Operation Of Power Plants Research Articles

Harmonic characteristics analysis and power quality requirements of Light-Emitting Diode lamps used in nuclear power plant lighting system

Abstract This article conducts an in-depth study of the application of LED (Light-Emitting Diode) lamps in nuclear power plant lighting systems and conducts a comprehensive analysis of its impact on the power quality of the plant’s low-voltage AC power system. First of all, through a detailed discussion of the harmonic characteristics of LED lamps and the power quality requirements of nuclear power plants, it is pointed out that the harmonics of LED lamp drivers will inevitably have an impact on the power quality of low-voltage AC power systems. Therefore, the harmonic characteristics of LED lamps must comply with certain constraints. Subsequently, the application of LED lamps under different capacity transformers was analyzed and combined with relevant standards and specifications, and application suggestions for LED lamps in nuclear power plant lighting systems were put forward. Finally, measures to improve the total harmonic distortion rate of LED lamp current were discussed, emphasizing the importance of power factor correction technology. In summary, this article provides important reference and guidance for the application of LED lamps in nuclear power plants, helping to ensure the safe and stable operation of nuclear power plants.

Optical microscopy of ceramic materials based on fly ash from thermal power plants

The data on the annual release of ash and slag waste as a result of the operation of thermal power plants (TPP) in Siberia and the Far East of the Russian Federation are presented. The main reasons for the insignificant use of ash in the construction industry (about 5-8% of the total waste output) and the problems hindering its use for the production of ceramic wall materials are considered. A brief description of the composition and properties of the raw materials used in this work for the manufacture of ceramic wall materials is given. The compositions of the developed fly ash-based charges and the method of manufacturing ceramic samples with a matrix structure are given. The results of the study of the structure of ceramic materials by optical microscopy are presented. It is established that during the firing process, a matrix (dispersion medium) is formed from the fusible shell of aggregated complexes, and granules from fly ash are transformed into cores (dispersed phase) of a ceramic matrix composite. It was revealed that the kernels have an oval shape due to the deformation of the granules during pressing. Clusters are formed in the nuclei, consisting of a large number of small crystalline grains connected by an amorphous substance and edged with thin chains of pores. It is shown that the developed pore space inside the cores is mainly represented by frost-proof and reserve pores, which ensures high frost resistance of ceramic samples based on fly ash.

Development of an integrated framework to implement the nuclear safety goals with various safety criteria

The question “How safe is safe enough?” has been raised since the start of nuclear power plant (NPP) operations in the 1950s, and to address this issue, the Nuclear Regulatory Commission (NRC) of the United States (U.S.) introduced the concept of safety goals in the mid-1980s. In the U.S., the safety goal is called the 0.1 % rule because it requires that the additional risk imposed on individuals and society by the operation of a new NPP should not exceed one-tenth of one percent (0.1 %) of the total risk resulting from other factors to which members of the U.S. population are generally exposed. Nordic countries Sweden and Finland have taken a different approach to setting safety goals for NPPs than the U.S. by introducing requirements for the amount and frequency of cesium-137 (Cs-137) releases. In the case of the Republic of Korea, the regulatory body introduced nuclear safety goals following amendments to the Nuclear Safety Act in 2016. Korea adopted the same safety goal of the 0.1 % rule of the U.S. and simultaneously introduced the Cs-137-related safety goal, which requires that the total frequency of NPP accidents in which the release of Cs-137 exceeds 100 TBq is less than or equal to 1.0E-6 per year. The latter was introduced as a safety goal to protect the environment. Furthermore, while Canada, which has also formally adopted the Cs-137-related safety goal, and Finland require this goal to be applied only to new NPPs, the Korean regulatory body requires the Cs-137-related safety goal to be applied to both operating and new NPPs. Thus, various safety criteria are used in the Korean nuclear safety goals. The use of various safety criteria as in the Korean nuclear safety goals might cause several issues in the practical application of those safety criteria. In this paper, I examine these issues from three main perspectives: (1) consistency, (2) assessment framework, and (3) practical application. I propose several methods to resolve these issues, and also recommend a new integrated framework to implement the safety goals with various safety criteria based on the proposed methods.

