Enhancing Prediction Accuracy in the Evaluation of Power Plant Uprates Utilizing a Novel ‘Big Data’ Approach

The evaluation of power plant uprates has traditionally been based on the definition of several ‘typical’ operating modes based on historical data and a — more or less detailed — model of the plant that is compared in current configuration against the same base model including the modifications under consideration. For the economic assessment of the uprate, annual operating hours are allocated to the operating points, and fuel savings and/or additional output predicted by the model due to the modifications are evaluated against the expected investment cost. In this study, the authors demonstrate that this classic approach contains risks in several aspects, in particular: • the representativeness of the ‘typical’ operating modes, • the accuracy of the model, and • the correctness of the assumptions in the allocation of operating hours. Utilizing the example of an actual uprate of a heat recovery steam generator (HRSG) in a large utility plant of an Austrian steel company, a new approach for an evaluation based on ‘big data’ is presented that uses a full year of operational data in hourly granularity for both, the verification of the accuracy of the plant model, and the evaluation of the effect of the uprate. The authors also provide details of the underlying technologies that allow for both, excellent match of operational data with a fully-fledged heat balance software and fast evaluation of tens of thousands of calculation cases.

Evaluation of Flexibility Optimization for Thermal Power Plants

The increasing share of fluctuating renewable energy sources leads to changing requirements for conventional power plants. The changing characteristics of the residual load requires the conventional fleet to operate with higher load gradients, lower minimum load at improved efficiency levels as well as faster start-ups and provision of ancillary services. Despite the requirements from the electricity market, the value of improving those flexibility parameters is hard to evaluate for power plant operators. In order to quantify the additional benefit that can be achieved by improving flexibility parameters on a certain power plant in a changing market environment, an adjustable load dispatch model has developed for that purpose. Using past electricity market data, the model is validated for typical coal and a typical gas fired power plants by reproducing their operational schedule. In the next step, the model is used to apply parameter changes to the power plants specifications and economic effects are demonstrated. General statements are derived on which flexibility parameter needs to be improved on each power plant type. Furthermore, specific economic evaluations are shown for the reference power plants in order to present the ability of the developed tool to support investment decisions for modernization projects of existing power plants.

Analysis and Operation Optimization of Recompression Supercritical Carbon Dioxide Power Generation System Based on the Power Flow Method

Due to the peculiar physical properties, supercritical carbon dioxide (sCO2) is considered as a promising working fluid in power generation cycles with high reliability, simple structure and great efficiency. Compared with the general thermal systems, the variable properties of sCO2 make the system models obtained by the traditional modelling method more complex. Besides, the pressure distribution in the system will affect the distribution of the fluid properties, the fluid properties influencing the heat transfer process will produce an impact on the temperature distribution which will in turn affect the pressure distribution through the mass flow characteristics of all components. This contribution introduces the entransy-based power flow method to analyze and optimize a recompression sCO2 power generation system under specific boundary conditions. About the heat exchanger, by subdividing the heat transfer area into several segment, the fluid properties in each segment are considered constant. Combining the entransy dissipation thermal resistance of each segment and the energy conservation of each fluid in each segment offers the governing equations for the whole heat transfer process without any intermediate segment temperatures, based on which the power flow diagram of the overall heat transfer process is constructed. Meanwhile, the pressure drops are constrained by the mass flow characteristics of each component, and the inlet and outlet temperatures of compressors and turbines are constrained by the isentropic process constraints and the isentropic efficiencies. Combining the governing equations for the heat exchangers and the constraints for turbine and the compressors, the whole system is modeled by sequential modular method. Based on this newly developed model, applying the genetic algorithm offers the maximum thermal efficiency of the system and the corresponding optimal operating variables, such as the mass flow rate of the working fluid in the cycle, the heat capacity rate of the cold source and the recompression mass fraction under the given heat source. Furthermore, the optimization of the system under different boundary conditions is conducted to study its influence on the optimal mass flow rate of the working fluid, the heat capacity of the cold source and the maximum system thermal efficiency. The results proposes some useful design suggestions to get better performance of the recompression supercritical carbon dioxide power generation system.

