Multiple System Models Research Articles

Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory

To tackle the issues of cooperative energy and reserve trading as well as fair cooperative benefit allocation among multiple integrated energy systems (IESs), this paper proposes a two-stage cooperative energy and reserve trading model for multiple integrated energy systems (MIESs). Specifically, at day-ahead stage, MIESs aim to maximize their overall profit through cooperative electricity and heat trading. At real-time stage, MIESs trade demand response (DR) reserve to minimize the overall wind power deviation compensation cost. To reduce the complexity in model solution, we transform the model into two sub-problems. In sub-problem 1, we determine the energy and DR reserve trading volumes. Here, distributionally robust optimization (DRO) is utilized to manage the severe uncertainties in wind power distribution. In sub-problem 2, based on the outcomes from sub-problem 1, we settle the energy and DR reserve trading prices. To ensure the fairness of benefit allocation, asymmetric Nash bargaining theory is applied to assess each IES's contributions in trading volumes and profit growth. Interval adaptive alternating direction method of multipliers (IA-ADMM) is used to preserve each IES's privacy. Finally, simulation results demonstrate that, compared with independent operation, cooperative trading among MIESs increases profits for all IESs, thereby motivating their participation in cooperative trading.

Modeling of fluence-dependent hole-burned spectra and hole-growth kinetics using multiple two-level system model.

Numerical formalism is presented that perfectly describes resonant low-temperature hole-burned spectra (including zero-phonon holes, ZPHs) and spectral hole-growth dynamics of Al-phthalocyanine tetrasulphonate embedded in hyperquenched glassy water films over more than seven orders of fluence magnitude (0.4 µJ/cm2-5.9 J/cm2). Frequency changes during spectral hole-burning (HB) are traditionally explained with the help of a single extrinsic two-level-system (TLSext) associated with impurity molecules. The new multiple two-level system (n-TLSext) models and data analysis presented in this work show that each chromophore in an amorphous medium can couple with multiple independent TLSext, which maintain perfect photo-memory, allowing a full return of the photoproduct to the initial ("preburn") state. Modeling reveals that the experimentally observed narrow photoproduct peak at higher energies, in close vicinity of the zero-phonon hole (ZPH), reflects a dynamical feature of the HB process populating so-called "terminal" states (states that do not interact with laser excitation). Within the n-TLSext model, each chromophore possesses multiple possibilities to create a photoproduct when in interaction with the burning laser, i.e., chromophores can interact with burning laser-light multiple times until reaching the terminal states. Due to phonon-assisted absorption, terminal states are typically at higher energies than the ZPH, in agreement with the hole burned spectra reported for many molecules embedded in various amorphous solids. However, many HB systems reveal both blue- (high-energy) and red-shifted (low-energy) antiholes (i.e., photoproducts). We suggest that future modeling of resonant holes in various proteins using our n-TLSext model will provide more insight on the complexity of the protein energy landscape.

Analysis of active impedance characteristics and harmonic deterioration of multiple grid connected inverters considering nonlinear factors

AbstractThe harmonic problems caused by non‐linear factors of the grid connected inverter (GCI) system are more complicated, including both non‐characteristic harmonics emitted by the dead‐time and the changes in harmonic impedance characteristics. Harmonics interact with the changing impedance, and even cause harmonic amplification and resonance. The harmonic deterioration of the multiple GCIs system is serious. To analyse the mechanism and way of harmonic deterioration in grid‐connected system caused by nonlinear factors, the active impedance models of single inverter and multiple GCIs system including dead‐time effect and digital control delay are established first. In view of this, the influence mechanism of non‐linear factors on system stability is explored. The improved modal analysis method is used to traverse the network series parallel resonance caused by nonlinear factors. The results show that both the dead‐time effect and digital control delay reduce phase margin of the system, resulting in the resonant frequency shift and the resonant peak increase. When the harmonic excitation source interacts with the complex network under the influence of nonlinear factors, it will lead to further deterioration of harmonics. Finally, based on the MATLAB and RT‐LAB hardware‐in‐the‐loop simulation platforms, a multiple GCIs system model is built to verify the correctness of the established active impedance model and harmonic deterioration analysis.

