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In system reliability analysis, researchers are interested in evaluating the characteristics of the lifetime distribution of components in a system based on system lifetime data. In collecting system lifetime data, a censoring scheme is often adopted due to time and budget constraints. In this article, we consider the situation where the system lifetime data from two different coherent systems are subjected to Type‐II right‐censoring, and we are interested in testing the homogeneity of component lifetime distributions based on Type‐II censored system lifetime data with known system structures. Based on the Mann‐Whitney U$$ U $$ test and empirical likelihood ratio tests developed for testing the homogeneity of component lifetime distributions with complete system lifetime data, we propose different non‐parametric test procedures using the permutation of the unobserved censored system lifetimes. We consider a restricted assumption on the censored lifetimes to reduce the permutations required in the computation. The computational approaches to obtain the critical values of the proposed test procedures are provided using the Monte Carlo method. A practical example is used to illustrate the proposed test procedures. Then, the power performance of the proposed test procedures is evaluated and compared through a Monte Carlo simulation study. The simulation results show that the proposed test procedures provide comparable power performance for Type‐II censored system lifetime data in contrast to the complete sample case under different scenarios. Finally, recommendations based on the simulation results and concluding remarks are provided. [ABSTRACT FROM AUTHOR]

The research on the randomness and volatility of wind power (WP) and photovoltaic (PV) output of the integrated energy system (IES) has emerged as a pivotal concern, commonly dealt with by clustering techniques. However, traditional clustering techniques often fall short of capturing the comprehensive characteristics of the original scenery data. This paper presents an enhanced clustering logic to improve WP and PV output correlation scenarios. Utilizing the Frank‐Copula function, the complex relationship between WP and PV is accurately described. The Kendall rank correlation coefficient (Kendall′s tau) is used as a metric to study the correlation between these two renewable energy sources. The original scenes' correlation coefficients are then clustered using the K‐means algorithm, which forms the basis for scene reduction. On the basis, a two‐phase robust optimization method is employed to conduct a planning study for a real IES, with the total cost of the system as the optimal objective. This approach ensures that the planning results are better aligned with the specific local conditions during the operation cycle. [ABSTRACT FROM AUTHOR]

Background: In radiopharmaceutical therapies (RPT) involving beta emitters, absorbed dose (Dabs) calculations often employ the use of dose voxel kernels (DVK). Such methods are faster and easier to implement than Monte Carlo (MC) simulations. Using DVK methods implies a non‐stochastic distribution of particles. This is a valid assumption for betas where thousands to tens of thousands of particles traversing the cell nucleus are required to achieve cell kill. However, alpha particles have linear energy transfers (LET) that are ∼500 times higher than LETs of betas. This results in a significant probability of killing a cell from even a single traversal through its nucleus. Consequently, the activity used for therapy involving alphas is very low, and the use of DVKs for estimating Dabs will generate results that may be erroneous. Purpose: This work aims at illustrating how use of DVKs affect the resulting Dabs in small tumors when irradiated with clinically relevant amounts of beta‐ and alpha‐emitters. The results are compared with those from using a Monte Carlo method where the energy deposition from individual tracks is simulated. Methods: To illustrate the issues associated with DVK for alpha radiopharmaceutical therapies at the microscale, a tumor cluster model was used to compare beta (177Lu) and alphas (211At, 225Ac, and 227Th) irradiations. We used 103 beta particles and 20 alpha particles per cell, which is within the range of the required number of particle traversals through its nucleus to sterilize a cell. Results from using both methods were presented with Dabs histograms, dose volume histograms, and Dabs error maps. Results: For beta‐emitter (177Lu) irradiating the modeled tumor cluster, resulting Dabs was similar for both DVK and MC methods. For all alpha emitters, the use of DVK led to an overestimation of Dabs when compared to results generated using a MC approach. Conclusions: Our results demonstrate that the use of DVK methods for alpha emitters can lead to an overestimation in the calculated Dabs. The use of DVKs for therapies involving alpha emitters may therefore not be appropriate when only referring to the mean Dabs metric. [ABSTRACT FROM AUTHOR]

