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As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions. [ABSTRACT FROM AUTHOR]

The regional source–grid–load friendly interaction and precision regulation of adjustable resources with high sensitivity provide important support for the development of a new power system, improving its regulation ability and efficiency and promoting the safe, efficient, and reliable operation. Therefore, a precision regulation strategy for a regional source–grid–load friendly interaction based on a double-layer sensitivity model is proposed in this paper. Firstly, from the two perspectives of power grid-application requirements and power node-adjustable resources, the overall implementation process of the double-layer sensitivity analysis method is described, and the layered logic architecture of the precision regulation platform for regional source–grid–load friendly interaction is constructed in order to realize and apply the proposed double-layer sensitivity model and precision-regulation strategy. Secondly, the first-layer power transmission line-sensitivity analysis model is proposed for the power grid side to obtain the power transmission line-sensitivity matrix. A fine-grained second-layer adjustable resources sensitivity-analysis model is further established for the diversified adjustable resource aggregated under the power node to complete the quantitative ranking of adjustable resource-regulation sensitivity. Then, the application process of double-layer sensitivity analysis results is designed in detail, and the regulation sequence and regulation quantity of source and load are defined. Finally, taking a power system with seven lines, five power nodes, two power sources, and seven types of adjustable resources as an example, the effectiveness and rationality of the proposed model and strategy are verified. The results indicate that the proposed precise regulation strategy can accurately select high-sensitivity power nodes and effectively sort high-sensitivity adjustable resources when solving the multiple scenarios' application needs of power grids. [ABSTRACT FROM AUTHOR]

The aim of this research is to simulate and analyze a combined power cycle (Steam turbine and gas turbine cycles) by studying the effect of changing the natural gas flow rate on the developed power. Therefore, reducing the amount of used natural gas in the combustion chamber of the gas turbine cycle from 9.2 to 4 kg/s showed a significant drop in the power produced by the gas turbine, i.e., from 123.7 to 57.7 MW. Additionally, this change in the combusted natural gas amount affected the heat recovered in both heat recovery steam generators (HRSGs), i.e., from 219.79 to 100.35 MW, respectively. Consequently, the amount of generated steam in the high pressure HRSGs and the power developed in the steam turbine changed from 60.88 to 27.79 kg/s and from 56.39 to 27.13 MW, respectively. A heat exchanger (HFHX) utilizing a heating fluid was used as an external source of energy to compensate the reduction in the generated heat and to increase the amount of generated steam up to 157.32 kg/s, which keeps the power plant capacity at 180 MW. Existing combined local plant data were used in this study and were simulated in Aspen Plus software V11. A sensitivity analysis was made to optimize the cycle operating conditions that use less natural gas and produce the same amount of power. [ABSTRACT FROM AUTHOR]

U-tube steam generators (UTSGs) are crucial in nuclear power plants, serving as the interface between the primary and secondary coolant loops. UTSGs ensure efficient heat exchange, operational stability, and safety, directly impacting the plant's efficiency and reliability. Existing UTSG models have fixed structures, which can only be used when certain parameters are given as model input. Such constraints hinder their ability to accommodate the diverse operating conditions, where input and output parameters can vary significantly. To address this challenge, we propose a machine learning-based method for developing a high-degree-of-freedom UTSG thermal model. The most notable feature of this approach is its capacity to flexibly interchange input and output parameters. By adopting comprehensive parameter sensitivity analysis, the most efficient method for training dataset generation is determined. Leveraging a lightweight machine learning method, the prediction accuracy for all UTSG parameters is improved to within 2.1%. The flexibility of the proposed machine learning approach ensures that the UTSG model can accommodate any type of parameter input without extensive reconfiguration of the model structure, thereby enhancing its applicability and robustness in real-world applications. [ABSTRACT FROM AUTHOR]

This work continues the review and illustrative application to energy systems of the "Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling" (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of "large-scale computations" such as those involved in the modeling of energy systems. The result ("response") of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for "high-precision" parameters (2% standard deviations) and "typical-precision" parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions. [ABSTRACT FROM AUTHOR]

This work (in two parts) will review the recently developed predictive modeling methodology called "4th-BERRU-PM" and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the "Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling" methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of "large-scale computations". The model result ("response") of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties. [ABSTRACT FROM AUTHOR]

More than two-thirds of the installed solar power in Brazil comes from distributed mini-generation and microgeneration (DMMG), mostly residential and for local self-consumption of solar PV. DMMG had its legal framework approved in 2022, gradually introduces charges for distribution network use, previously exempt. To understand these implications, this article compiles recent legislative and normative changes, particularly for solar PV DMMG projects. In addition, the present study focuses on the use of DMMG systems in the city of Fortaleza in the Northeast Region. The study includes an analysis of the integration of photovoltaic plants in homes in Fortaleza and their economic viability under the new conditions established after 2020. Two residential photovoltaic systems, with varying power levels common in the residential sector of Fortaleza, were studied. Each plant underwent three analyses: one with vested rights; another with 100% taxation of FIO Bl and a third for the current scenario, factoring in the growing FIO B charge from 15% in 2023 to 100% in 2029. Customers who obtained the system by cash payment or financing were considered. An economic feasibility study factored in a 5.79% yearly inflation rate and an 11.35% annual tariff readjustment across the two plants, including sensitivity analysis for this variable. [ABSTRACT FROM AUTHOR]

