Probabilistic Simulation Approach Research Articles

Probabilistic and Harmonic Assessment of Power Quality Parameters from Distributed Energy Resources in a Distribution Network

Distributed energy resources (DERs) are leading actors in energy transition worldwide. Despite the environmental benefits, DERs are intermittent (consumption/generation power changes according to the input variables), and the use of power electronic devices affects the power quality in distribution networks. Typically, models and simulations (deterministic or probabilistic) are proposed to estimate such impacts. However, previous studies have analyzed the main effects caused by one or two DERs during a day. These studies had a reduced level of detail in the applied methodology in modeling and impact evaluation, or they did not consider that the harmonic distortion level depends on input variables. The aim of this article was to develop a probabilistic harmonic simulation approach to characterize the hourly effect of photovoltaic systems (PVS), battery energy storage systems, and electric vehicle charging stations in a 34-node IEEE test feeder. The individual and collective effect estimation of the DERs consists of the hourly quantification of the effective voltage value, voltage unbalance, power factor, and harmonic distortion, with data obtained using Python and PowerFactory co-simulation for 23 operation scenarios. These impacts were assessed through indicators proposed in the literature and hypothesis tests (inferential statistics). Each DER generally operates at a specific time, making it easy to identify the individual effects of network parameters. The PVS increased the effective voltage value and total harmonic distortion. However, electric vehicles and batteries increase the voltage unbalance and power factor due to the two-phase connection in some network nodes.

PDEM-based multi-component and global reliability evaluation framework for steel tubular transmission towers with semi-rigid connections

The conventional analysis and design of transmission towers usually ignore the effect of semi-rigid behaviors of bolted joints and simplify the joints as the rigid or pinned connection, which might overestimate or underestimate the structural response of transmission towers and the reliability of structural components and systems. To consider the effect of semi-rigid connections on the reliability of transmission towers in this paper, a stochastic dynamic finite element model of steel tubular transmission towers with semi-rigid connections (STTTs-SRCs) is established by deriving their stiffness matrix, where the uncertainties of dimensions, material and semi-rigid connections are considered and the wind loads are simulated as stochastic processes by the dimension-reduced probabilistic simulation approach. Then, based on the equivalent extreme-value event, the multi-component and global performance functions with stress and displacement failure modes for STTTs-SRCs are established, respectively. Further, a reliability evaluation framework for STTTs-SRCs is proposed based on the probability density evolution method (PDEM). Finally, a practical engineering transmission tower is regarded as an example to demonstrate the implementation process of the proposed reliability evaluation framework. Moreover, the reliability of steel tubular transmission towers with rigid and pinned connections is compared with that of STTTs-SRCs to investigate the effect of semi-rigid connections on the reliability of transmission towers. In general, the semi-rigid connections would mainly affect the value of the multi-component failure probability, however, it would not change the location where the maximum failure probability of components occurs. Additionally, the semi-rigid connection significantly affects the global failure probability of towers for the stress failure mode while slightly affects the global failure probability of towers for the displacement failure mode. Therefore, it may be more appropriate to consider the semi-rigid connections into the analysis and design of steel tubular transmission towers.

Construction of a project risk assessment model based on a probabilistic simulation approach

Manifestations of risk in the economic sphere of human activity have entailed numerous definitions and interpretations, a significant part of which are associated with two statements: a) risk is caused by random events outside the sphere of influence of the risk subject;b) the economic consequences of these events are undesirable or unacceptable for him.An ambiguous interpretation of the original concept leads to an incorrect formulation of the problem of formalizing the process of optimizing investment projects.The paper provides an analysis of the possibility of resolving the problem using a simulation-probabilistic approach.

Verification and validation of automated driving systems utilizing probabilistic FMEA and simulation approaches

One of the main challenges for deploying highly automated driving is the need for a new strategy for verification and validation ensuring a safety and reliability in all traffic scenarios and conditions. In this context, the two projects of the German PEGASUS family VV Methoden and Set Level are focusing on this challenge addressing advanced methods for verification and validation as well as a holistic simulation environment for development and testing of automated driving functions. Within this paper, the overall concept of defining system and test requirements based on a probabilistic FMEA will be presented and discussed in view of applicability and limitations. The discussion will be complemented by outlining the closed-loop simulation approach and demonstration its potential by means of a first XiL test rig for a camera system applying a virtual traffic scenario.

