Operation Of Power Plants Research Articles

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.

Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment.

Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation

Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge.

The main problem in the operation of nuclear power plants (NPPs) is the scale formation of mineral impurities in an open recirculating system (ORS). The discharge of water from an ORS into natural water bodies can alter the chemical equilibrium of wastewater components, necessitating continuous monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts (DIS) in water within an ORS during water treatment, using the Rivne Nuclear Power Plant (RNPP) as a case study. Moreover, the analysis impact of their discharge with return water in the Styr River. The DIS concentration has a significant impact on the efficiency of the system and the environmental of an ORS power plant. Altogether, each of the DIS components was analysed separately using the standard measurement methods, statistical methods of data processing and correlation analysis. In addition, the annual discharge of the DIS components was calculated, and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the impact of the formation of DIS and the variations in their concentration levels upon the discharge of wastewater into a natural water body were examined.

Optimal scheduling of virtual power plants considering integrated demand response

Abstract In contemporary power system management, leveraging multilevel demand response strategies plays a pivotal role in balancing energy supply with demand, thereby enhancing system efficiency and flexibility. This paper introduces an innovative optimization model for virtual power plant operations that anticipates the scheduling needs several days in advance. It incorporates a comprehensive approach to demand response on the consumer side, highlighting two main types: price-based and substitution-based responses. Furthermore, the model integrates the functionalities of energy storage and conversion devices. This integration aims to facilitate effective load management, ensuring an optimized load distribution while minimizing the overall system costs.

A comparison on the critical success factors of MSW and RDF power plant development in Thailand: Using an interpretive structural modelling and cross-impact matrix multiplication applied to classification analysis

Waste-to-energy (WtE) power plants, supplied mainly with municipal solid waste (MSW) and refuse-derived fuel (RDF), which convert waste into electricity, have emerged as a solution to Thailand's waste management problems. This study focused on identifying and studying the critical success factors (CSFs) that influence the success of MSW and RDF power plants in Thailand. This study employed interpretive structural Modelling and cross-impact matrix multiplication applied to a classification analysis to evaluate the impact of these CSFs on the development of WtE projects. The results showed that, for MSW, most CSFs were related to energy and waste management policies, followed by waste quality for electricity generation. In addition, strong financial resources and appropriate power plant locations are important for MSW management success. Conversely, for RDF, most CSFs were sufficient waste quality for electricity generation and performed well according to licensing conditions. In this study, high-level CSFs indicated that these factors were crucial for MSW and RDF development. CSFs differ based on specific technologies and regulations. However, sufficient waste quality (heating value and moisture content) is a common CSF in the MSW and RDF technologies. This study provides valuable insights into the CSFs that affect the development of WtE. Understanding and addressing these CSFs is essential for the development and operation of WtE power plants in Thailand and other countries with similar conditions. Thus, policy-makers and other stakeholders can make informed decisions to ensure the success of WtE projects.

Increasing variable renewables in coal-based energy systems under high electrification in the transport and heating sectors: The case of Kosovo

The increasing penetration of variable renewables poses major challenges in energy systems with limited flexibility. However, there are many solutions to support variable renewable integration when utilizing potential synergies among the different energy sectors. This research examines the technical and environmental effects of electrifying the transport and heating sectors in a coal-based energy system with limited flexibility, aiming to support the integration of variable renewable energy. Scenario analysis is employed to evaluate the use of heat pumps with thermal storage (power-to-heat) for individual heating solutions, electric vehicles, and Vehicle-to-Grid in the transport sector. These measures are evaluated as the primary means for enhancing the energy system flexibility to accommodate a larger share of variable renewables. Additionally, a dynamic analysis was conducted on thermal power plant operational capacities and efficiencies under varying electrification rates and renewable energy shares. The analysis is developed in the EnergyPLAN model using Kosovo energy system as a case study. The results confirm that the electrification of the heating and transport sectors positively impacts the integration of variable renewable energy by reducing critical excess electricity production and CO2 emissions, while power-to-heat and Vehicle-to-Grid facilitate synergies among different energy sectors. However, scenarios with high shares of variable renewable energy present challenges for the nominal operation of thermal power plants, potentially leading to lower operational efficiencies and slightly higher CO2 emissions compared to scenarios where thermal power plant part-load efficiency remains unchanged.