Power systems are complex and often subject to faults, requiring accurate mathematical models for a thorough analysis. Traditional time-domain models are employed to evaluate the dynamic response of power system elements during transmission system faults. However, only the positive sequence components are considered for unbalanced faults, so the small-signal stability analysis is no longer accurate when assuming balanced conditions for asymmetrical faults. The dynamic phasor approach extends traditional models by representing synchronous machines with harmonic sequence components, making it suitable for an unbalanced condition analysis and revealing dynamic couplings not evident in conventional methods. By modeling electrical and mechanical equations with harmonic sequence components, the study implements an eigenvalue sensitivity analysis and participation factor analysis to identify the variable with significant participation in the critical modes and consequently in the dynamic response of synchronous machines during asymmetric faults, thereby control strategies can be proposed to improve system stability. The article validates the dynamic phasor model through simulations of a single-phase short circuit, demonstrating its accuracy and effectiveness in representing the transient and dynamic behavior of synchronous machines, and correctly identifies the harmonic sequence component with significant participation in the critical modes identified by the eigenvalue sensitivity to the rotor angular velocity and rotor angle. [ABSTRACT FROM AUTHOR]

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well. [ABSTRACT FROM AUTHOR]

This paper presents the results of a severe accident parametric sensitivity study performed on a model of a generic integral pressurized water reactor (iPWR). The analyzed sequence is a loss of coolant accident (LOCA)-type scenario, postulating that safety systems are not available. In this work, a MELCOR 2.2 input deck of a generic iPWR was developed based on publicly available data. The iPWR used in this work is a generic iPWR with a thermal power of about 160 MWth, characterized by a compact steam generator and a submerged containment configuration. A hypothetical scenario considered in this work was an unmitigated small break LOCA leading to a severe accident with partial core degradation, due to the postulated assumptions. In the presented paper, 16 sensitivity cases were calculated and analyzed, focusing mainly on heat transfer, decay heat, and core degradation parameters. The selected parameters and their combination caused partial core degradation and showed significant differences in investigated variables such as core degradation and hydrogen generation, as well as examined CPU time consumption. This a preparatory work performed in the framework of the Horizon Euratom SASPAM-SA project, which aims to investigate the applicability and transfer of the operating large light-water reactor knowledge and know-how to the iPWR, taking into account European licensing analysis needs for the severe accident and emergency planning zone. [ABSTRACT FROM AUTHOR]

Floating LiDAR systems (FLSs) may replace conventional offshore met masts, and they have been developed well in Europe. However, before using them in Japan, we must determine whether they demonstrate the same performance under the unique East-Asian meteorological and oceanographic conditions. Therefore, herein, we investigate the performance of FLSs by focusing on the differences among models. Four independent wind datasets from three FLSs were simultaneously verified against a reference met mast and vertical LiDAR at a Japanese site. The data availability was confirmed to vary from 62.7 to 98.0% over the period at 63 m. This was strongly affected by the system availability of the buoy and LiDAR, suggesting that buoy system robustness is key to better campaigns with higher data availability. The 10 min averaged wind speed and direction largely satisfied the Carbon Trust's key performance indicators, with a low sensitivity to wave conditions depending on the buoy shape. The standard deviation of the wind speed and turbulence intensity had poorer accuracy than that of the 10 min averaged statistics because of the wave-induced buoy motion, especially for small buoys. In short, this paper provides an overview of a measurement by FLS in Japan. Also, the unique verification with multiple units suggests the need for a low-motion buoy or motion compensation to improve the measurement accuracy of the turbulence component. [ABSTRACT FROM AUTHOR]

Hybrid PVT solar systems offer an innovative approach that allows solar energy to be used to simultaneously generate thermal and electrical energy. It is still a challenge to develop an energy-efficient hybrid PVT system. The aim of this work is to develop a mathematical model, investigate the system's performance based on parameters, include sensitivity analysis in the upper layer mainly photovoltaic part, and provide an efficient and innovative system. Performance analysis of the hybrid system is obtained by establishing a mathematical model and efficiency analysis. The electrical model and thermal model of the hybrid system is also obtained by appropriate and complete mathematical modeling. It establishes a good connection of the system in the context of electrical analysis and power generation. The parameters variation impact and sensitivity analysis of the most important parameters, namely, irradiance, ambient temperature, panel temperature, wind speed, and humidity in the PV panel section, are also obtained using a MATLAB model. The results show the effective increase or decrease in the electrical power and sensitiveness in the output of the system due to this modification. Related MPP values as a result of these parameters variation and their impact on the overall output of the hybrid PVT system are also analyzed. [ABSTRACT FROM AUTHOR]

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cell power generation systems are devices that utilize solid electrolytes to transfer ions for electrochemical energy conversion. A wide range of gases can be used as fuel gas, including hydrogen, natural gas, and carbon monoxide. Considering the high cost of pure hydrogen, hydrogen production from natural gas reforming has become a hot research area. In this study, the 4F-LCA method was employed to construct an evaluation framework, with a particular emphasis on the cost analysis of solid oxide fuel cell power generation systems, and uses a bottom-up approach to build a system economic analysis model to visualize the major costs involved in the system. An economic benefit analysis and sensitivity analysis were carried out for the 2 kW natural gas solid oxide fuel cell as a case by taking the financial net present value (NPV), internal rate of return (IRR) and payback period into account. In this study, the investment cost and payback period of a 2 kW solid oxide fuel cell system are obtained, which can provide a reference for the project construction and operation of solid oxide fuel cell systems. [ABSTRACT FROM AUTHOR]

Microgrids and hybrid renewable energy systems play a crucial role in today's energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings. [ABSTRACT FROM AUTHOR]