Phthalate Esters from Packaged Milk and Associated Human Health Risk: A Monte Carlo Probabilistic Simulation Approach

Phthalate Esters from Packaged Milk and Associated Human Health Risk: A Monte Carlo Probabilistic Simulation Approach

Creating virtual species to test species distribution models: the importance of landscape structure, dispersal and population processes

The use of virtual species to test species distribution models is important for understanding how aspects of the model development process influence model performance. Typically, virtual species are simulated by defining their niche as a function of environmental variables and simulating occurrence probabilistically via Bernoulli trials. This approach ignores endogenous processes known to drive species distribution such as dispersal and population dynamics. To understand whether these processes are important for simulating virtual species we compared the probabilistic simulation approach to those incorporating endogenous processes. This comparison was done by evaluating changes in the relationship between species occurrence and habitat suitability over a number of landscapes with varying spatial structure. We found that the combined effects of population dynamics and dispersal meant the probability of occurrence of a single cell was not only dependent on habitat suitability, but also the number of occupied cells nearby. This resulted in a dependence on the size of clusters of high suitability cells (analogous to patch size) to maintain populations, increased residual spatial autocorrelation and non‐stationarity of the species response between landscapes. These data characteristics are attributes of real species distribution data and are not present in probabilistic simulations. Researchers using virtual species should consider the importance of these characteristics to their study objectives to decide whether the inclusion of endogenous processes is necessary.

Probabilistic evaluations on marginal price and capacity adequacy of power systems with price-elastic demand

An increasing deployment of smart grid technologies and demand response programs has enabled demands to be more elastic in response to electricity prices, actively contributing to power system operation. However, demand-price elasticity could potentially impact accuracy of electricity price forecasting and generation capacity adequacy evaluation, which have not been thoroughly explored in literature. To this end, a probabilistic model is developed to evaluate marginal electricity price and capacity adequacy of power systems with elastic demand, while considering uncertainties of both demand and supply sides (e.g., random outages of generating units with multi-bidding-segments as well as variability of renewable energy outputs). Specifically, based on the concept of marginal bidding segment, the probability of a bidding segment being marginal is computed via the probabilistic production simulation approach. Accordingly, the discrete probability distribution of marginal electricity price can be obtained, which is further used to calculate mean and variance of marginal electricity price with elastic demand. Moreover, considering the interaction between elastic demands and uncertain electricity prices, the loss-of-load probability and the expected unserved energy are further estimated. Numerical studies are presented to validate effectiveness of the proposed method.

Heuristic search strategy based on probabilistic and geostatistical simulation approach for simultaneous identification of groundwater contaminant source and simulation model parameters

In this study, a heuristic search strategy based on probabilistic and geostatistical simulation approach is developed for simultaneous identification of groundwater contaminant source and simulation model parameters. The numerical simulation model, which is repeatedly invoked to evaluate the likelihood in Bayesian formula, can be substituted by the surrogate system to reduce the huge computational load. To improve the approximation accuracy of the surrogate system to the simulation model, we employ Entropy Weight method as a novel method to establish a combined surrogate system by combining Gaussian process, support vector regression, and kernel extreme learning machine. A state evaluation (heuristic) function based on Bayesian formula and the surrogate system is introduced to quantify the approximation degree of variables in current state to true values for contaminant sources and simulation model parameters. Thereafter, a heuristic search iterative process related to artificial intelligence is designed for simultaneous identification, which takes full advantage of the guidance and correction role of actual field monitoring data. A multi-vector and variable-step size random walk method is proposed to select the candidate point. A Metropolis formula based on the state evaluation function is constructed, and the result is used as the judging criterion for the state transition. Finally, simultaneous identification results are obtained when the iteration reaches the convergence criteria. The proposed approaches are tested with a numerical case study. The results indicate that the heuristic search strategy can assist in identifying groundwater contaminant source and simulation model parameters simultaneously with high accuracy and efficiency.