Asymmetric Nash bargaining model for operation optimization of multi-integrated energy systems considering peer-to-peer energy trading

Energy interactions across integrated energy systems constitute crucial means to enhance energy efficiency and match supply and demand. However, the cooperative operation and benefit distribution among multiple integrated energy systems still need in-depth exploration. To fill this gap, this paper presents an asymmetric Nash bargaining optimization model for multiple integrated energy systems considering peer-to-peer trading. First, a scheduling model for multi-integrated energy systems is formulated considering carbon trading. Then, the model is incorporated into Nash bargaining framework and transformed into the alliance cost minimization subproblem and peer-to-peer trading payment bargaining subproblem. The bargaining power factor is introduced to measure the contribution of participants in energy sharing. The alternating direction multiplier method is utilized to handle the proposed model. Finally, a case study is carried out to validate the validity of the proposed strategy. The results show that compared with the independent operation mode, the performances of three integrated energy systems in collaborative operation mode are enhanced by 11.7 %, 9.0 %, and 4.8 % respectively. The distributed algorithm can reduce the computation time by 30 % and obtain highly efficient solutions while protecting private information of each participant. This research provides support and practical tools for conducting peer-to-peer transactions of multiple integrated energy systems.

Theoretical modeling and analysis of an equipment–shell multiple-transmission-path system with attached liquid-filled pipe

Vibrations generated by mechanical equipment are the main source of radiation noise for ships. In actual systems, there are multiple transmission paths for mechanical vibrations from the equipment to shell. Common paths include vertical support structures, such as vibration isolators, and lateral attachment structures, such as pipes. However, most existing models only consider a single-path scenario. Multi-path systems have more complex modal and vibration transmission characteristics and introduce more complex coupling problems. There is a lack of theoretical modeling and physical analyses of common multiple transmission systems in ships. In this study, a theoretical model of an equipment–shell multiple-transmission-path system with an attached liquid-filled pipe is established as a typical multiple-transmission-path system for ships. In this model, the liquid-filled pipe is solved using a newly proposed traveling-wave method based on Kennard shell theory. Based on the theoretical model, the effects of each type of traveling wave on the pipe on the system modal characteristics, the mechanism behind each path on the system coupling characteristics, the influence of structural parameters on system modal and vibration transmission characteristics, and the proportion of vibration transmitted by the two transmission paths are analyzed. This study provides theoretical insights and guidance for practical vibration and noise reduction engineering.

Frailty model for multiple repairable systems hierarchically represented subject to competing risks

In this paper, we propose a statistical model to describe the behaviour of failure times associated with groups of repairable systems hierarchically represented, under a competing risks framework, considering the existence of unobserved heterogeneity that acts individually on systems of each group, as well as the possibility of imperfect repairs whose initial failure rate is in the form of the power law. In this context for the unobserved heterogeneity in the groups, we consider the multiplicative frailty. To illustrate the use of the proposed model, we consider a database with the failures of 38 agricultural machines categorized into five different groups. We understand that the tractor fleet corresponds to the farm's agricultural system, therefore the need for intervention in this system occurs with the failure of any unit, individually, in a serial structure, of competing risks.e On the other hand, the understanding of the time in which all the machinery that makes up the fleet will have required some intervention is obtained by analysing the results under a parallel structure.