Theoretical modeling and investigation of the influence of deaerator on the transient process in power plants

Deaerator is one of the most important equipment for steady state and dynamic operation of power plants. The deaerator energy storage utilization process is one of the most essential ways to enhance the variable load rate of power plants. The purpose of this study is to improve the dynamic simulation performance of the deaerator during unit load changes by constructing a more reasonable deaerator model, aiming to provide guidance for practical operations. In this paper, a thermal mass microelement algorithm is proposed for the heat transfer between droplets and steam in the deaerator, followed by segmental modeling of the deaerator. By comparing with the operation data of a power plant, the steady state operation error of the deaerator model is within 0.6 %. Subsequently, the unit load variation process is simulated and the dynamic variation accuracy of the model proposed in this paper is enhanced by 1–2 % compared to the lumped parameter model. The dynamic characteristics of the deaerator are obtained by simulating the step and ramp changes of the deaerator boundary conditions and the deaerator start-up process. In addition, during the simulation of condensate throttling, the maximum power of the unit using the deaerator model in this paper is 0.2–1.5 MW larger than that of the lumped parameter model.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Experimental analysis on improving heat storage efficiency of high-temperature packed bed system using spherical capsules filled with MgCl2–KCl–NaCl/Al2O3 nanoparticles composite phase change material

While thermal energy storage (TES) systems are becoming crucial to the stable operation of solar power plants, molten salt packed bed thermal energy storage (PBTES) systems attracted widespread attention due to their wide operating temperature range and low cost. However, achieving high-efficiency thermal storage systems is still a datum challenge to meet the higher energy demand, as well as sustainable development and utilization of renewable energy systems. This study developed spherical capsules employing nanocomposite salts (MgCl2–KCl–NaCl/Al2O3 nanoparticles) as composite phase change materials to experimentally study the heat storage process of a high-temperature PBTES prototype experimental system. Also, the heat storage performance of the PBTES system under different operating conditions is further investigated. The results showed that the nanocomposite salt gradually melted along the flow direction of the heat transfer fluid by exhibiting three-stage characteristics including solid phase sensible heat, phase change latent heat, and liquid phase sensible heat. Also, the temperature difference inside and outside the capsules showed the characteristics of peak-trough-peak. The PBTES system reached a heat storage density of 1290.51 MJ m−3 at a thermal efficiency of 86.95 %. The average heat storage rate increased by 49.55 % and the heat storage time was shortened by 29.67 % when the blower speed was increased from 800 r·min−1 to 1200 r·min−1. The experimental results implied that the PBTES system encapsulating nanocomposite salts has excellent thermal performance. This study demonstrated solving the problems of insufficient thermal energy supply in time or space and improving energy utilization could result in a high-efficiency thermal storage system.

Joint bidding strategy of multi‐virtual power plant operators

AbstractThe distributed generation of new energy can effectively use the local endemic clean new energy and provide green power. However, due to the small scale and scattered layout of distributed generation resources, it is difficult to participate in the economic dispatch of power systems and even the competition of the power market. Virtual Power Plant (VPP) can act as a carrier of Distributed Energy Resource (DER) to manage its internal energy, to carry out combined bidding in the day‐ahead energy market and the regulation market. When virtual power plants coordinate multiple distributed energy resources to participate in electricity market transactions, the degree of disclosure of their internal privacy information will produce different trading strategies. A Stackelberg game model between multiple virtual power plant aggregators and virtual power plant operators is constructed, and the Kriging model is combined to protect the privacy information in the transactions of virtual power plant operators. Energy management for distributed generation is carried out, and the profit situation of virtual power plant operators under different strategies is analyzed.

A Master–Slave Game Model of Electric Vehicle Participation in Electricity Markets under Multiple Incentives

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity.