Speed-Up in Test Methods Using Probabilistic Merit Indicators

Deterministic test generation methods are time consuming and, this has led to emergence of simulation-based approaches. The basis of simulation-based methods is to propose a number of test vectors, evaluate the efficiency of the proposed vectors, and accept or reject them. In these methods, the efficiency of each test vector is traditionally evaluated based on fault coverage of that vector. In this paper an alternative criterion based on probabilistic simulation approaches is proposed that can be calculated much faster than fault coverage. Statistical evaluations confirm a strong correlation between the proposed measure and fault coverage. The proposed measure can be used in simulation-based and meta-heuristic test generation methods to achieve speedup. In order to indicate efficiency of the proposed probabilistic measure, two test generation methods that utilize this measure are suggested and exploited to generate test for ISCAS85 benchmark circuits. These test generation methods are called PMI_PRUNE and PMI_GA. Our results demonstrate that PMI_PRUNE can achieve up to 21.2x, while the maximum speedup in PMI_GA is 5.0x. The average speedup in PMI_PRUNE and PMI_GA is about 9.6x, and 2.7x respectively.

Probabilistic Evaluation of Off-Fault Displacements of the 2016 Kumamoto Earthquake

The 2016 Kumamoto earthquake occurred on April 16, 2016, and induced the complex distribution of surface ruptures and fault displacements, which revealed the difficulty of evaluating off-fault displacement hazards. The evaluation of off-fault displacements is necessary for the site investigation of an important facility, such as nuclear installations, but is difficult because off-fault displacements are thought to occur on unmapped or small-scale structures. A probabilistic fault displacement hazard analysis (PFDHA) assesses an off-fault displacement hazard as well as an on-fault one. Several PFDHA models have been developed from fault displacement datasets parsed by global-specific and Japanese-various faulting types. We developed a procedure to assess off-fault displacement hazards based on probabilistic and numerical simulation approaches. In the probabilistic approach, we constructed strike-slip and reverse-slip PFDHA models from a dataset of Japanese fault displacements. The off-fault components in PFDHA had a probability of non-zero off-fault displacement and an attenuation relationship. In the case of evaluating the site of an important facility, detailed field surveys were performed to collect numerous site-specific information. There is little site-specific data of off-fault displacements in Japan. The trench surveys were performed at off-fault displacement sites from the 2016 Kumamoto earthquake. The two trench sites were selected based on a linear low-coherence zone derived from the InSAR analysis, which emphasizes small-scale displacements. The excavation survey result at one site suggests a shorter recurrence interval than that of the active fault long-term evaluation. We also performed a probabilistic fault hazard analysis (PFDHA) of the trench sites by using the global strike-slip model and Japanese PFDHA models with and without the site-specific data. For the PFDHA results without site-specific data, the difference in the annual rates of exceedance between the global strike-slip and Japanese models increased with increasing fault displacements. This is because of the reduced decrease in the attenuation relationship of Japanese models. The differences in the annual rates of exceedance constrained with the site-specific data are independent of fault displacements due to variation in the probability of non-zero off-fault displacement between the global strike-slip and Japanese PFDHA models. These results suggest that the off-fault PFDHA models have high variability. In the case of PFDHA evaluation with several models, the individual PFDHA components are carefully selected by considering the individual effects. The site-specific short recurrence interval revealed by the trench survey leads to higher annual rates of exceedance.

Detecting outliers in species distribution data: Some caveats and clarifications on a virtual species study

AbstractLiu et al. (2018) used a virtual species approach to test the effects of outliers on species distribution models. In their simulations, they applied a threshold value over the simulated suitabilities to generate the species distributions, suggesting that using a probabilistic simulation approach would have been more complex and yield the same results. Here, we argue that using a probabilistic approach is not necessarily more complex and may significantly change results. Although the threshold approach may be justified under limited circumstances, the probabilistic approach has multiple advantages. First, it is in line with ecological theory, which largely assumes non‐threshold responses. Second, it is more general, as it includes the threshold as a limiting case. Third, it allows a better separation of the relevant intervening factors that influence model performance. Therefore, we argue that the probabilistic simulation approach should be used as a general standard in virtual species studies.