A vibration model of a flexible multiple shaft gear system with the tip relief modification

The fixed-shaft gear system (FSGS) is one critical part in gear transmission systems (GTSs), whose vibrations can significantly influence performances of GTSs. To reduce the vibrations of rigid FSGS caused by installation and manufacture errors, the tooth profile modifications without the shaft deformation were widely introduced. This paper establishes a flexible multiple shaft gear system (FMSGS) model of an unmanned underwater vehicle, which can obtain vibrations of rotor at different axial positions. However, the previous rigid FSGS model cannot obtain those vibrations. In the FMSGS model, the Timoshenko beam elements are applied to the establishment of shaft segments considering flexibility. The meshing stiffness for helical gears with different ranges of tooth profile modification are quantitatively expressed by a gear slicing method. Based on the kinetic relationship between the gear pairs, shaft segment, and bearings, the dynamic equations of FMSGS are derived. By comparing the time-domain waveforms and spectra of FMSGS, the effects of the tooth profile modification amounts and lengths on the vibrations of FMSGS are revealed. The proposed FMSGS model is validated by an experiment. This study extends the dynamic methods of FSGSs, and some suggestions on the vibration control of FSGSs are given.

A novel energy management method for multiple residential energy systems with energy exchange

Energy saving and emission reduction are the worldwide outstanding problems in the development of economy and society, which lead to wide attention of residential energy management. This paper presents a novel energy management method for multiple residential energy systems with energy exchange based on action-dependent heuristic dynamic programming (ADHDP). The major contributions are displayed as follows: (1) a novel energy management model is established for multiple residential energy systems with real-time electricity price and time-varying residential loads; (2) a novel ADHDP based energy management method is presented to obtain the energy scheduling scheme of multiple residential energy systems; (3) the continuous control action of the overall system is transformed into discrete control space which avoids training the action network. First, the structure and system models of multiple residential energy systems are introduced, and the energy management problem is formulated. Then, a brief introduction of ADHDP method is provided. Based on this, an energy management method is presented and an effective discretization method of control action is developed for solving the presented energy management problem with less computational burden. Simulation results show that the presented method can reduce the energy cost effectively. Finally, the comparison results verify the superiorities over the traditional energy management methods.

Using long-term condition monitoring data with non-Gaussian noise for online diagnostics

The number of timely diagnoses based on condition monitoring data is increasing with the growing usage of monitoring systems. In most of the methods used in these systems, a pre-established fault detection threshold is needed, while there are no specific limit values or thresholds in many cases, especially when the machine is unique. Also, in most actual applications, due to the kind of process and harsh environment, the noise inherent in the observed process exhibits non-Gaussian characteristics, making it a challenging task for diagnostics based on condition monitoring (CM) data. Therefore, this paper introduced a robust methodology based on the switching maximum correntropy Kalman filter (SMCKF) to address the mentioned problems (threshold and online diagnostics in the presence of non-Gaussian noise by using CM data). This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. As this approach is based on dynamic behavior, a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied to the online diagnosis of simulated and actual data sets. The results of both simulated and real data sets prove the method’s efficacy.

Robust switching Kalman filter for diagnostics of long-term condition monitoring data in the presence of non-Gaussian noise

Machinery condition prognosis system uses long-term historical data to predict remaining useful life (RUL). One of the critical steps to reach this purpose is to segment long-term data into two or several degradation stages (Healthy, Unhealthy, and Critic stage). Finding changing points between regimes may be a crucial preliminary task for further predicting the degradation process. However, finding the accurate partition into two or more regimes is a challenging task in the actual application when the noise inherent in the observed process is non-Gaussian. Therefore, this paper introduced a robust methodology based on switching Kalman filters to address the problems mentioned. This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. Also, based on this fact, this approach works based on dynamic behavior; a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied for the online diagnosis of simulated data sets in the presence of Gaussian and non-Gaussian noise. The result of the applied methodology on simulated data sets proves the method’s efficacy.