The ongoing electrification of road transportation sector, which is expected to continue to strongly increase over the next years, will result in the connection of a significant number of electric vehicle (EV) chargers in LV and MV distribution networks, particularly in residential applications with on‐board ("slow") EV chargers. In order to evaluate loading limits of existing distribution networks for the maximum number of EV chargers that can be safely connected (commonly denoted as a network EV "hosting capacity", HC), this paper introduces a general approach to determine one commonly used network design parameter (after‐diversity maximum demand, ADMD) and one new parameter (maximum daily energy demand, MDED), which are both obtained from the load profiles of maximum per‐hour demands for uncontrolled residential EV charging. The presented approach uses actual EV charging data from the UK as the inputs in Monte Carlo simulations to generate daily EV charging profiles for arbitrary numbers of EVs, enabling to identify related ADMD, MDED and per‐hour maximum demand values, as well as their seasonal variations. The assessed ADMD, MDED and hourly maximum EV charging demands for uncontrolled EV charging are then combined with available UK residential daily load profiles before the EVs are connected ("pre‐EV demands"), where their combined coincidental and noncoincidental maximum demands are evaluated against the static thermal rating (STR) and dynamic thermal rating (DTR) loading limits of network components (transformers and overhead lines), taking into account relevant weather/ambient conditions. This is denoted as a network HC for uncontrolled EV charging. Finally, evaluating the resulting per‐hour maximum demand values against the STR and DTR loading limits and MDED values allows to select one particular scheduling method for controlled EV charging, which gives the absolute maximum number of EVs that can be safely connected in the considered network, that is, maximum network HC for fully controlled EV charging. The presented approach is illustrated on the example of the IEEE 33‐bus test network (modelled using typical UK network components), for the pre‐EV residential demands taken from the recordings at a UK MV substation, and for ambient data taken from a UK Met Office weather station. Obtained results allow to evaluate the range of network EV HC values for uncontrolled and controlled EV charging, that is, lower and upper HC limits, which can be correlated with the commonly used allocations of the firm and non‐firm network HC, respectively. [ABSTRACT FROM AUTHOR]

The still growing share of intermittent renewable sources in the electric energy matrix is a factor that greatly increases the complexity of modelling electrical power systems. Although its exploitation is conditioned to the geographic location where the natural resource emerges itself, when this location coincides with the grid‐edge of the system, close to the consumer, the composite reliability assessment is exposed to the combination of two problems: (i) representation of the intermittent energy availability of generators; and (ii) failure event rarity. To address the first problem, a fast Monte Carlo tool capable of representing chronological aspects can be used. For the second problem, an efficient variance reduction technique that adapts well to the composite problem of generation and transmission must be established. Therefore, a quasi‐sequential Monte Carlo simulation tool aided by importance sampling via the cross‐entropy method is proposed as an efficient and robust procedure for evaluating the composite reliability of systems with renewable participation at grid‐edge. The method is evaluated through a modified version of the IEEE Reliability Test System ‐ 1996, which presents renewable generation in consumer load buses and rarity in the composite failure. [ABSTRACT FROM AUTHOR]

Mobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies. [ABSTRACT FROM AUTHOR]

This study presents an approach to better characterize the P‐wave and S‐wave velocity structure of the seafloor sediment layer using ocean bottom seismometers. The presence of low‐velocity seafloor sediment layers influences the observed seismic record at the seafloor over a broad frequency range, such that detailed knowledge of this sediment structure is essential to predict its effect on teleseismic records. We use the radial component of teleseismic P waves and autocorrelation functions of the radial, vertical, and pressure components of teleseismic P and S waves to obtain sediment layer models using the Markov chain Monte Carlo approach with parallel tempering. Synthetic tests show that the body waves constrain the P‐ and S‐wave impedances and travel times and the P‐ to S‐wave velocity ratio of the sediment layers. The proposed method resolves thin layers at a high resolution, including the uppermost thin (∼50 m to a few hundred meters) low S‐wave velocity layer. Real data applications at sites across the Pacific Ocean that are coincident with previous in situ studies demonstrate the effectiveness of this method in characterizing the seafloor sediment unit. The sediment models characterized by this new approach will allow us to more accurately predict and correct the effects of sediment layers in generating P‐ and S‐wave reverberations. Plain Language Summary: Ocean bottom seismometers are powerful tools for investigating the detailed structure of the oceanic mantle. However, most ocean bottom seismometers are placed above the low‐velocity water‐saturated sediment layer, such that their seismic records are inevitably affected by this seafloor sediment layer. Detailed constraints on the structure of the seafloor sediment layer allow us to accurately predict its effects on the observed seismic records. The seafloor sediment layer may consist of multiple discontinuities and vary regionally. This study proposes a new approach for characterizing the seismic properties of the seafloor sediment layer using teleseismic body waves recorded at ocean bottom seismometers. Synthetic tests show that the method can resolve thin sublayers within the sediment layer, such as a very thin low shear‐wave velocity layer and sharp seismic velocity jumps. Real data applications show that the sediment models obtained using this approach are in good agreement with those obtained from direct measurements and previous body‐wave analyses. Our proposed method will enable more detailed prediction and correction of the influence of the sediment layer on ocean bottom seismometer records, thereby providing more accurate constraints on deeper Earth structures. Key Points: The seismic properties of the seafloor sediment layer are characterized using ocean bottom seismometer teleseismic body‐wave recordsThe P‐ and S‐wave impedances and travel times and the VP/VS values of each sublayer within the sediment layer are resolvedIn situ seafloor sediment structure constraints will enable accurate correction and prediction of shallow structure effects [ABSTRACT FROM AUTHOR]