Utilizing the discrete fracture model (DFM), a transient flow model is established for fractured horizontal wells in tight oil reservoirs, accounting for threshold pressure gradient (TPG), stress sensitivity effect, hydraulic fracture parameters, and fracture distribution pattern. This model is solved using the finite-volume method (FVM), and an important sensitivity analysis is conducted. The findings reveal that the models incorporated by the threshold pressure gradient result in an upward trend in the pressure-derivative curve. As the threshold pressure gradient increases, this upward trend becomes more pronounced, rendering the distinction between flow regimes more challenging. The stress sensitivity effect predominantly impacts the pressure-derivative curve during the later flow period. Additionally, as the fracture half-length increases, the pressure performance of both fracture radial flow and formation radial flow becomes more difficult. Fracture conductivity has a significant influence during the early flow period, facilitating the identification of flow regimes with the trend of increasing fracture conductivity. [ABSTRACT FROM AUTHOR]

Under the challenge of global energy transition, coal-fired cogeneration systems are undergoing a technical revolution towards enhanced efficiency, heating capacity, and flexibility. In this paper, four schemes using a steam ejector integrated into a cogeneration system are designed. Considering operational safety, integrated locations are selected at the front and back of high- and medium-pressure turbines. Subsequently, the thermodynamic and operational characteristics under both design and off-design conditions are analyzed based on a model built in EBSILON Professional. Finally, a sensitivity analysis of the heating process is conducted. The results show that the integration of steam ejectors can increase the waste heat recovery ratio of exhaust steam by 18.42–45.61% under design conditions. The largest waste heat recovery ratio is obtained in System 4, resulting in the power generation efficiency ( η g ) and gross energy utilization efficiency ( η p ) of 81.95% and 65.53%, respectively. Meanwhile, the steam ejector can expand the power-load regulation range of the cogeneration system, and System 4 has the lowest lower power limit among all the systems. The η p values of Systems 1–4 reach extreme values at different mixed steam pressures of the ejector. Increasing the pinch point temperature difference reduces the power load η g and η p of Systems 1–4. The results provide technical solutions for improving the heating capacity and efficient and flexible operation of cogeneration systems. [ABSTRACT FROM AUTHOR]

Ammonia is a very promising alternative fuel for internal combustion engines, but there are some disadvantages, such as difficulty in ignition and slow combustion rate when ammonia is used alone. Aiming to address the problem of ammonia combustion difficulty, measures are proposed to improve ammonia combustion by blending hydrogen. A one-dimensional turbocharged ammonia-hydrogen engine simulation model was established, and the combustion model was corrected and verified. Using the verified one-dimensional model, the effects of different ratios of hydrogen to ammonia, different rotational speeds and loads on the combustion performance are investigated. The results show that the ignition delay and combustion duration is shortened with the increase of the hydrogen blending ratio. The appropriate amount of hydrogen blending can improve the brake's thermal efficiency. With the increase in engine speed, increasing the proportion of hydrogen blending is necessary to ensure reliable ignition. In conclusion, the ammonia-hydrogen fuel engine has good combustion performance, but it is necessary to choose the appropriate hydrogen blending ratio according to the engine's operating conditions and requirements. [ABSTRACT FROM AUTHOR]

The Second-Order Features Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as "2nd-FASAM-L"), presented in this work, enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to the functions ("features") of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model's uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters. This characteristic enables the 2nd-FASAM-L methodology to maximize the efficiency and accuracy of any other method for computing exact expressions of first- and second-order response sensitivities with respect to the model's features and/or primary uncertain parameters. The application of the 2nd-FASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics. [ABSTRACT FROM AUTHOR]

In the paper, an equation with two delay times (dual-phase lag Equation (DPLE)) in a version that takes into account the dependence of thermophysical parameters (volumetric specific heat and thermal conductivity) on temperature is considered. In particular, an analysis of the sensitivity of transient temperature field in relation to disturbances in delay times (the relaxation and thermalization times) is performed. The sensitivity model concerns the process of heating an ultrathin metal layer with a laser beam. First, the equation with two delay times in the case of temperature-dependent thermophysical parameters is presented. Next, the sensitivity equations with respect to delay times are derived using the direct method. The algorithms for solving the basic and sensitivity tasks are also briefly presented. At the stage of computations, an authorial program based on the implicit scheme of a finite-difference method is developed. In the final part of the paper, examples of numerical solutions (for layers made from gold and nickel) are presented. The research conducted here shows that disturbances in the temperature field are clearly visible and depend, on the one hand, on the thermophysical parameters of the material, and on the other hand, on the intensity of heating with an external heat source. [ABSTRACT FROM AUTHOR]

Reducing energy consumption in residential and commercial buildings is an important research topic. Thermally activated building systems are a promising technology for significantly reducing energy consumption. The high thermal inertia, large surfaces, and radiative nature are advantages of these systems, but, on the other hand, this makes the system control and design complex. A transient simulation is also required to address the dynamic behavior of the system. The influence of 19 factors (material, design, and operating parameters) on the air temperature and mean radiant temperature inside the room as well as the required cooling equipment power were analyzed to better understand the system. The screening experiment was conducted using the random balance design method, and measurement data were used to validate the resistance–capacitance model. The analysis was performed using the Plackett–Burman design and a design with randomly selected points from a full factorial experiment. The results show that internal heat gains and the inlet water temperature have a significant influence on the system, and the influence of the screed's properties is insignificant compared to other parameters. It should be borne in mind that the obtained results and conclusions are valid for the assumed range of factors' variability. [ABSTRACT FROM AUTHOR]