Study of Surface Tension and Natural Evaporation of Aqueous Surfactant Solutions

Surface tension and solution evaporation of aqueous solutions of sodium lauryl sulfate (SLS), ECOSURF™ EH-14, and ECOSURF™ SA-9 under natural convection is examined through experimental methods. SLS is an anionic surfactant while EH-14 and SA-9 are environmentally-friendly nonionic surfactants. Surfactants are known to affect evaporation performance of solutions and are studied in relation to water loss prevention and heat dissipation. Surfactants could be useful under drought conditions which present challenges to water management on a yearly basis in arid areas of the world. Recent water scarcity in the greater Los Angeles area, south eastern Africa nations, eastern Australia and eastern Mediterranean countries has high cost of water loss by evaporation. Surfactants are studied as a potential method of suppressing evaporation in water reservoirs. Surfactants are also studied as performance enhancers for the working fluid of heat dissipation devices, such as pulsating heat pipes used for electronics cooling. Some surfactants have been shown to lower thermal resistances and friction pressure in such devices and thereby increase their efficiency. The static surface tensions of the aqueous-surfactant solutions are measured with surface tensiometer using Wilhelmy plate method. The surfactants are shown to lower surface tension significantly from pure water. The surface tension values found at the Critical Micelle Concentration are 33.8 mN/m for SLS, 30.3 mN/m for EH-14, and 30.0 mN/m for SA-9. All three surfactants reduced natural convection water loss over 5 days with SLS showing the greatest effect on evaporation rates. The maximum evaporation reduction by each surfactant from distilled water with no surfactants after 5 days is 26.1% for SLS, 20.8% for EH-14, and 18.4% for SA-9.

Case Study: Enhancing Human Reliability With Artificial Intelligence and Augmented Reality Tools for Nuclear Maintenance

Human error accounts for about 60% of the annual power loss due to maintenance incidents in the fossil power industry. The International Atomic Energy Agency reports that 80\% of industrial accidents in the nuclear industry can be attributed to human error and 20\% to equipment failure. The Personal Augmented Reality Reference System (PARRS) is a suite of computer-mediated reality applications that looks to minimize human error by digitizing manual procedures and providing real-time monitoring of hazards present in an environment. Our mission is to be able to provide critical feedback to inform personnel in real-time and protect them from avoidable hazards. PARRS aims to minimize human error and increase worker productivity by bringing innovation to safety and procedural compliance by leveraging technologies such as augmented reality, LiDAR, computer machine learning and particulate mapping using remote systems.

Potential Impacts of Net-Zero Energy Buildings With Distributed Photovoltaic (PV) Power Generation on the Electrical Grid

This study evaluates potential aggregate effects of net-zero energy building (NZEB) implementations on the electrical grid in simulation-based analysis. Many studies have been conducted on how effective NZEB designs can be achieved, however the potential impact of NZEBs have not been explored sufficiently. As significant penetration of NZEBs occurs, the aggregated electricity demand profile of the buildings on the electrical grid would experience dramatic changes. To estimate the impact of NZEBs on the electrical grid, a simulation-based study of an office building with a grid-tied PV power generation system is conducted. This study assumes that net-metering is available for NZEBs such that the excess on-site PV generation can be fed to the electrical grid. The impact of electrical energy storage (EES) within NZEBs on the electrical grid is also considered in this study. Finally, construction weighting factors of the office building type in U.S. climate zones are used to estimate the number of national office buildings. In order to consider the adoption of NZEBs in the future, this study examines scenarios with 20%, 50%, and 100% of the U.S. office building stock are composed of NZEBs. Results show that annual electricity consumption of simulated office buildings in U.S. climate locations includes the range of around 85 kWh/m2-year to 118 kWh/m2-year. Each simulated office building employs around 242 kWp to 387 kWp of maximum power outputs in the installation of on-site PV power systems to enable NZEB balances. On a national scale, the daily on-site PV power generation within NZEBs can cover around 50% to 110% of total daily electricity used in office buildings depending on weather conditions. The peak difference of U.S. electricity demand typically occurs when solar radiation is at its highest. The peak differences from the actual U.S. electricity demand on the representative summer day show 9.8%, 4.9%, and 2.0% at 12 p.m. for 100%, 50%, and 20% of the U.S. NZEB stocks, respectively. Using EES within NZEBs, the peak differences are reduced and shifted from noon to the beginning of the day, including 7.7%, 3.9%, and 1.5% for each percentage U.S. NZEB stock. NZEBs tend to create the significant curtailment of the U.S. electricity demand profile, typically during the middle of the winter day. The percentage differences at a peak point (12 p.m.) are 8.3%, 4.2%, and 1.7% for 100%, 50%, and 20% of the U.S. NZEB stocks, respectively. However, using EES on the representative winter day can flatten curtailed electricity demand curves by shifting the peak difference point to the beginning and the late afternoon of the day. The shifted peak differences show 7.4%, 3.7%, and 1.5% at 9 a.m. for three U.S. NZEB stock scenarios, respectively.