An estimation method for bias error of measurements by utilizing process data, an incidence matrix and a reference instrument for data validation and reconciliation: Part 2 Extension to the energy balance relation

Abstract Ensuring data reliability is becoming increasingly important for further applications of AI, IoT, and digital twins. One promising technology for ensuring data reliability is data validation and reconciliation (DVR), which minimizes the uncertainty of measurements based on statistics. DVR has been widely used for the operation and maintenance of nuclear power plants in Europe and the United States in recent years. The most important input for DVR analysis is measurement uncertainty. The catalog value provided by sensor manufacturers includes the measurement uncertainty, but in reality, the actual measurement uncertainty is often smaller. Previous studies have proposed several methods for evaluating the actual measurement uncertainty based on process data, which have been confirmed to be effective for evaluating random errors. It is important to note that bias errors also contribute significantly to the measurement uncertainty. In our previous paper, we proposed a method for estimating bias error using process data, an incidence matrix, and a reference instrument. The proposed method was limited to a mass balance relation, i.e., flow rate measurements. In this paper, we extend the method to include an energy balance relation by considering energy conservation in addition to mass conservation. This extension enables the evaluation of measurement uncertainty for flow rate and temperature. The proposed method was validated with two benchmark problems. It was found to be applicable to various flow conditions, including physically fluctuating flow, such as that observed in the feedwater flow in nuclear power plants.

Fixed-time fractional-order sliding mode controller with disturbance observer for U-tube steam generator

Water level control of a U-tube steam generator plays a critical and challenging role in ensuring safe and reliable operation for a pressurized-water reactor-type nuclear power plant, as both excessively high and low water level in the U-tube steam generator would affect the normal operation of the nuclear power plant and even lead to unplanned shutdown events. This work presents a fixed-time fractional-order sliding mode water level controller coupled with a fixed-time disturbance observer, aiming to further improve the water level control performance under full operating conditions, considering uncertainties and actuator saturation. The proposed scheme is formulatedbased on the popular Irving water level model with uncertainties and the pre-existing actual water level control framework, while at the same time incorporating the benefits of the state-of-the-art anti-windup techniques, disturbance observer-based feedforward compensation techniques, fractional-order calculus, sliding mode control techniques, and fixed-time stability theorem, such that the transient water level response and steady-state control accuracy can be further enhanced even in the presence of uncertainties and actuator saturation phenomenon. It is theoretically proved that under the proposed scheme, both the estimation error of the uncertainties and the water level control error can be driven to zero within a fixed time regardless of their initial values by Lyapunov’s theorem. Meanwhile, simulation studies, involving the comparisons with a real-world adopted water level controller and a pre-existing integer-order sliding mode water level controller coupled with an uncertainty estimator, reveal the feasibility and superiority of the proposed approach.

Optimizing Asset Management: Comprehensive Analysis and Innovative Strategy Design to Increase the Effectiveness of Life Cycle Delivery at PT Pembangkitan Jawa-Bali Services

Asset management is the art and science of making the right decisions and optimizing the delivery of value from organizational assets. ( Institute of Asset Management , 2010). PT PLN Pembangkitan Jawa Bali Services, which is often abbreviated as PT PJB Services, is a subsidiary of PT Pembangkitan Jawa-Bali (PT PJB) . This company operates in the field of power plant operation and maintenance services, as well as supporting services related to power plants. The company has a strong focus on asset management, given its role in managing and maintaining critical power generation assets. This research examines the implementation of multidimensional strategies in optimizing asset management at PT Pembangkitan Jawa-Bali Services (PJB Services). The main focus of the analysis is to test the variables that influence the success of asset performance at PT Pembangkitan Jawa Bali Services based on group 3 ( Life Cycle Delivery ) consist of 11 key dimensions of asset management: Technical Standards & Legislation , Asset Creation & Acquisition , Systems Engineering , Configuration Management , Maintenance Delivery. Reliability Engineering, Asset Operation, Resource Management, Shutdown & Outage Management, Fault & Incident Response, Asset Decommissioning & Disposal. The main aim of the research is to design an integrated approach to improve the effectiveness of corporate asset management. The method used in this research is the Structural Equation Model with the Partial Least Square approach. The research results revealed that the accepted hypothesis is H1, H2, H3, H4, H9, H10, H12, H16, H17, H18, and H19 are acceptable because they have t-statistic values > 1.96 and p-values < 0.05. Meanwhile, the hypotheses H5, H6, H7, H8, H11, H13, H14, and H15 were rejected because they had t-statistic values > 1.96 and p-values > 0.05.