Distributed data filtering and modeling for fog and networked manufacturing

Fog Manufacturing applies both Fog and Cloud Computing collaboratively in Smart Manufacturing to create an interconnected network through sensing, actuation, and computation nodes. Fog Manufacturing has become a promising research component to be integrated into the existing Smart Manufacturing paradigm and provides reliable and responsive computation services. However, Fog nodes' relatively limited communication bandwidth and computation capabilities call for reduced data communication load and computation time latency for modeling. There has long been a lack of an integrated framework to automatically reduce manufacturing data and perform computationally efficient modeling/machine learning. This research direction is increasingly important as both the computational demands and Fog/networked Manufacturing become prevalent. This paper proposes an integrated and distributed framework for data reduction and modeling of multiple systems in a Smart Manufacturing network considering the system similarities. A simulation study and a Fog Manufacturing testbed for ingot growth manufacturing validated that the proposed framework significantly reduces the sample size used for improved computational runtime metrics while outperforming various other data reduction methods in modeling performance.

Improving Drilling Efficiency and Safety Based on Hydraulic Mechanical Specific Energy in the Framework of Economic Model Predictive Control

Summary The economic value of real-time optimization of the drilling process has attracted extensive attention in the period of crude oil price fluctuation. Because of the complicated bottomhole conditions and coupling relationship among subsystems, it is difficult for the driller to continuously adjust the primary manipulation variables—weight on bit (WOB), revolutions per minute (RPM), and managed pressure drilling (MPD) equipment—which leads to unsatisfactory drilling effect and increased nonproductive time. Here, we propose an integrated control strategy based on economic model predictive control with zone tracking (ZoneEMPC), nonlinear model predictive control (NMPC), and Lyapunov-based model predictive control (LMPC) to optimize hydraulic mechanical specific energy (HMSE) while stabilizing bottomhole pressure (BHP). During normal drilling, ZoneEMPC is used to minimize HMSE through operating RPM, WOB, mud pump flow, and choke-valve opening as well as its tracking performance, which ensures that the BHP is maintained within the pressure window. Moreover, the controller is switched to NMPC, which adjusts the BHP to quickly reach the new setting zone. The reason for controller switching is that the tracking accuracy of ZoneEMPC could decline when encountering high-pressure formation with gas invasion. As the BHP reaches the new pressure range, the controller is switched to ZoneEMPC again and the HMSE is reoptimized. In addition, the transmission rate of mud pulse telemetry is not enough to meet the real-time control in practical engineering. Therefore, the LMPC controller is used to ensure the closed-loop stability of the system when the downhole measurement data (e.g., BHP) delayed. This study uses the drilling data of a vertical well located in Tarim, China, to verify the control strategy. The results indicated that the proposed integrated control strategy could improve the rate of penetration (ROP), especially in a formation with high rock strength. Meanwhile, the BHP could be adjusted efficiently and stably under different conditions. This work provides a unified framework integrating multiple system models for practical engineering, which has certain theoretical guiding significance for the automation and intelligent development of the oil and gas industry.

Deforestation intensifies daily temperature variability in the northern extratropics

While the biogeophysical effects of deforestation on average and extreme temperatures are broadly documented, how deforestation influences temperature variability remains largely unknown. To fill this knowledge gap, we investigate the biogeophysical effects of idealized deforestation on daily temperature variability at the global scale based on multiple earth system models and in situ observations. Here, we show that deforestation can intensify daily temperature variability (by up to 20%) in the northern extratropics, particularly in winter, leading to more frequent rapid extreme warming and cooling events. The higher temperature variability can be attributed to the enhanced near-surface horizontal temperature advection and simultaneously is partly offset by the lower variability in surface sensible heat flux. We also show responses of daily temperature variability to historical deforestation and future potential afforestation. This study reveals the overlooked effects of deforestation or afforestation on temperature variability and has implications for large-scale afforestation in northern extratropic countries.