The dark figure of sexual recidivism.

Empirical studies of sexual offender recidivism have proliferated in recent decades. Virtually all of the studies define recidivism as a new legal charge or conviction for a sexual crime, and these studies tend to find recidivism rates of the order of 5-15% after 5years and 10-25% after 10+ years. It is uncontroversial that such a definition of recidivism underestimates the true rate of sexual recidivism because most sexual crime is not reported to legal authorities, a principle known as the "dark figure of crime." To estimate the magnitude of the dark figure of sexual recidivism, this paper uses a probabilistic simulation approach in conjunction with the following: (i) victim self-report survey data about the rate of reporting sexual crime to legal authorities; (ii) offender self-report data about the number of victims per offender; and (iii) different assumptions about the chances of being convicted of a new sexual offense given that it is reported. Under any configuration of assumptions, the dark figure is substantial, and as a consequence the disparity between recidivism defined as a new legal charge or conviction for a sex crime and recidivism defined as actually committing a new sexual crime is large. These findings call into question the utility of recidivism studies that rely exclusively on official crime statistics to define sexual recidivism, and highlight the need for additional, long-term studies that use a variety of different measures to assess whether or not sexual recidivism has occurred.

A novel probabilistic simulation approach for forecasting the safety factor of slopes: a case study

Stabilization of slopes is considered as the aim of the several geotechnical applications such as embankment, tunnel, highway, building and railway and dam. Therefore, evaluation and precise prediction of the factor of safety (FoS) of slopes can be useful in designing these important structures. This research is carried out to evaluate the ability of Monte Carlo (MC) technique for the forecasting the FoS of many homogenous slopes in the static condition. Moreover, the sensitivity of the FoS on the effective parameters was identified. To do this, the most important factors on FoS, such as angle of internal friction $$(\emptyset )$$ , slope angle $$(\alpha )$$ and cohesion $$(C)$$ were investigated and used as the inputs to forecast the FoS. Then, a regression analysis was performed, and the results were used for the FoS prediction using MC. The obtained results of MC simulation were very close with the actual FoS values. The mean of the simulated FoS by MC was achieved as 1.32, while, according to actual FoSs, it was 1.27. These results showed that MC is an acceptable technique to estimate the FoS of slopes with high level of accuracy. Moreover, based on the results of correlation and regression sensitivity analyses, it was concluded that angle of internal friction, was the most influential one on the results of FoS in both types of sensitivity analyses.

A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment

The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

ABSTRACTArtificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.

Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system production simulation. Existing power system probabilistic production simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic production simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.

Probabilistic-numerical assessment of pyroclastic current hazard at Campi Flegrei and Naples city: Multi-VEI scenarios as a tool for "full-scale" risk management.

The Campi Flegrei volcanic field (Italy) poses very high risk to the highly urbanized Neapolitan area. Eruptive history was dominated by explosive activity producing pyroclastic currents (hereon PCs; acronym for Pyroclastic Currents) ranging in scale from localized base surges to regional flows. Here we apply probabilistic numerical simulation approaches to produce PC hazard maps, based on a comprehensive spectrum of flow properties and vent locations. These maps are incorporated in a Geographic Information System (GIS) and provide all probable Volcanic Explosivity Index (VEI) scenarios from different source vents in the caldera, relevant for risk management planning. For each VEI scenario, we report the conditional probability for PCs (i.e., the probability for a given area to be affected by the passage of PCs in case of a PC-forming explosive event) and related dynamic pressure. Model results indicate that PCs from VEI<4 events would be confined within the Campi Flegrei caldera, PC propagation being impeded by the northern and eastern caldera walls. Conversely, PCs from VEI 4–5 events could invade a wide area beyond the northern caldera rim, as well as part of the Naples metropolitan area to the east. A major controlling factor of PC dispersal is represented by the location of the vent area. PCs from the potentially largest eruption scenarios (analogous to the ~15 ka, VEI 6 Neapolitan Yellow Tuff or even the ~39 ka, VEI 7 Campanian Ignimbrite extreme event) would affect a large part of the Campanian Plain to the north and the city of Naples to the east. Thus, in case of renewal of eruptive activity at Campi Flegrei, up to 3 million people will be potentially exposed to volcanic hazard, pointing out the urgency of an emergency plan. Considering the present level of uncertainty in forecasting the future eruption type, size and location (essentially based on statistical analysis of previous activity), we suggest that appropriate planning measures should face at least the VEI 5 reference scenario (at least 2 occurrences documented in the last 10 ka).