Transient stability preventive control (TSPC), a method to efficiently withstand the severe contingencies in a power system, is mathematically a transient stability constrained optimal power flow (TSC-OPF) issue, attempting to maintain the economical and secure dispatch of a power system via generation rescheduling. The traditional TSC-OPF issue incorporated with differential-algebraic equations (DAE) is time consumption and difficult to solve. Therefore, this paper proposes a new TSPC method driven by a naturally inspired optimization algorithm integrated with transient stability assessment. To avoid solving complex DAE, the stacking ensemble multilayer perceptron (SEMLP) is used in this research as a transient stability assessment (TSA) model and integrated into the optimization algorithm to replace transient stability constraints. Therefore, less time is spent on challenging calculations. Simultaneously, sensitivity analysis (SA) based on this TSA model determines the adjustment direction of the controllable generators set. The results of this SA can be utilized as prior knowledge for subsequent optimization algorithms, thus further reducing the time consumption process. In addition, a naturally inspired algorithm, Aptenodytes Forsteri Optimization (AFO), is introduced to find the best operating point with a near-optimal operational cost while ensuring power system stability. The accuracy and effectiveness of the method are verified on the IEEE 39-bus system and the IEEE 300-bus system. After the implementation of the proposed TSPC method, both systems can ensure transient stability under a given contingency. The test experiment using AFO driven by SEMLP and SA on the IEEE 39-bus system is completed in about 35 s, which is one-tenth of the time required by the time domain simulation method. [ABSTRACT FROM AUTHOR]

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities. [ABSTRACT FROM AUTHOR]

The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship between CO2 emission and multivariate vehicle driving data, in which the vehicle specific power (VSP) is identified as the most significant factor on the CO2 emission factor, followed by speed. Additionally, acceleration and exhaust temperature had the least impact. It is inferred that the changes in data sampling transform the establishment of subspace matrices, affecting the calculation of eigenvector components and the fitting of the final quadratic response surface, so that the emission sensitivity and final fitting accuracy are impressionable by the data distribution form. For the VSP, the best fitting result can be obtained when the VSP conforms to a uniform distribution. Moreover, the Bayesian linear regression method accounts for fitting parameters between the VSP and CO2 emission factor with uncertainties derived from heteroscedastic measurement errors, and the values and distributions of the intercept and slope α and β are obtained. In general, the high-resolution inventory of the carbon emission factor of the tested vehicle is set up via systematically analyzing it, which brings a bright view of data processing in further counting the carbon footprint. [ABSTRACT FROM AUTHOR]

Fractures are common features in deep gas reservoirs with strong heterogeneity, which are generally evaluated by well-testing analysis. Based on the characteristics of a variety of spatial scales from microscopic fractures to macroscopic faults in the Kuche area, this paper investigated the pressure behavior of naturally fractured gas reservoirs by using four typical patterns, including the Warren–Root model, radial composite model, dual-flow-state model, and local heterogeneity model. As a result, typical well-testing curves and parameter sensitivity analysis were demonstrated in detail. Furthermore, the pressure transient behavior of multi-scaled fractured reservoirs was identified by use of a state-of-the-art workflow. The analysis shows that the four patterns and their identification processes were feasible for the inversion of reservoir parameters in fractured gas reservoirs. Combined with dynamic data, the proposed method could further guide the optimization of development schemes and is of great significance for the development of fractured gas reservoirs. [ABSTRACT FROM AUTHOR]

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building's four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions. [ABSTRACT FROM AUTHOR]

Biomass is a renewable energy source gaining attention for its potential to replace fossil fuels. Biomass gasification can produce hydrogen-rich gas, offering an environmentally friendly fuel for power generation, transportation, and industry. Hydrogen is a promising energy carrier due to its high energy density, low greenhouse gas emissions, and versatility. This study aims to develop a hydrogen generation plant using a dual fluidized bed gasifier, which employs steam as a gasifying agent, to convert olive pomace waste from the Lebanese olive oil industry into hydrogen. The process is simulated using Aspen Plus and Fortran coding, and it includes a drying unit, gasification unit, gas cleaning unit, steam methane reformer unit, water–gas shift reactor unit, and a pressure swing adsorption unit. The generated gas composition is verified against previous research. Sensitivity analyses are conducted to investigate the impacts of the steam-to-biomass ratio (STBR) and gasification temperature on gas composition, demonstrating a valid STBR range of 0.5 to 1 and a reasonable gasification temperature range of 700 °C to 800 °C. Further sensitivity analyses assess the impact of reformer temperature and the steam-to-carbon ratio (S/C) on the gas composition leaving the steam methane reformer. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy sources (HRES) are increasingly being utilized to meet global energy demands, particularly in rural areas that rely on diesel generators and are disconnected from the utility grid, due to their environmental and human health benefits. This study investigates the performance of an off-grid, hybrid PV/diesel generator/battery system for a decentralized power plant in Kuakata, Bangladesh, meeting a load demand of 3000 kWh/day with a 501.61 kW peak load demand. HOMER Pro (hybrid optimization model for electric renewable) software (version 3.11) was used to simulate and optimize system operations utilizing real-time solar radiation and load profile data from that location. This study also includes a sensitivity analysis of the off-grid HRES system under different electrical load demands, project longevity, and derating variables. The results reveal that CO2 emissions have potentially decreased by more than 30% and over 10 tons per year, respectively, when compared to traditional power plants. The optimized system's net present cost (NPC) was determined to be around USD 5.19 million, with a cost of energy (COE) of USD 0.367 per kWh per unit with a 100% renewable component. Furthermore, the current study's findings are compared to previous research that has resulted in an economical hybrid renewable energy system with an affordable COE. The hybrid energy system under consideration might also be applicable to other parts of the world with comparable climate conditions. [ABSTRACT FROM AUTHOR]

