiency of PV stations in winter. For this reason, this paper firstly provides a detailed review of the current research into the physical processes and shedding reasons for the natural melting of snow on PV module surfaces. Then, several common techniques to promote snow and ice melting on PV surfaces are discussed, and the feasibility and shortcoming of ultrasonic technology in removing high‐density snow and ice are analyzed in detail. Finally, this paper proposes two snow sensing technologies for PV modules, namely, image processing method and interdigital capacitance method. Among them, the image method based on fixed camera and UAV detection is more suitable for wide area PV power stations, while the interdigital capacitance method is helpful in obtaining snow characteristic parameters, such as icing type and LWC. The relevant research has certain reference significance for PV power stations to formulate a more reasonable snow removal plan and power generation forecast. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: Coal‐fired thermal power plants play important roles in daily life but are vulnerable to earthquakes. This paper presents a novel cable‐damper stopper with linear damping and carries out a series of shaking table tests to evaluate its performance on a 1/25‐scaled coal‐fired thermal power plant. The stopper consists of a cable, a linear viscous damper and a steel block attached to the boiler. The seismic responses of the structure equipped with different sets of cable‐damper stoppers which are of different damping coefficients and diameters of cables were studied and evaluated, where three seismic ground motions with two intensities were selected as input motions. The obtained acceleration and displacement responses of the support structure and the boiler reveal that the stopper cable effectively restrains the relative displacement between the boiler and the support structure. Furthermore, it can be found that the damping affects the seismic protection performance of the stopper depending on the intensity of the boiler vibration. In the paper, the working mechanism of the stopper is also analyzed based on the test data. It turns out that the stopper is more effective when it works under severe earthquakes. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) can aggregate distributed energy resources (DER) and controllable loads to participate in the bidding of electricity market and carbon trading market. However, the uncertainty of renewable power production brings high transaction risks to VPP. Given this background, this paper proposes a novel Wasserstein‐distance based two‐stage distributionally robust chance constrained (DRCC) bidding model for VPP participating in the electricity‐carbon coupled market. The uncertainties are modelled as an ambiguity set based on Wasserstein distance, in which the two‐sided chance constraints are guaranteed satisfied. In the first stage, VPP's revenue is maximized according to the forecast information. In the second stage, the re‐dispatch measures are determined to hedge against the perturbation of uncertainties under the worst‐case distribution within the ambiguity set. Finally, a reformulation approach based on strong duality theory and conditional value‐at‐risk (CVaR) approximation is proposed to transform the DRCC problem into a tractable mixed‐integer linear programming (MILP) framework. Case studies are carried out on the IEEE 30‐bus system to verify the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

To effectively utilize ventilation air methane (VAM), it is proposed to pass it into the boiler of the coal‐fired power plant for mixed combustion. However, the different types of thermal coal utilized present distinct characteristics when mixed with VAM. In this paper, lignite, bituminous coal, and anthracite are selected to study the CH4 conversion rate, system ignition temperature, and NO emission characteristics of the VAM‐pulverized coal coupled system on a fluidized bed experimental platform. The experimental results show that the ignition temperatures of VAM are 748, 736, and 732 °C when the CH4 concentration is 0.25, 0.5, and 0.75%. After the addition of thermal coal, the ignition temperature decreased significantly. When the CH4 concentration is 0.25%, the ignition temperature of the lignite‐VAM system is the lowest, which is 450 °C. Anthracite has the strongest catalytic effect on CH4 combustion. When the heating rate is 5 °C /min, the reaction rate of CH4 is the fastest in the anthracite‐VAM coupled system. Under laboratory conditions, the peak NO concentration in the coal‐VAM coupled system was lignite > bituminous coal > anthracite at different CH4 concentrations, and the CH4 in VAM had a reducing action on the NO generated during mixed combustion, with a stronger reducing effect observed as the CH4 concentration increased. The results of this study can lay the foundation for the industrial application of the mixed combustion of VAM and thermal coal, and be of great significance for solving the practical problems caused by the change of boiler coal types. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A multi‐model predictive control strategy based on kernel fuzzy c‐means (KFCM) clustering and integrated model is proposed for the complex problem of rapid and accurate control of ammonia injection in selective catalytic reduction (SCR) denitrification systems of coal‐fired power plants under a wide range of variable load conditions. First, the SCR data samples are clustered using the KFCM clustering algorithm, and the number of clusters is determined by introducing the Xie‐Beni index. Second, the prediction model of the SCR denitrification system is established by an integrated modelling approach, and the sub‐learners of the integrated model are the genetic algorithm optimized back propagation (GA‐BP) neural network model and the least squares support vector machine (LSSVM) model. Third, a multi‐model prediction controller based on the particle swarm optimization (PSO) algorithm and the integrated model is designed and developed. To ensure the stability of the system, a model‐switching strategy based on the minimum Euclidean distance is proposed. Finally, simulation verification and industrial field application verification are fulfilled by comparing with proportion integral differential (PID) control and single model predictive control (MPC). The results show that the multi‐model predictive control method proposed in this paper can obtain higher control accuracy and better control stability and meet the control requirements for the long‐term operation of the SCR denitrification system. [ABSTRACT FROM AUTHOR]