A well‐liked maneuvering target tracking algorithm is a variable structure multi‐model (VSMM). One of the crucial elements determining the tracking effect is the successful model set adaptation (MSA). The ability to further enhance tracking accuracy for the conventional VSMM method is constrained by the absence of a mechanism to thoroughly utilise observation and tracking data to optimise MSA. We incorporate the Reinforcement learning (RL) approach into the MSA procedure to address this issue and provide a VSMM algorithm based on Monte Carlo (MC) learning. To formulate the challenge of optimising the number of effective models as a RL problem, we first used the prediction error, the number of effective model sets, and tracking accuracy to build the models of the appropriate state space, decision space, and reward. The number of efficient models was optimised using MC learning, and the entire VSMM algorithm was then created. The proposed approach was compared to the simulated experiment's five maneuvering target tracking algorithms. The outcomes demonstrate that the suggested algorithm has a lower computation scale and accurate tracking accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration, and mainly addresses issues: 1) insufficient consideration of the characteristics for the components and distributed generations (DGs) of distribution network such as time sequences, randomness and intermittency; 2) changes in the feeder area of the distribution network after the access of DGs; and 3) multi‐dimensional reliability index for distribution network with DGs has not been formed perfectly. Firstly, Markov reliability model for distribution network components/DGs is established in our study. Then, considering the fault recovery and network reconfiguration of the feeder area, island partitioning method of distribution network with DGs is also proposed. Moreover, a reliability evaluation model is presented for distribution network with DGs based on sequential Monte Carlo method. Case study is performed on the improved IEEE RBTS BUS6 F4 feeder system, and the research results indicate the validity and effectiveness of the proposed method. In addition, the system reliability indicators, such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), and average system availability index (ASAI), are studied and discussed in detail from three dimensions: the access of DGs, the types of DGs, and the capacity of DGs. [ABSTRACT FROM AUTHOR]

A Monte Carlo (MC) method was introduced into a state-of-the-art model used to analyse small-angle X-ray scattering (SAXS) data of SBA-15, an ordered mesoporous material with many applications. With this new procedure, referred to herein as the SBA-15+MC model, it is possible to retrieve the size distribution of the mesopores, D(r), in a free modelling approach. To achieve this, two main points were addressed: (i) based on previous implementations, the method was adapted to work with long core-shell cylinders; (ii) since the MC model requires longer processing times, strategies to speed up the calculations were developed, which included a simplified version of the original model used to analyse SAXS data of SBA-15 (referred to as the SBA-15 model) as well as the determination of several structural features from the SAXS curve prior to the fit. The new model was validated with simulated data and later used to fit experimental SAXS curves of SBA-15. The obtained results show that the SBA-15 model only works well because the mesopore size distribution of SBA-15 is narrow, whereas the new approach can be successfully used in cases where D(r) is wider and/or has a more complex profile, such as SBA-15 with expanded mesopores. Even though a specific SAXS example was chosen to prove the model, the strategies presented herein are general and suitable for inclusion in other models aimed at the analysis of SBA-15 and similar ordered mesoporous materials. [ABSTRACT FROM AUTHOR]

A method is proposed for optical characterisation of materials, which is a very important input for realistic channel simulation based on Monte‐Carlo Ray‐Tracing algorithms. This original approach consists first of all in carrying out some measurements of the optical power received after propagation in the environment containing the materials sought, using a simple and low‐cost experimental setup. In a second step, this approach is based on an optimization algorithm. It takes as input the optical power measurements made, associated with the parameters of the measurement environment, such as the positions or properties of the sensors. This algorithm searches for the parameters of the material reflection models, minimising the difference between the optical measurement and the simulation. Two cost functions are studied to perform this search and showed that the correlation measure is the more robust one. To avoid uncertainties in the real input data, this approach is discussed using only a virtual configuration with well‐controlled input data and thus a virtual measurement obtained by simulation. The results show that this method produces a correct estimate of the Bidirectional Reflectance Distribution Function (BRDF) albedos, provided that the chosen BRDF models correspond well to the reflection behaviour of the materials, and that the materials have a significant influence on the measured optical power. [ABSTRACT FROM AUTHOR]