Influence of the Surrounding Environment Steam Power Plant (PLTU) Kema Satu Village, Kema District, North Minahasa District

In today's era, it is important to understand the impact of Steam Power Plant (PLTU) operations on the environment and economy around the plant site, especially in Kema Satu Village. PLTUs, which harness the kinetic energy of steam from geothermal heat, are widely used due to their ease of operation and accessibility of fuel. However, they also have significant impacts, both positive and negative, on local communities. This study aims to assess the environmental and economic impacts of PLTU Sulut 3 on the surrounding area, evaluate its operational efficiency, and determine its contribution to stabilizing and improving electricity supply in North Sulawesi and Gorontalo. The research method used was a combination of qualitative and quantitative techniques to ensure a comprehensive analysis. Data was collected through observation, interviews, and analysis of relevant secondary data. The results show that the PLTU in Kema Satu Village has significant impacts on the local environment, including air and water pollution, soil degradation, and ecosystem disruption. In addition to providing electricity supply and employment opportunities for the local community, the PLTU has also triggered property price inflation and shifts in the local economy. Other negative impacts include damage to the local agricultural sector and increased greenhouse gas emissions. The implications of this study emphasize the need for careful planning, community involvement, and the implementation of sustainable practices to mitigate negative effects on residents and the environment.

Evaluation of return cooling water reuse in the wet cooled power plant to minimise the impact of water intake and drainage

The treatment and reuse of industrial water is a matter of great interest, given the severe shortage of water resources. It is widely acknowledged that power plants, particularly wet-cooled power plants, consume a significant amount of water. Therefore, research on the circulating cooling system (CCS) and the treatment of the return cooling water is of utmost importance. This study examined data on CCS inlet (water body), make up, and cooling water parameters, calculated a water balance, and predicted the hydrochemical quality of cooling tower blowdown water (CTBD). The study was conducted for an operating power plant to propose an effective treatment scheme for the return of CTBD to the process cycle. Additionally, it evaluated the possibility of recycling CTBD in the clarifier through laboratory modeling. Therefore, based on the results of this study, an effective treatment scheme is proposed to return CTBD to the technological cycle to ensure environmental sustainability. Recycled water was used as the water supply for the cooling tower, and a lime softening treatment was applied to clean the CTBD. According to the results, it is possible to recover quality indicators from a portion of the return cooling water after lime softening treatment. The novelty of this study lies in the successful demonstration of a lime softening regime that enables the blending of up to 25 % CTBD with make up water, without necessitating an increased CaO dose. This regime maintains TH levels at 2.2 mg-eq/dm³ and ensures that the discharge velocity of 0.7 m/s remains within regulatory standards. Additionally, it maintains standard concentrations of chloride (Cl⁻) and sulfate (SO₄²⁻) in CTBD at 200 mg/dm³ and 250 mg/dm³, respectively.

Environmental impacts generated by the hydroelectric power plant El Quimbo - Colombia. An ex-post evaluation

Introduction: Hydroelectric power plants are the most demanded source of electrical energy worldwide after the year 2000. Objective: This article identified the environmental impacts generated by the construction and operation of the El Quimbo hydroelectric power plant in the area of influence in southern Colombia from an ex-post perspective. Methodology: The study was of a mixed nature, under the deductive and descriptive method using information gathering techniques such as documentary analysis, open and participatory reconnaissance and the Delphi survey. Results: A total of eighty-eight (88) environmental impacts were identified in the physical, biotic and socioeconomic spheres. In the physical dimension, aspects such as decreased river flow and land flooding were found; in the biotic dimension, aspects such as increased displacement of terrestrial fauna and increased mortality of native species of terrestrial flora and fauna were identified; and in the socioeconomic field, decreased agricultural employment and increased cost of living were highlighted. Conclusions: We conclude with the identification of a greater number of negative impacts in the socioeconomic dimension compared to the impacts in the physical and biotic fields.