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin's environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities. [ABSTRACT FROM AUTHOR]

Well pattern infilling optimization is a crucial measure to enhance gas recovery, especially in tight gas reservoirs with low permeability and small-scale sand bodies. Traditional methods of determining well pattern density rely on qualitative analysis from the perspective of gas blocks. However, these methods are challenging to apply to sand bodies with different properties, and there have been no studies conducted on infilling time, which significantly impacts production increment. In response to this situation, this paper establishes a series of evaluation indexes and proposes a numerical simulation of economic well pattern density and infilling time based on real parameters obtained from the Linxing–Shenfu gas field. To quantitatively determine the economic well pattern density and infilling time based on fine characterization of sand bodies, a sensitivity analysis is conducted, considering various permeability levels and reserve abundance. Two intersection charts of economic well pattern density and infilling time, relating to reserve abundance and permeability, are then drawn. Furthermore, a real well site is selected as an example, and the infilling effect analysis confirms the reliability of the charts. The new method of determining economic well pattern density and infilling time presented in this article can provide theoretical support for the economic and efficient development of the Linxing–Shenfu gas field, serving as a useful reference for the beneficial development of similar gas reservoirs. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing is the main means for developing low-permeability shale reservoirs. Whether to produce artificial fractures with sufficient conductivity is an important criterion for hydraulic fracturing evaluation. The presence of clay and organic matter in the shale gives the shale creep, which makes the shale reservoir deform with time and reduces the conductivity of the fracture. In the past, the influence of shale creep was ignored in the study of artificial fracture conductivity, or the viscoelastic model was used to predict the conductivity, which represents an inaccuracy compared to the actual situation. Based on the classical Perzyna viscoplastic model, the elasto-viscoplastic constitutive model was obtained by introducing isotropic hardening, and the model parameters were obtained by fitting the triaxial compression creep experimental data under different differential stresses. Then, the constitutive model was programmed in a software platform using the return mapping algorithm, and the model was verified through the numerical simulation of the triaxial creep experiment. Then, the creep calculation results of the viscoplastic constitutive model and the power law model were compared. Finally, the viscoplastic constitutive model was applied to the simulation of the long-term conductivity of the fracture to study the influence of creep on the fracture width, and sensitivity analysis of the influencing factors of the fracture width was carried out. The results show that the numerical calculation results of the viscoplastic model were in agreement with the experimental data. The decrease in fracture width caused by pore pressure dissipation and reservoir creep after 72 h accounts for 32.07% of the total fracture width decrease. [ABSTRACT FROM AUTHOR]

This article addresses challenges in the design of underground high-voltage transmission lines, focusing on thermal management and cable ampacity determination. It introduces an innovative proposal that adjusts the dimensions of the backfill to enhance ampacity, contrasting with the conventional approach of increasing the core cable's cross-sectional area. The methodology employs a particle swarm optimization (PSO) technique with adaptive penalization and restart strategies, implemented in MATLAB for parameter autoadaptation. The article emphasizes more efficient solutions than traditional PSO, showcasing improved convergence and precise results (success probability of 66.1%). While traditional PSO is 81% faster, the proposed PSO stands out for its accuracy. The inclusion of thermal backfill results in an 18.45% increase in cable ampacity, considering variations in soil thermal resistivity, backfill properties, and ambient temperature. Additionally, a sensitivity analysis was conducted, revealing conservative values that support the proposal's robustness. This approach emerges as a crucial tool for underground installation, contributing to continuous ampacity improvement and highlighting its impact on decision making in energy systems. [ABSTRACT FROM AUTHOR]

An innovative process layout for sludge waste management based on chemical looping combustion and flue gas methanation is analyzed in this work. The technical performance of the system was assessed by considering that the flue gas is first purified and then mixed with a pure hydrogen stream sourced from an array of electrolysis cells to produce methane. The life cycle assessment (LCA) and life cycle cost (LCC) methodologies were applied to quantify the environmental and economic performances of the proposed process, and a hotspot analysis was carried out to recognize its most critical steps. The proposed system was then compared with a reference system that includes both the conventional waste management pathways for the Italian context and methane production. Finally, to account for the variability in the future economic climate, the effects of changes in landfill storage costs on sewage end-of-life costs for both the proposed and reference systems were evaluated. With respect to 1 kg/h of sewage sludge with 10%wt of humidity, the analysis shows that the proposed system (i) reduces landfill wastes by about 68%, (ii) has an end-of-life cost of 1.75 EUR × kg−1, and (iii) is environmentally preferable to conventional sewage sludge treatment technologies with respect to several impact categories. [ABSTRACT FROM AUTHOR]

In this study, a concentration overvoltage model that focuses on describing variable-temperature operating condition properties for PEMFCs is established. Sensitivity analysis and a quantification study of oxygen transport resistance are carried out based on the oxygen transport resistance model and measurement data. By analyzing the influence of temperature on cathode oxygen transport resistance, the key structural parameters of the cathode oxygen transport resistance models are estimated, and the parameter modification method of fuel cell limiting current density under variable temperatures is proposed. Based on the polarization curve test experiments under variable-temperature conditions, it is demonstrated that the newly developed concentration overvoltage model reduces the relative error of simulation for a low Pt loading fuel cell in the high current region by 2.97% and 10.06% at 60 °C and 80 °C, respectively. The newly established concentration overvoltage model of a PEMFC solves the problem that the parameter of limiting current density is set without considering the influence of fuel cell temperature fluctuation, which leads to the poor simulation accuracy of the concentration overvoltage model in the high current region. [ABSTRACT FROM AUTHOR]