Localization is one of the essential problems in wireless sensor applications (WSNs). Most range‐free localization schemes for mobile WSNs are based on the Sequential Monte Carlo (SMC) algorithm. Multiple iterations, sample impoverishment and less sample diversity, leading to low localizing efficiency, are the most usual problems demanding to be solved in these SMC‐based methods. An improved localization scheme for mobile aquaculture WSNs based on the Improving Dynamic Population Monte Carlo Localization (I‐DPMCL) method is proposed. A population of probability density functions is proposed to approximate the unknown location distribution based on a set of observations through an iterative mixture importance sampling procedure, accompanied by node dynamic behaviours being analysed quantitatively or definitely. Threefold constrain rules are put forward in the I‐DPMCL scheme to decrease the iteration number and trade off iteration number and enough valid samples to obtain the optimum iteration number. Then, these localization behaviours, especial delay, are predicted based on the statistical point of view. Moreover, performance comparisons of I‐DPMCL with other SMC‐based schemes are also proposed. Simulation results show that delay of I‐DPMCL has some superiority to those of other schemes, and accuracy and energy consumption are improved in some cases of lower mobile velocity. [ABSTRACT FROM AUTHOR]

A first‐order finite‐state Markov chain (FSMC) typically models the Rayleigh fading channel in the open literature because the first‐order FSMC is analytically tractable and can derive closed‐form results. Non‐orthogonal multiple access (NOMA) has been recognised as a novel wireless technology that addresses challenges in the next generation of mobile communications. According to the power‐domain NOMA protocol, channels in the NOMA wireless network are sorted by the channel gain. Then considering NOMA, there is insufficient information on how to further form a suitable model for ordered Rayleigh fading channels based on the first‐order FSMC. Given the mathematical statement on how to model the order statistics of multidimensional Markov chains for ordered Rayleigh fading channels, the authors consider these order statistics as a Markov chain, and propose specific processes of representing the state space and constructing the transition probability matrix accordingly. Numerical and simulation results validate the mathematical correctness and accuracy of these novel processes. In addition, for ordered Rayleigh fading channels, the performances of various methods of partitioning the entire signal‐to‐noise ratio range are compared. The performance comparison results are the same as those obtained for the individual unordered Rayleigh fading channel. [ABSTRACT FROM AUTHOR]

We present the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection, proposed by Liu F.T., Ting K.M. and Zhou Z. H. in their seminal work as a heuristic method for anomaly detection in Big Data. We prove that the IRF space can be endowed with a probability induced by the Isolation Tree algorithm (iTree). In this setting, the convergence of the IRF method is proved, using the Law of Large Numbers. A couple of counterexamples are presented to show that the method is inconclusive and no certificate of quality can be given, when using it as a means to detect anomalies. Hence, an alternative version of the method is proposed whose mathematical foundation is fully justified. Furthermore, a criterion for choosing the number of sampled trees needed to guarantee confidence intervals of the numerical results is presented. Finally, numerical experiments are presented to compare the performance of the classic method with the proposed one. [ABSTRACT FROM AUTHOR]

Summary: The effective sample size (ESS) is widely used in sample‐based simulation methods for assessing the quality of a Monte Carlo approximation of a given distribution and of related integrals. In this paper, we revisit the approximation of the ESS in the specific context of importance sampling. The derivation of this approximation, that we will denote as ESS^, is partially available in a 1992 foundational technical report of Augustine Kong. This approximation has been widely used in the last 25 years due to its simplicity as a practical rule of thumb in a wide variety of importance sampling methods. However, we show that the multiple assumptions and approximations in the derivation of ESS^ make it difficult to be considered even as a reasonable approximation of the ESS. We extend the discussion of the ESS^ in the multiple importance sampling setting, we display numerical examples and we discuss several avenues for developing alternative metrics. This paper does not cover the use of ESS for Markov chain Monte Carlo algorithms. [ABSTRACT FROM AUTHOR]