An evaluation method to predict progressive collapse resistance of steel frame structures

In the current work, a probabilistic assessment method of a steel framed building under abrupt removal of a column due to catastrophic events is developed. A multi-story steel framed model taking into account the influence of catenary effect has been analyzed. Uncertainties in the structural variables are incorporated in the probabilistic simulation approach. Based on the changes of component internal energy, the progressive collapse sensitivity to abrupt removal of a column has been investigated. Besides, a simplified beam damage model is proposed to analyze the energies absorbed and dissipated by structural beams under large deflections. In addition, the correlation incorporating catenary action between bending moment and axial force in a beam during the whole deformation development process is studied. With the methodologies adopted for progressive collapse assessment under removal of a column, a deterministic method has been developed, framed within the Advanced First Order Reliability Method (AFORM). A robustness index (RI) is proposed to evaluate the structural robustness performance based on the acceptable probability of global failure and structural collapse probability.

Efficient and consistent reliability analysis of soil slope stability using both limit equilibrium analysis and finite element analysis

Limit equilibrium methods (LEMs) and finite element methods (FEMs) of slope stability analysis can be used in computer-based probabilistic simulation approaches (e.g., direct Monte Carlo Simulation (MCS) and Subset Simulation (SS)) to evaluate the slope failure probability (Pf). For a given slope problem, the computational effort for the LEM is generally much less than that required for the FEM, but the FEM tends to give a more realistic prediction of slope failure mechanism and its associated factor of safety. To make use of the advantages of both the LEM (e.g., computationally more efficient) and FEM (e.g., theoretically more realistic and rigorous in terms of slope failure mechanisms), a new probabilistic simulation method is developed in the paper. The proposed approach combines both a simple LEM (i.e., Ordinary Method of Slices considering a limited number of potential slip surfaces) and FEM with the response conditioning method to efficiently calculate Pf of slope stability and to give an estimate of Pf consistent with that obtained from directly performing MCS and SS based on the FEM. It is illustrated through two soil slope examples. Results show that the proposed approach calculates the Pf properly at small probability levels (e.g., Pf < 0.001). More importantly, it significantly reduces the number of finite element analyses needed in the calculation, and therefore improves the computational efficiency at small probability levels that are of great interest in slope design practice. In addition, the proposed approach opens up the possibility that makes use of the information obtained using a simple model (e.g., LEM) to guide the reliability analysis based on a relatively sophisticated model (e.g., FEM).

Evaluation of probabilistic snow avalanche simulation ensembles with Doppler radar observations

In recent years snow avalanche simulation software, which is based on depth-averaged models operating in three-dimensional terrain, has gained importance. Simulations are used to plan protection measures. The software computes the spatio-temporal evolution of flow depth and velocity and is optimized to determine runout distances of avalanches. However, considering the complex output of these computer models no sophisticated evaluation procedure exists to compare the velocity output with experimental data. In this study we present a new method to objectively evaluate the velocity results of the simulation software SamosAT with Doppler radar measurements from two European snow avalanche test sites. A coordinate transformation allows comparing the maximum simulated velocities with measured data. A probabilistic simulation approach is applied, performing and evaluating a large number of simulation runs. The simulation results are evaluated in two ways. Firstly, an average evaluation is used to obtain a discrepancy estimate, representing the deviance between simulations and measurements and to explore the range of possible velocity results accounting for different uncertainties in the input parameters. Secondly, an optimized evaluation determines the magnitude of possible accordance between simulation and measurement. It reveals that for the employed simulation approach release depth and certain friction coefficients are crucial parameters to obtain optimal correspondence of simulated and measured velocities. In the investigated cases, simulation and observation show a reasonable accordance with a tendency of velocity underestimation. The presented approach is a valuable method for the evaluation and comparison of complex model outputs.