Large discrepancies can occur between building energy performance simulation (BEPS) outputs and reference data. Uncertainty and sensitivity analyses are performed to discover the significant contributions of each input parameter to these discrepancies. Variance-based sensitivity analyses typically require many stochastic simulations, which is computationally demanding (especially in the case of the large number of input parameters involved in the analysis). To overcome these impediments, this study proposes a reliable meta-model-based sensitivity analysis, including validation, Morris' method, multivariate adaptive regression splines (MARS) meta-modeling, and Sobol' method, to identify the most influential input parameters on BEPS prediction (annual energy consumption) at the early building design process. A hypothetical building is used to analyze the proposed methodology. Six statistical metrics are applied to verify and quantify the accuracy of the model. It is concluded that the cooling set-point temperature and g-value of the window are the most influential input parameters for the analyzed case study. [ABSTRACT FROM AUTHOR]

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution. [ABSTRACT FROM AUTHOR]

The effects of six control parameters, intake valve opening timing (IVO), exhaust valve opening timing (EVO), compression ratio (CR), engine speed, intake temperature, and intake pressure on engine output power, indicated specific fuel consumption (ISFC), and nitrogen oxides (NOx) emissions, are analyzed through engine simulation. The six parameters were categorized into two groups based on the degree of influence: high influence (EVO, speed and intake pressure) and low influence (CR, IVO and intake temperature). The relationship between these two groups of parameters and power, ISFC and NOx emissions was explored. Optimization was carried out for each of the two groups of parameters, and the optimization of the high impact parameters resulted in a higher diversity and wider distribution of the solution set. On the other hand, the optimization of the low-impact parameters resulted in a more concentrated distribution of the solution set, while better reflecting the trade-off between the optimization objectives. For the optimal solutions for both sets of parameters, the high-impact parameters provided significant optimization performance compared to the standard operating conditions. Although power and ISFC were optimized, the optimal solution for the low-impact parameter performed poorly with a significant increase in NOx emissions. Therefore, the parameters should be evaluated for optimization using high impact parameters to improve engine performance. [ABSTRACT FROM AUTHOR]

Within domestic food cooking, burning firewood in three-stone fires (TSF) is a common practice by more than 16 million users in the Mexican tropical climate region (CR-TR). This article aims to evaluate the implementation of improved firewood cookstoves (ICS) to replace TSF in the CR-TR by constructing firewood consumption scenarios covering 2018–2050. The results show that in CR-TR, with the implementation of ICS, the consumption of 354.95 PJ of firewood, 36.6 Mt of CO2e, 1.29 Mt of CO, and 163.78 kt of PM2.5 can all be avoided. The most important reduction in firewood consumption, CO2e emissions and CO and PM2.5 pollutants, occurs in exclusive firewood users and mixed users who utilize firewood as the primary fuel source, both of whom are low socioeconomic level in rural areas. Furthermore, most paying-for-firewood users often show negative mitigation costs and a high 50% IRR, while all non-paying-for-firewood users have mitigation costs ranging from 7.74 to 41.23 USD/tCO2e. At the end of the results section, we perform a sensitivity analysis of the relevant parameters, which complements this study. Therefore, implementing ICS will contribute to the solution of climate change, deforestation, and facilitate the formulation of sustainable development policies for the most vulnerable population sector of the Mexican CR-TR. [ABSTRACT FROM AUTHOR]

Although the accuracy of short-term prediction of building-integrated photovoltaics is essential to making an optimal decision on the management of the generated electricity, the weather forecasting service in many countries provides insufficient features for improving the prediction accuracy of the photovoltaics power output. This study suggests a machine learning model incorporated with feature engineering to improve the prediction performance of day-ahead hourly power outputs using a simple weather forecast service. A new synthetic feature, the modified sky condition, is derived to infer onsite sky condition and solar irradiation, which is not supported by the typical weather forecasting services. It evaluated the prediction performance with different training and hyper-parameter conditions for 60 days. By using the derived modified sky condition, the model outperformed other predictor configurations in most daily sky conditions; particularly, the accuracy improved by more than 50% on overcast days compared to when it used the original weather forecasting service data. The result demonstrates the feasibility and ability of the model to enable more efficient energy management of building-integrated photovoltaic power output in buildings without an onsite weather station, thus contributing toward the optimized dispatch of the integrated electricity energy storage system and other distributed energy resources. [ABSTRACT FROM AUTHOR]