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

Digital engineering implementation in nuclear demonstration and nonproliferation projects at Idaho National Laboratory

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations.

Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach

This study presents a novel digital twin-based predictive modeling approach to enhance the operation, maintenance, and management of combined cycle power plants. The research aimed to accurately forecast the combined cycle power (CCP) output, a critical performance indicator, to support intelligent decision-making for plant operators and engineers. The proposed framework leverages a neural ordinary differential equation (NODE) model integrated within a digital twin architecture to capture the complex, nonlinear dynamics of combined cycle gas turbine unit (CCGU) system. The predictive model was trained and validated using multivariate time-series data collected from a CCGU, achieving high accuracy with an R2_score of 0.993, a mean absolute percentage error of 0.325 %, and a root mean squared error of 1.518. Importantly, the NODE model demonstrated superior forecasting capabilities compared to traditional machine learning techniques. The digital twin (DT) concept enables seamless real-time data integration, physics-based models, and advanced data analytics to facilitate comprehensive CCGU lifecycle management. This novel approach provides operators with reliable, real-time insights to optimize plant performance, reduce maintenance costs, and improve overall sustainability. The generalization of the NODE model to an independent CCGU unit validated its applicability across diverse power plant configurations, highlighting the originality and practical value of the research. The key contribution of this work is the development of a digital twin-based predictive modeling framework centered on the innovative use of neural ordinary differential equations to enhance the operation and maintenance of critical energy infrastructure. This research advances the state-of-the-art in intelligent asset management for the built environment.

Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty

Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty

Exploring signal interactions in PWR nuclear power plants through noise-based signal processing

Nuclear power plants operate as complex, interconnected systems with tightly interlinked variables. Monitoring systems within such plants gather data from various process parameters. This paper systematically analyzes key signals in a typical PWR power plant. We develop a dynamic model of the PWR plant encompassing mathematical models for the reactor core, steam generator, pressurizer, turbine, and condenser. This model is rigorously validated against reference data. We use this model to analyze signal perturbations in hypothetical scenarios, employing both time-domain and frequency-domain analyses. Our results demonstrate that noise in one signal noticeably impacts others. Furthermore, the extent of this impact varies depending on the nature of their connection. Direct connections exhibit a higher susceptibility to noise-induced interference, whereas intermediate connections typically show a diminished impact. Through signal analysis, we identify correlated signal pairs, offering insights into processes during abnormal conditions. These findings have implications for enhancing the safety and reliability of nuclear power plant operations.

Harmonized control framework for integrated hybrid microgrid and virtual power plant operation

This paper presents a novel approach in the realm of electric vehicle (EV) applications and renewable energy-based distributed generation (RE-DG) by proposing a unified DC/AC hybrid microgrid (HMG) system tailored for poultry farms. The system aims to streamline design complexity, minimize component requirements, and champion sustainability by seamlessly integrating wind and solar photovoltaic (PV)-based DGs. An innovative EV-based virtual power plant (VPP) concept is introduced to mitigate power intermittency and eliminate the need for energy storage and extra charging stations. To ensure synchronized operation, a Harmonized Control Framework (HCF) is devised, capable of managing both grid-following and grid-forming operations while adapting to dynamic conditions and non-linear loads. Rigorous software and real-time Hardware-in-the-Loop (HIL) testing demonstrate the proposed microgrid's exceptional power transfer capabilities, improved harmonic performance, rapid settling times, and superior voltage/frequency regulation compared to the conventional double-stage synchronous frame (DD-SRF) control approach.