Harvesting Waste Thermal Energy From Military Systems

Military systems greatly depend on the availability of energy. This energy comes mostly in the form of burning fuel in order to produce mechanical work or producing electricity. The ability to extract the most out of these systems aligns with the current focus of energy efficiency, not only in the military, but in society at large. In this research, an infrared camera was used to create an infrared map to infer temperature differences on a gasoline-powered generator at steady state operations. These temperature differences were inputted into an experimental phase during which a digitally-controlled hot plate, water block, variable resistor, and digital acquisitions system were used to measure current output from a single TEG for loads of 1, 10, and 100 Ω, respectively. Data were analyzed and the correlation coefficients determined. These coefficients were modeled a single module and then various array configurations for TEGs in COMSOL. Using the findings, a single commercial 56 mm by 56 mm Be2Te3 TEG can yield 0.72 W of power. Simple calculations yield 72 W of power when 100 modules are joined in 10 sets coupled in parallel with each set containing 10 modules in coupled in series. This would require 560 mm by 560 mm or approximately 2 ft. by 2 ft. of system space to be covered.

A Novel Parameter for the Evaluation of Static Mixers

Static, or motionless, mixers are widely used in applications that involve chemical reactions, heat transfer, blending of fluids, or a combination of these. Within those applications, mixing can affect various parameters such as heat or mass transfer rates, process operating time, cost, safety, and product quality. Therefore, it is crucial to assess the performance of static mixers. In general, their performance is evaluated based on their ability to carry out mixing while minimizing energy loss. To accomplish this, a novel mixing parameter, the M number, is proposed and evaluated. The M number is a unitless parameter that describes the effects of the mixer using entropy change and pressure drop. The parameter is compared to another method of mixing evaluation that relies on Covariance (CoV) change across the mixer. Computational Fluid Dynamics (CFD) is executed using both methods to evaluate two static mixers and compare the results of each method. Potential applications for the M number are discussed and its limitations are noted.

CFD Analysis of a Water Flume Design for Testing Marine and Hydrokinetics Energy Converters

Marine and hydrokinetic (MHK) energy resources with advantages such as predictability and less variability compared to other forms of renewable energies, have been drawing more interest in recent years. One important phase before commercialization of new MHK technologies is to conduct experimental testing and evaluate their performance in a real environment. In this work, a numerical computational fluid dynamics (CFD) method is used to study the fluid flow behavior within a designed water flume for MHK energy technologies. The water flume design parameters were given by the team collaborators at National Renewable Energy Laboratory (NREL) and Colorado School of Mines. The results from this simulation showed the flow characteristics within the test-section of the proposed water flume design. These results can be used for the follow on phases of this research that includes testing scaled MHK prototypes at different flow rates as well as optimizing either the water flume design to obtain more realistic flow characteristics within the test section or the MHK devices to obtain higher performance metrics at lower cost.

Metamodeling-Based Performance Analysis for Digital Power Plant

Digital power plant is the theory and method to improve the operating quality of power plant by quantifying, analyzing, controlling and deciding the physical and working objects of power plants in the whole life cycle. Signals and management information of power plants will be digitized, so as to realize information exchange reliably and accurately and large-scale distributed resource sharing based on the network technology. Then optimization decisions and scientific guidance for plant operation will be proposed by intelligent expert system based on the digital resources. Therefore, the foundation of digital power plant is system modeling and performance analysis. However, there are some problems in the process of the modeling performance analysis of digital power plant. For instance, each unit of the system model has different dimensions and different type of mathematical description, and the data or information used for modeling are defined differently and belong to different enterprises, who do not want to share their information. Metamodeling is potential to solve these problems. It defines the specification to describe a unit and the relationship between different elements. Compared with traditional modeling methodologies for thermal systems, metamodeling makes the model more standardized, and the relationship of the model elements is more explicit and better understood by the co-simulation partners. In this paper, the collaborative modeling and simulation platform for digital power plant has been established based on the metamodeling method and the performance of the target plant has been analyzed from different aspects via field data. The metamodeling method consists of three parts: syntax definition, model development and algorithm definition. The result shows that the collaborate modeling and simulation platform can be used to reduce costs, decrease equipment failure rate, and improve plant output, so as to guarantee the safety and increase economics.