The development of a geothermal system involves changes in the temperature field (T), seepage field (H), stress field (M), and chemical field (C) and the influence among them and injecting the heat extraction working fluid into the injection well that flows (migrating) through natural fractures and exchanges heat with the geothermal high-temperature rock. At the same time, the injection of low-temperature working fluid will induce thermal stress, resulting in changes in the reservoir temperature field and stress field. To study the influence factors and influence degree of heat production performance and mining life under multi-field coupling in the process of thermal reservoir development, based on THMC multi-field coupling numerical simulation software, this paper deeply studies the control differential equations and boundary coupling conditions of rock mass (fracture) deformation, seepage, heat exchange, the chemical reaction, and other processes based on the numerical solution method of the discrete fracture network model, simulating heat production capacity during the deep geothermal resource extraction process. The reservoir geological model analysis and generalization, parameter setting, boundary conditions, initial condition settings, mesh generation, and other steps were carried out in turn. Two different heat extraction working fluids, water, and CO2 were selected for numerical simulation in the mining process. The changes in the thermal reservoir temperature, net heat extraction rate, and SiO2 concentration during the thirty years of systematic mining were compared. The results show that CO2 has a better heat extraction effect. Finally, the reservoir thermal conductivity, heat capacity, well spacing, injection temperature, fracture spacing, fracture permeability, fracture number, fracture length, and other parameters were set, respectively. The parameter variation range was set, and the parameter sensitivity analysis was carried out. The numerical simulation results show that the engineering production conditions (injection temperature, well spacing) have little effect on the thermal efficiency and mining life, and the properties of fractures (fracture permeability, fracture number, fracture length) have a great influence. [ABSTRACT FROM AUTHOR]

In this paper, a simple model is proposed for predicting the thermal energy requirements of greenhouses in Europe. The model estimates the annual heating requirements and the maximum required heating power, along with the corresponding heating and zero-energy operating periods. It is based on the greenhouse technical data (the overall heat loss coefficient, cover transmission, sensible absorbance), the cultivation conditions (temperature range), and the meteorological data (solar radiation and ambient temperature) according to the site characteristics (longitude and latitude). The model results can be obtained using a hand calculator. The model results are compared with those of a detailed model simulating a greenhouse's thermal performance and they agreed with real data from the literature. Moreover, a model sensitivity analysis revealed the effect of various factors on the greenhouse's energy requirements. The results proved that the most significant factor affecting heating requirements, the maximum heating power, and heating periods is the latitude of the greenhouse site, whereas zero-energy periods are primarily influenced by the plant cultivation temperature range and then by the latitude. According to our findings, in lower latitudes (40 to 50 degrees), heating requirements range from 250 to 430 kWh/m2/y, whereas, in higher latitudes (50 to 60 degrees), heating needs range from 430 to 650 kWh/m2/y. [ABSTRACT FROM AUTHOR]

The fast-neutron burst reactor is a chain reactor that can operate in a prompt critical state. In order to ensure the operational safety of the fast-neutron-pulse reactor and prevent the supercritical pulse from causing physical damage to the material, it is necessary to simulate and analyze the pulse operating conditions of the fast-neutron-pulse reactor. Godiva-I is a spherical assembly of highly enriched uranium metal made during the 1950s. A prompt-critical transient in such a nuclear system impels a quick power excursion, which will cause a temperature rise and a subsequent reactivity reduction because of the metal sphere's expansion. The overall transient lasts for a few fractions of a millisecond. Based on the point kinetics and Monte Carlo method, the temporal and spatial characteristics of transient input power were calculated, the difference of the average reactivity temperature coefficient between uniform density and non-uniform density was compared, and the transient power distribution condition was loaded into the thermal–mechanics calculation of the MOOSE platform; thus, the pulse process of Godiva-I with different initial reactivity periods was simulated. The JFNK (Jacobian–Free–Newton–Krylov) direct method and multi-app indirect method were used to analyze the transient response of the pulse dynamic process using the heat conduction module and tenor mechanics module, respectively. After considering the influence of the inertia effect and wall-reflected neutrons, the simulation results were much closer to the experimental values. Based on the stochastic tools module, the uncertainty propagation and sensitivity analysis of the Godiva-I model were carried out, the uncertainty of external surface displacement of Godiva under input disturbance of material properties and heat source amplitude factor was obtained, and the sensitivity of different input parameters to output parameters was quantified. The research results can lay a technical foundation for the thermal–mechanics coupling analysis and uncertainty quantification of the metal fast reactor. [ABSTRACT FROM AUTHOR]

This work introduces a new methodology, which generalizes the extant second-order adjoint sensitivity analysis methodology for computing sensitivities of model responses to primary model parameters. This new methodology enables the computation, with unparalleled efficiency, of second-order sensitivities of responses to functions of uncertain model parameters, including uncertain boundaries and internal interfaces, for linear and/or nonlinear models. Such functions of primary model parameters customarily describe characteristic "features" of the system under consideration, including correlations modeling material properties, flow regimes, etc. The number of such "feature" functions is considerably smaller than the total number of primary model parameters. By enabling the computations of exact expressions of second-order sensitivities of model responses to model "features", the number of required large-scale adjoint computations is greatly reduced. As shown in this work, obtaining the first- and second-order sensitivities to the primary model parameters from the corresponding response sensitivities to the feature functions can be performed analytically, thereby involving just the respective function/feature of parameters rather than the entire model. By replacing large-scale computations involving the model with relatively trivial computations involving just the feature functions, this new second-order adjoint sensitivity analysis methodology reaches unsurpassed efficiency. The applicability and unparalleled efficiency of this "2nd-Order Function/Feature Adjoint Sensitivity Analysis Methodology" (2nd-FASAM) is illustrated using a paradigm particle transport model that involves feature functions of many parameters, while admitting closed-form analytic solutions. Ongoing work will generalize the mathematical framework of the 2nd-FASAM to enable the computation of arbitrarily high-order sensitivities of model responses to functions/features of model parameters. [ABSTRACT FROM AUTHOR]