In Malang, Indonesia, a techno-economic analysis of hybrid energy systems in public buildings

Because of the fickle nature of the renewable sources of energy production, professionals in this sector have developed hybrid renewable energy systems (HRES) that offer a constant and stable load supply. This research intends to build off-grid hybrid energy systems (HES) in Malang, Indonesia, that uses a solar generator, wind turbine, and biogas to power public buildings. The HOMER program was used to construct this model. Following the computations, multiple hybrid renewable energy system models are used to analyze each component’s capital cost and also cost of energy (COE). Furthermore, energy output, gas emissions, and a thermoeconomic assessment of several HRES models have been explored. Two ideal HRES models were evaluated: one with a biogas generator and one without. According to the research, employing a generator of biogas would reduce fuel consumption and emissions by 68.3%. This HRES model is impressive in light of Malang’s severe air pollution. Switching from diesel to biogas generator decreases NPC by 6.84%, according to the data.

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

AbstractFor a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total‐effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total‐effect parameter sensitivity index for one system model to total‐effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total‐effect process sensitivity index includes not only the first‐order process sensitivity index for measuring the importance of individual processes but also higher‐order indices that account for process interactions. The total‐effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. The total‐effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*‐functions with analytical solutions of first‐order and total‐effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second‐order and higher‐order process interactions, the first‐order and total‐effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first‐order and total‐effect process sensitivity indices to appreciate the difference between the first‐order impact of a process alone and the overall total‐effect impact of the process itself and its interactions with other processes on a model output.

Statistical modeling and reliability analysis of multiple repairable systems with dependent failure times under perfect repair

In repairable systems, a fundamental aspect to be considered is to predict the reliability of the systems under study. Establishing reliability models for multiple repairable systems, however, is still a challenging problem when considering the dependency or unobserved heterogeneity of component failures. This paper focuses on a special repair assumption, called perfect repair, for repairable systems with dependent failures, where only the failed component is restored to as good as new condition. A frailty model is proposed to capture the statistical dependency and unobserved heterogeneity. The classical inferential method for estimating parameters and the reliability predictors will be shown for models in repairable systems under the assumption of perfect repair. An extensive simulation study is conducted under different scenarios to verify the suitability of the model and the asymptotic consistency and efficiency properties of the maximum likelihood estimators. The proposed methodology can be especially useful for industries that operate with repairable systems subjected to the replacement of parts after failures and to non-quantifiable factors that can interfere with the failure times of these parts. We illustrate the practical relevance of the proposed model on two real data sets. The first deals with a set of 9 sugarcane harvesters, observed during a fixed period of time, whose cutting blades failed several times in this interval. The other deals with a set of 5 dump trucks whose diesel engines also had recurring failures during the observation period. Parametric inference is carried out under the power-law process model that has found significant attention in industrial applications.

Optical Thouless pumping transport and nonlinear switching in a topological low-dimensional discrete nematic liquid crystal array

We theoretically investigate a Thouless pumping scheme in the 1D topological Su-Schrieffer-Heeger (SSH) model for single and multiple band-gaps systems when implemented in a discrete nematic liquid crystal arrangement. For an electrically controlled SSH waveguide array, we numerically demonstrate edge-to-edge light transport at low power levels. On the other hand, at higher powers, the transport is frustrated by light-induced nonlinear defect states, giving rise to robust all-optical switching.

Recharge sources and hydrogeochemical evolution of groundwater in a heterogeneous karst water system in Hubei Province, Central China

Although numerous studies on hydrogeochemistry in karst catchments have been performed, some challenges in understanding their hydrogeochemical behavior remain due to the unique architecture of karst groundwater systems and the high heterogeneity of the karst medium. Here we used a multicriteria approach combining hydrodynamic, hydrochemical, and stable isotopic tools to investigate the impact of heterogeneity on karst hydrogeochemistry in a mountainous area of Hubei Province, Central China. Our results showed that groundwater was dominated by Ca2+, Mg2+, HCO3−, and SO42−, and typically characterized by HCO3–Ca·Mg type in the shallow aquifer and by SO4–Ca·Mg type in the deep aquifer. With increasing circulation depth, water-rock interactions and the heterogeneity of the karst medium produced water with increased Ca2+, Mg2+, Sr2+, and SO42− concentrations, and also increased the dissolved total solids. Calcite, dolomite, and gypsum dissolution in local and intermediate flows were important geochemical processes in groundwater evolution, and calcite precipitation occurred in the regional flow due to dedolomitization, supported by PHREEQC modeling of minerals. The decreased major ions and increased nitrate with increasing δ18O due to the dilution of shallow groundwater by high δ18O recharge and nitrogen fertilizer required during the summer rainy season were found relying on Plots of δ18O vs major ions, which offer an alternative approach to learn seasonal effects of hydrogeochemistry. All advances presented were integrated into a hydrogeochemical conceptual model, including local, intermediate, and regional flows, hydrogeochemical evolution, horizontal and vertical karst zones, hydrochemistry, and stable isotopes of groundwater, providing a typical model for multiple karst groundwater systems in Central China.