The application of time series deep learning model to the fast prediction of parameters in the MSLB accident

The Main Steam Line Break Accident (MSLB) threatens the safe operation of nuclear power plants. The transient safety parameters of the Passive Containment Cooling System (PCCS) in the MSLB accident are predicted by the time series deep learning model based on LSTM or RNN. In the dataset preprocessing, the linear normalization and two of feature label segmentation methods are used. The neural network models and the other models, namely the GBRT, RF and SVR, are used to construct the multi-parameter time series deep learning models to predict the transient safety parameters. The performance of the different models and the impact of two segmentation methods on models are compared, and it is obtained that the prediction accuracy of LSTM is higher than that of other models.

Carbon capture and co-pollutants in a networked power system

Abstract We evaluate how the availability of Carbon Capture (CC) in a networked electricity system affects the emissions of both carbon and of co-pollutants, under a range of plausible technical, economic, and policy scenarios about CC technology, the pace of renewable deployment, the structure of the power grid, and climate policy. We employ a Power Flow model of a three-node, mixed-source network in which fossil fuel power plants may invest in CC via retrofit. Our stylized model retains some of the complexities of a real power system while allowing for a detailed analysis of the impact of power plant operations and transmission constraints. We find that, in a networked system, the availability of CC may lead some generation to move from Natural Gas to Coal, thus leading to a significant increase in co-pollutants. This is of particular concern during the mid-transition, a period when both carbon and non-carbon electrical generation is active. The introduction of CC can lead to an increase in co-pollution even as the energy system transitions toward renewable energy and, surprisingly, co-pollution outcomes can be worse under a stronger decarbonization policy. This insight is important and timely in light of recent rules incentivizing the use of CC. Systems in the early stages of the energy transition may experience an increase in co-pollution if the co-pollutant dynamics are not considered in the first steps of CC policy design.

Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants

The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.

P2P Optimization Operation Strategy for Photovoltaic Virtual Power Plant Based on Asymmetric Nash Negotiation

Under the guidance of the “dual-carbon” target, the utilization of and demand for renewable energy have been growing rapidly. In order to achieve the complementary advantages of renewable energy in virtual power plants with different load characteristics and improve the rate of consumption, an interactive operation strategy for virtual power plants based on asymmetric Nash negotiation is proposed. Firstly, the photovoltaic virtual power plant is proposed to establish the optimal scheduling model for the operation of the virtual power plant, and then the asymmetric Nash negotiation method is adopted to achieve the fair distribution of benefits. Finally, the ADMM distribution is used to solve the proposed model in the solution algorithm. The simulation results show that the revenue enhancement rates are 28.27%, 1.09%, and 12.37%, respectively. The participating subjects’ revenues are effectively enhanced through P2P power sharing. Each subject can obtain a fair distribution of benefits according to the size of its power contribution, which effectively improves the enthusiasm of the PV virtual power plant to participate in P2P interactions and thus promotes the development and consumption of renewable energy.

A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps

Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.

A comprehensive dosimetry analysis of barakah nuclear power plant: Integrating gaseous and liquid radionuclide dispersion across multiple units

Monitoring radioactive releases during the normal operation of power plants is crucial to ensure compliance with safety limits set by regulatory bodies for environmental safety. In the case of the Barakah nuclear power plant in the UAE, comprehensive multiunit dispersion modelling and radiological safety analysis have been conducted. Utilizing the HotSpot Health Physics Code and GENII (Second-generation environmental dosimetry), assessments were made for both 37 gaseous and 51 liquid source terms generated by GALE. Release rates for source terms were determined using GALE based on APR 1400 specifications. HotSpot employed the Gaussian Plume model to simulate the dispersion of gaseous source terms up to 80 km surrounding the plant, calculating Total Effective Dose Equivalent (TEDE) and Committed Effective Dose Equivalent (CEDE) in rural and urban areas. Findings indicated that neither scenario exceeded the 1 mSv threshold for the general public nor the 20 mSv limit for operational workers. Notably, skin, thyroid, and surface bones exhibited the highest CEDE, primarily influenced by iodide radionuclides. GENII's Surface Water module modelled the effects of liquid source terms, accounting for various contamination and exposure pathways such as external exposure and ingestion across different age groups. The calculated doses remained well below FANR's annual limits, with negligible cancer incidences and fatalities predicted for one year of exposure.

Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant

This work presents an algorithm for estimation-based model predictive control with objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transitions between a bank of objectives. The control objective prioritization is cast in the framework of model predictive control that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state variables. The algorithm is applied to the challenging control problem of an industrial superheater (SH)-reheater (RH) system of a natural gas combined cycle plant under load following operation where smooth transitions among various control objectives is desired – operation under nominal conditions, avoidance of spraying to saturation at the inlet of the SH and RH systems, and avoidance of main steam temperature excursions. The results from the estimator framework are compared with the industrial data from an operating power plant. The control algorithm is evaluated by simulating a servo control problem and disturbance rejection scenarios as expected under load-following operation of the power plant. This algorithm is generic and can be applied to accomplish local control policies for safety, economics, quality control, state constraints, and others. Topical HeadingProcess Systems Engineering

Enhanced framework to mitigate maintenance strategy failure of electric power plant during crises

In engineering, a design is best described based on its alternative performance operation. In this paper, an electric power plant is analysed based on its effective operational performance even during critical situation or crisis. Data is generated and analysed using both quantitative and qualitative research approach. During maintenance operation of an electric power plant, some components are susceptible to wide range of issues or crises. These includes natural disasters, supply chain disruptions, cyberattacks, and economic downturns. These crises significantly impact power plant operations and its maintenance strategies. Also, the reliable operation of power plants is often challenged by various technical, operational, and environmental issues. In this research, an investigation is conducted on the problems associated with electric power plants by proposing a comprehensive and novel framework to maintenance the power plant during crises. Based on the achieved results discussed, the framework impact and contribution are the integration of proactive maintenance planning, resilient maintenance strategies, advanced technologies, and adaptive measures to ensure the reliability and resilience of electric power plant during power generation operations in the face of unforeseen challenges/crisis. Hypothetical inferences are used ranging from mechanical failures to environmental constraints. The research also presents a structured approach to ensure continuous operation and effective maintenance in the electric power plant, particularly during crisis (such as environmental issues and COVID-19 pandemic issues).

Radial reflector macroscopic cross sections preparation impact on the low neutron leakage core Beavrs

Full core nodal calculations are essential for safe nuclear power plant operation. The accuracy and compliance with the calculation and the experiment are based on the proper macroscopic data preparation model. While fuel assembly data preparation is usually based on the simple infinite lattice model simulation, the reflector data preparation is not standardised and the influence of the reflector macroscopic data on full core simulation is not quantified. The paper evaluates the large core with low neutron leakage behaviour with the different reflector cross section preparation models with respect to criticality and power distribution prediction. However, in opposition to the first insight, the different reflector model cannot increase the compliance with the power prediction near the fuel assembly reflector boundary. However, the usage of a special rehomogenization model can be helpful for pin power reconstruction compliance near the edge of the core.

Improving the Performance Management System for Power Plant Operators in Caprycoal Mining Company

Caprycoal Mining Company, located in East Kalimantan, Indonesia, strives to be a leading coal supplier. The performance management system of Caprycoal Mining Company for power plant operators is the subject of this research, which critically evaluates and suggests a redesign. It incorporates the Resource-Based View (RBV), Performance Management Theory, Goal-Setting Theory, and Management by Objectives (MBO), focusing on strategic resources, clear objectives, and structured management. Using Soft System Methodology (SSM) and semi-structured interviews, significant gaps were identified, such as the lack of Key Performance Indicators (KPIs) and mechanisms for managing competency data. Existing training programs were unstructured and misaligned with operational demands. The study proposes a comprehensive overhaul, including a structured KPI framework based on SMART goals aligned with organizational objectives and a digital competency management system for efficient data handling. Regular, structured feedback and advanced data analytics are emphasized for continuous performance monitoring. Additionally, a formal rewards program is recommended to motivate and retain high-performing employees by linking performance outcomes with individual and team rewards.