The mathematical/computational model of a physical system comprises parameters and independent and dependent variables. Since the physical system is seldom known precisely and since the model's parameters stem from experimental procedures that are also subject to uncertainties, the results predicted by a computational model are imperfect. Quantifying the reliability and accuracy of results produced by a model (called "model responses") requires the availability of sensitivities (i.e., functional partial derivatives) of model responses with respect to model parameters. This work reviews the basic motivations for computing high-order sensitivities and illustrates their importance by means of an OECD/NEA reactor physics benchmark, which is representative of a "large-scale system" involving many (21,976) uncertain parameters. The computation of higher-order sensitivities by conventional methods (finite differences and/or statistical procedures) is subject to the "curse of dimensionality". Furthermore, as will be illustrated in this work, the accuracy of high-order sensitivities computed using such conventional methods cannot be a priori guaranteed. High-order sensitivities can be computed accurately and efficiently solely by applying the high-order adjoint sensitivity analysis methodology. The principles underlying this adjoint methodology are also reviewed in preparation for introducing, in the accompanying Part II, the "High-Order Function/Feature Adjoint Sensitivity Analysis Methodology" (nth-FASAM), which aims at most efficiently computing exact expressions of high-order sensitivities of model responses to functions ("features") of model parameters. [ABSTRACT FROM AUTHOR]

In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power system parameter identification for a long time, while the GSA has started to be used in recent years. However, there is no clear conclusion on the impact of different sensitivity analysis methods on parameter identification results. Therefore, this paper compares and studies the roles that LSA and GSA can play in different parameter identification methods, providing clear guidance for the selection of sensitivity analysis methods and parameter identification methods. The conclusion is as follows. If the identification strategy that only identifies key parameters with high sensitivity is adopted, we recommend still using the existing LSA method. If using a groupwise alternating identification strategy (GAIS) for high- and low-sensitivity parameters, either LSA or GSA can be used. To improve the identification accuracy, it is more important to improve the identification strategy than to change the sensitivity analysis method. When the accuracy of the non-key parameters with low sensitivity cannot be confirmed, using the GAIS is an effective method for ensuring identification accuracy. In addition, it should be noted that the high sensitivity of a parameter does not necessarily mean that the parameter is identifiable, which is revealed by the examples used in this paper. [ABSTRACT FROM AUTHOR]

This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network. [ABSTRACT FROM AUTHOR]

The detailed mechanisms of n-heptane and n-butanol were reduced for the target condition of ignition delay time using the direct relationship diagram method based on error transfer, the direct relationship diagram method based on coupling error transfer and sensitivity analysis, and the total material sensitivity analysis method. The reduced n-heptane (132 species and 585 reactions) and n-butanol (82 species and 383 reactions) were used to verify the ignition delay time and concentrations of the major species, respectively. The results showed that the reduced mechanism has a good prediction ability for the ignition delay time. The predicted mole fraction results of the major species were in good agreement. These reduced mechanisms were combined to finally construct a reduced mechanism for the n-heptane/butanol fuel mixture, which included 166 species and 746 reactions. Finally, the reduced mechanism was used to simulate the HCCI combustion mode, and the results showed that the reduced mechanism can better predict the ignition and combustion timings of HCCI under different conditions and maintain the ignition and combustion characteristics of the detailed mechanism; this indicates that the mechanism model constructed in this study is reliable. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of involving electric vehicles (EVs) in the load profile on the generation system. The impact was studied from a reliability perspective on Saudi Arabia's total generation system capacity as one source supplying the expected load for the year 2030. The EV load profile was then added. The outcomes were examined considering the gradual penetration percentage to the total load. The reliability indices measured are the loss of load probability (LOLP) and the expected energy not supplied (EENS). The results show that the estimated generation system of Saudi Vision 2030 will not withstand the estimated number of EVs without negatively impacting reliability. Similarly, the reliability assessment was conducted for the central region considering EVs in Riyadh City to verify Saudi Vision 2030. The results show that EV integration will greatly affect the electrical network's reliability. Furthermore, a sensitivity analysis was conducted for Saudi Arabia and the central region to assess the generation system better. The study shows that investing in the generation infrastructure is essential to handle EV growth for the upcoming years. The work introduced in this paper will also help decision-makers make appropriate planning decisions in the future. [ABSTRACT FROM AUTHOR]

Pulse tube refrigerators are widely used in certain special fields, such as aerospace, due to their unique advantages. Compared to a conventional phase shifter, the active displacer helps to achieve a higher cooling efficiency for pulse tube refrigerators. At present, the displacer is mainly studied by one-dimensional simulation, and the optimization method is not perfect due to its poor accuracy, which is not conducive to obtaining a better performance. Based on the current status of displacer research, phase-shift mechanisms of inertance tube pulse tube refrigerators and active displacer pulse tube refrigerators were firstly studied comparatively by multidimensional simulation, and then we determined the crucial effect properties that lead to a better cooling performance for the active displacer pulse tube refrigerator at different cooling temperatures. Finally, an efficient optimization method combining the Kriging model and genetic algorithm is proposed to further improve the cooling performance of the active displacer pulse tube refrigerator. The results show that the active displacer substantially improves the cooling performance compared to the inertance tube mainly by increasing the PV power and enthalpy flow in the pulse tube. The Kriging agent models of active displacer pulse tube refrigerator achieve 98.2%, 98.31%, 97.86%, and 97.32% prediction accuracy for no-load temperature, cooling capacity, coefficient of performance, and total input PV power, respectively. After optimization, the no-load temperature is minimally optimized for a 23.68% reduction compared to the initial one with a relatively high efficiency, and the founded optimization methods can also be weighted for multiple objectives, according to actual needs. [ABSTRACT FROM AUTHOR]