Passive towed arrays assume great importance in shallow ocean surveillance, owing to their capability to detect long range underwater acoustic targets. The detection and classification performance is severely impacted by the tow ship interference manifestation in the pressure sensor array towed behind the ship. In this work, a robust tow ship multipath interference mitigation algorithm intended for shallow ocean operation is developed, based on the dynamic mode decomposition (DMD) technique, which in turn exploits the coherent structure of the multimode acoustic wave propagation inherent in the shallow ocean scenario. The proposed algorithm effectively identifies the coherent normal mode interference subspace and eliminates its components from the received array data vector. Further, this approach compliments the shallow ocean normal mode acoustic field propagation and ensures simultaneous mitigation of all the multipath generated interferences, making it a superior method to reduce the azimuth spread of the interference and tow ship spectral leakage into the panoramic detection beams. This greatly enhances the detection and classification capability of sonar, especially for distant stealthy targets. The efficacy of the algorithm is validated using Monte Carlo simulations and further ratified by experimental data captured during the field trials in the Arabian Sea. [ABSTRACT FROM AUTHOR]

Biotic and abiotic factors drive assortative mixing (preference for or sorting with individuals with similar characteristics) in animal populations on a landscape, with implications for dispersal, population structuring, and other ecological and evolutionary processes. However, patterns and generative mechanisms of assortative mixing are overlooked in amphibians outside of specific life history events such as reproduction. The aims of this project were to determine whether there is assortative mixing by size and life history category in eastern spadefoots (Scaphiopus holbrookii), whether these patterns are preserved across time and spatial scale, and quantify the nature and relative role of various habitat and soil features in explaining observed patterns in spatial organization of individuals. We conducted field surveys in southeastern Virginia, USA, in 2016 and 2017 during nonbreeding periods to create spatial networks of S. holbrookii. We quantified spatial assortativity by size and life history stage and evaluated the roles of multiple landscape features in explaining spatial organization of S. holbrookii. We found that S. holbrookii sorted spatially by size and sex outside of breeding periods, with juveniles and adults less likely to sort with each other. Within each life history stage, S. holbrookii sorted by size. These patterns were similar across time and spatial scale. Soil and habitat types had no effect on assortativity. Instead, the distance to nearest breeding pool, wetland, and meadow were related to life history stage assortativity, as well as size assortativity in males and subadults. Adult males and females displayed affinity for breeding pools and meadows and avoidance of other types of wetlands, while subadults and nonbreeding adults showed opposite patterns compared with breeding adults. Our results indicate that (1) previously established guidelines for the minimum size of buffer zones to protect wetland‐breeding amphibians may be inadequate, (2) nonbreeding wetlands may be important core habitat for subadults, and (3) the upland spatial organization of amphibians may be used to predict locations of undetected breeding pools. [ABSTRACT FROM AUTHOR]

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling‐based approach that has widely used for quantification and propagation of uncertainties. However, the standard MC method is often time‐consuming if the simulation‐based model is computationally intensive. This article gives an overview of modern MC methods to address the existing challenges of the standard MC in the context of UQ. Specifically, multilevel Monte Carlo (MLMC) extending the concept of control variates achieves a significant reduction of the computational cost by performing most evaluations with low accuracy and corresponding low cost, and relatively few evaluations at high accuracy and corresponding high cost. Multifidelity Monte Carlo (MFMC) accelerates the convergence of standard Monte Carlo by generalizing the control variates with different models having varying fidelities and varying computational costs. Multimodel Monte Carlo method (MMMC), having a different setting of MLMC and MFMC, aims to address the issue of UQ and propagation when data for characterizing probability distributions are limited. Multimodel inference combined with importance sampling is proposed for quantifying and efficiently propagating the uncertainties resulting from small data sets. All of these three modern MC methods achieve a significant improvement of computational efficiency for probabilistic UQ, particularly uncertainty propagation. An algorithm summary and the corresponding code implementation are provided for each of the modern MC methods. The extension and application of these methods are discussed in detail. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Monte Carlo MethodsStatistical and Graphical Methods of Data Analysis > Sampling [ABSTRACT FROM AUTHOR]