29P Biological factors of breast cancer and DFS

Novel molecular characterization of breast cancer with cellular markers has allowed a new classification that offers prognostic value, with predictive categories of disease aggressiveness. Biological factors have predictive and prognostic value in breast cancer patients. Our work evaluates the prognostic value of the bioscore among non-metastatic female breast cancer patients concerning disease-free survival (DFS). We reviewed the clinical data of 317 female patients with non-metastatic surgically treated breast cancer from January 2015 to December 2018 presented at the Clinical Oncology Department of Assiut University Hospital. The biological variables include: pathologic stage (PS), T stage (T), nodal stage (N), grade (G), estrogen receptor (ER), progesterone receptors (PR), and human epidermal growth factor receptor (HER2) status. Univariate & two multivariate analyses were performed to identify variables associated with disease-free survival (DFS). Multiple staging system models were built for significant factors in both univariate and multivariate analyses: PS, PS + G, PS + G + E, T + N, T + N + G, T + N + G + E. The first one used the PS, which takes into account the combined T and N stage as a variable, while the second included T and N stages as separate variables. Model performance was quantified using Harrell’s concordance index (C-index) and the Akaike Information Criterion (AIC) was used to compare model fits. The only significant factors in the univariate analysis were PS3, T2, T3, T4, N3, G2, G3, ER -ve, PR -ve, and HER2 –ve with Hazard Ratio (HR): 4.77, 2.52, 2.80, 5.59, 2.74, 6.92, 16.80, 3.08, 2.11, 0.53 respectively with significant P value (˂ 0.05). The factors which were significant in the first multivariate model were: PS3, G3, ER –ve, and in the second one were: T2, T4, N3, G3, and ER –ve. Two sets of models were built to determine the utility of combining variables. Models incorporating G and E status had the highest C-index (0.72) for (T+N + G + ER) in comparison with (0.69) for (PS+ G + ER) and the lowest AIC (953.01) for (T + N + G + E) and (966.9) for (PS + G + E). Bioscore provides more optimistic prognostic stratification than the anatomic staging alone as regards DFS. It helps clinicians to provide patients with more personalized treatment options.

Lateral subsurface flow modulates forest mortality risk to future climate and elevated CO2

Forest mortality has been widely observed across the globe during recent episodes of drought and extreme heat events. But the future of forest mortality remains poorly understood. While the direct effects of future climate and elevated CO2 on forest mortality risk have been studied, the role of lateral subsurface water flow has rarely been considered. Here we demonstrated the fingerprint of lateral flow on the forest mortality risk of a riparian ecosystem using a coupled plant hydraulics-hydrology model prescribed with multiple Earth System Model projections of future hydroclimate. We showed that the anticipated water-saving and drought ameliorating effects of elevated CO2 on mortality risk were largely compromised when lateral hydrological processes were considered. Further, we found lateral flow reduce ecosystem sensitivity to climate variations, by removing soil water excess during wet periods and providing additional water from groundwater storage during dry periods. These findings challenge the prevailing expectation of elevated CO2 to reduce mortality risk and highlight the need to assess the effects of lateral flow exchange more explicitly moving forward with forest mortality projections.