To assess the efficacy of conservation translocations, survival of released individuals is typically compared to that of control groups. Such comparisons assume that treatment groups consist of otherwise equivalent individuals. When that assumption is unmet, incorporating physiological parameters may improve assessment of translocation programs. During 2012–2014, 19 weaned female Hawaiian monk seal pups were translocated to sites where survival prospects were expected to be more favorable than at their natal locations. We compared survival from weaning to age two years of translocated pups to two control groups; pups remaining at source sites and pups native to destination sites. To account for the known relationship between weaning girth and survival, we generated probability distributions of the number of survivors at source and destination sites given the weaning girths of translocated seals. Data were available to calculate girth‐adjusted survival probabilities for 13 of the translocated pups. Of these, we estimated that only one pup would have been expected to have survived had the translocated pups remained at their natal site. Seven of the 13 translocated seals survived, a value just below the median (eight) expected to have survived at the destination site. Thus, translocation substantially improved survival. Had we not accounted for weaning girth effects on survival, we would have erroneously concluded that the translocation program had yielded no survival benefit. Identifying and integrating correlates of survival into quantitative analyses associated with conservation translocations can reduce bias and lead to greater success. [ABSTRACT FROM AUTHOR]

Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single‐column versions of climate models with explicit simulations of boundary‐layer dynamics and clouds (Large‐Eddy Simulations [LES]). This paper focuses on the calibration of cloud geometry parameters (vertical overlap, horizontal heterogeneity, and cloud size) that appear in the parameterization of radiation. The solar component of a radiative transfer (RT) scheme that includes a parameterization for 3D radiative effects of clouds (SPARTACUS) is run in offline single‐column mode on an ensemble of input cloud profiles synthesized from LES outputs. The space of cloud geometry parameter values is efficiently explored by sampling a large number of parameter sets (configurations) from which radiative metrics are computed using fast surrogate models that emulate the SPARTACUS solver. The sampled configurations are evaluated by comparing these radiative metrics to reference values provided by a 3D RT Monte Carlo model. The best calibrated configurations yield better predictions of TOA and surface fluxes than the one that uses parameter values computed from the 3D cloud fields: The root‐mean‐square errors averaged over cumulus cloud fields and solar angles are reduced from ∼10 Wm−2 with LES‐derived parameters to ∼5 Wm−2 with adjusted parameters. However, the calibration of cloud geometry fails to reduce the errors on absorption, which remain around 2–4 Wm−2. Plain Language Summary: A way to improve the accuracy of climate models is to improve the physical formulations that represent the effects of small‐scale processes on the evolution of atmospheric state. Processes that involve clouds and radiation are particularly important due to their key role on climate. Choosing values for the parameters inherent to these formulations is a difficult task. This series of papers presents a rigorous strategy for calibrating models. It is based on comparisons between high‐resolution models that accurately represent clouds and single‐column versions of a climate model, on the basis of process‐oriented metrics such as cloud height. A set of acceptable parameters is efficiently found using machine learning techniques. In this third part, the parameters that control the radiative effects of cloud geometry are calibrated. A recent radiation model that includes realistic representation of the radiative effect of cloud heterogeneity, cloud vertical structure and cloud size is evaluated and calibrated using references that are provided by a ray‐tracing algorithm that tracks virtual photons in virtual cloud fields produced by high‐resolution models (Large‐Eddy Simulations [LES]). Calibration improves the model with respect to using parameters diagnosed in the LES. Good agreement is found only when interception of sunlight by cloud sides is represented. Key Points: A novel calibration approach is applied to an offline radiation scheme to disentangle sources of uncertainty in cloud radiative effectsThe SPARTACUS solver is run on cloud profiles derived from Large‐Eddy Simulations (LES) cumulus fields and compared to Monte Carlo 3D radiative transfer computationsAdjusting SPARTACUS cloud geometry parameters provides effective values that improve surface and TOA fluxes compared to LES‐derived values [ABSTRACT FROM AUTHOR]

A viscoelastic lattice spring model is applied to real polymer blend containing strong interaction, i.e., chemical bond, to investigate the structure–property correlation in combination of Monte Carlo method modified by cavity diffusion and bond length fluctuation algorithms. Impacts of inclusion ratio, chemical bonds, chain length, and interface orientation on the mechanical response along with fracture mode are quantified through simulation of uniaxial tensile test. It is confirmed that introduction of strong interaction and prolongation in chain length induces observable reinforcement in global modulus and ultimate tensile strength, respectively, and the amplification is more significant when the interface is parallel to external load. [ABSTRACT FROM AUTHOR]

The integration of information and communication technologies in power networks enables greater flexibility through Smart Grids and increased network adequacy and reliability. Information and communication technology's functionality and design diversity within the cyber‐physical power system should be explicitly defined to quantify the risk of information and communication technology failures. The purpose of this paper is to quantify the cyber‐physical reliability risk introduced by the information and communication technology deployed for dynamic line rating implementations, which are usually installed in adverse environments and have a higher failure risk than indoor information and communication technology installations. This cyber‐physical reliability study employs a modified sequential Monte Carlo approach with a Markov state space to capture the dynamic line rating‐information and communication technology functionality. The method integrates the additional dynamic line rating states within the overhead line operating states, which allows quantifying the risk of dynamic line rating failures against the risk of traditional probabilistic line rating uprating practices. Results from IEEE 24‐bus and 14‐bus network reliability studies indicate that dynamic line rating‐information and communication technology failures (a) increase generation costs in the 24‐bus transmission network with high generation flexibility, while (b) they reduce the reliability of the 14‐bus network with small generation diversity and multiple load points. Such results can provide insightful recommendations on the quality of dynamic line rating‐information and communication technology infrastructure and inform utility's investment planning processes and maintenance practices. [ABSTRACT FROM AUTHOR]

Joint detection and tracking of multiple extended targets (ETs) from image observations is a challenging radar technology; especially for extended stealth targets (ESTs). This work provides a new approach for the ESTs tracking under the non‐linear Gaussian system based on track‐before‐detect (TBD) approach. The sequential Monte Carlo cardinality‐balanced multi‐target multi‐Bernoulli (SMC‐CBMeMBer) filter provides a good framework to cope with TBD approach. However, this filter suffers from the particles' degradation problem seriously; especially for ETs tracking. Recently, the cubature Kalman (CK)‐CBMeMBer filter which employs a third‐degree spherical‐radical cubature rule has been proposed to handle the non‐linear models, the CK‐CBMeMBer filter is more accurate and more principled in mathematical terms compared to SMC‐CBMeMBer filter. To this point, the authors address a TBD of ESTs with extended CK‐CBMeMBer filter based on random matrix model (RMM), which is an efficient way to track ellipsoidal ESTs. In RMM‐ESTs scenarios, although the extension ellipsoid is efficient, it may not be accurate enough because of lacking useful information, such as size, shape, and orientation. Therefore, they introduce a filter composed of sub‐ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed filter. [ABSTRACT FROM AUTHOR]

Islanded microgrids (IMGs) are embedded power networks with distributed energy resources (DERs) providing a reliable and flexible energy option for off‐grid customers. This work addresses the planning model of renewable‐based IMGs feeding multi‐energy demands considering investment and emission related objectives. The proposed solution is to determine the optimal mix and sizing of various energy sources in IMG, including renewables; for multiple energy demands. This study also presents a hybrid‐scenario and Monte Carlo approach to gauge the uncertainty involved in multi‐energy demands, i.e. electrical, heating, and cooling loads; together with correlation among wind and solar generations. The spatial interdependence among renewable generations is implemented using copula; that generates a synthetic set of stochastic correlated data. The combined load scenarios for multi‐energy demands and renewable samples are implemented with the proposed hybrid approach in the formulated stochastic planning model. In this work, the formulated problem is proposed to solve using meta‐heuristic multi‐objective ant lion optimiser algorithm, that is validated on the test system. The superiority of the proposed approach is highlighted in comparison with other multi‐objective optimisers. The multi‐energy dispatch between associated sources and loads were simulated to show how the obtained capacity can suffice the seasonal multi‐energy demands of a typical day considered. [ABSTRACT FROM AUTHOR]

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results. [ABSTRACT FROM AUTHOR]

To coordinate the increasing application of electric vehicles (EVs), the deployment of charging stations becomes vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve the balanced reliability in both the electricity network and EV trip, an optimal planning method for charging stations in the electricity‐transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the quasi‐sequential Monte Carlo simulation is presented. This study proposes a novel reliability‐oriented multi‐objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large‐scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The access of distributed generations (DGs) in the traction power supply system (TPSS) can not only make use of the renewable energy near the railway route, but also improve the condition of weak external power supply. In order to quantify the effect when DGs access TPSS, this study proposes a method for evaluating the power supply capability and quality of TPSS which considers the access of DGs. First, the method fully considers the randomness and uncertainty of the power provided by DGs and that consumed by electrical locomotives. A comprehensive model of TPSS with the access of DGs is built by means of the stochastic model, and then the index system for evaluating the power supply capability and quality of TPSS is formulated. Finally, the Monte Carlo simulation is applied to calculate the probabilistic load flow to obtain the evaluation indices. The proposed method is verified by simulating a typical TPSS in a remote mountain area. The results show that the method can quantify the power supply capability and quality of the system after the access of DGs. It can provide an intuitive conception of the power supply condition for design and operation personnel. [ABSTRACT FROM AUTHOR]

This study investigates the impact of co‐channel interference caused by external terminals on multi‐user bi‐directional amplify‐and‐forward based wireless relaying network. The investigation considers multi‐user pair and a single bi‐directional relay terminal and a finite number of co‐channel interference in the system model. A lower bound on the end‐to‐end outage probability is derived for the simultaneous transmission and the opportunistic source‐pair selection strategy. According to analytical, asymptotic, and Monte‐Carlo based computer simulation results, the opportunistic source‐pair selection strategy achieves a near‐optimal performance for the inter‐cell interference caused by other user‐pairs within the system. Results also show that the opportunistic source‐pair selection strategy also achieves optimal diversity order, which is equal to N, in high signal‐to‐noise ratio regimes. Results also show that the co‐channel interference degrades the achievable diversity order from N to 0 while also affecting the system coding gain. [ABSTRACT FROM AUTHOR]

The achievable secrecy rate regions of various multi‐user channels with Gaussian inputs are well‐known in the literature. To gain more practical insights into the achievable rates, it is more useful to consider channels with inputs from finite constellations, such as M‐ary quadrature amplitude modulation (M‐QAM), M‐ary phaseshift keying (MPSK), M‐ary amplitude phase‐shift keying (M‐APSK) etc. The authors study the achievable secrecy rates with a constrained constellation input for a multiple access wiretap channel with an eavesdropper. They also show that if the constellation points are rotated relatively for the two users then the secrecy sum‐rate can be improved. They perform Monte–Carlo simulations for computing these secrecy rates for classical modulation schemes including BPSK, quadrature phase‐shift keying, M‐QAM, M‐PSK, M‐PAM, and M‐APSK. They also derived an approximate function, whose argument of supremum provides an approximately optimal rotation angle for obtaining a maximum secrecy sum‐rate. they also show, via simulations, that rotation of constellation is helpful for some range of signal‐to‐noise ratio only, which is contrary to the results of multiple access channel without security constraint. Finally, they consider a more realistic scenario, where the channel from transmitters to receivers and eavesdroppers is time‐varying. They consider the case of complex circularly symmetric Gaussian random channel gains and compute the optimal rotation angle, which will maximise the expected value of an upper bound of the secrecy sum‐rate. [ABSTRACT FROM AUTHOR]

This study is concerned with reinforcement learning enhanced by two‐time scale approximations. Many systems arising in applications are large and complex. To treat these problems, it is often beneficial, and sometimes necessary, to reduce the dimensionality and aggregate states that are 'close' to each other. In this study, the authors propose a two‐time scale reinforcement learning method for such an aggregation process. In particular, they present how to classify states that are 'close' and demonstrate the effectiveness of the authors' state aggregation based two‐time scale methods. Thus the problem can be considered as using learning for identifying the system. A production planning problem with failure‐prone machines is used throughout this study to illustrate the main ideas, key steps and results. Monte Carlo simulations are used to generate the random environment. [ABSTRACT FROM AUTHOR]

In this study, the authors present robust tuning rules for an event‐based control architecture for the automatic regulation of the depth of hypnosis in anaesthesia. The authors' control system uses propofol and remifentanil coadministration as control variables and the bispectral index as controlled variable. The control system is based on a PIDPlus controller combined with an event generator that detects significant variations of the BIS signal, thus providing strong filtering of the noise. A fixed ratio between the drug infusions allows the anaesthesiologist to explicitly regulate the opioid–hypnotic balance of the anaesthesia. The tuning rules are developed by solving a min–max optimisation problem that optimises the worst‐case scenario over a given data set of patient models. A gain scheduling strategy yields optimal performance in both the induction and the maintenance phases of anaesthesia. Finally, through the Monte Carlo method, they validate the effectiveness of the proposed approach on a general population, and the robustness to the intra‐ and inter‐patient variability for different infusion balances. [ABSTRACT FROM AUTHOR]

Transport electrification is becoming the mainstream as a means to improve efficiency, performance, and sustainability of transportation systems. Electrical vehicles (EVs) can help to de‐carbonise the environment, but a downside is the technical issues presented to the low‐voltage distribution network. To quantify the stochastic nature of transport‐affected electrification, probabilistic load flow is employed. Monte Carlo‐based simulation is applied to accommodate the probabilistic uncertainties associated with variable EV charging patterns. This study considers high‐power charging (up to 11 kW) at the domestic level while monitoring power quality variations (voltage drop, voltage unbalance factor, voltage sag) standards. This work focuses on the Irish and UK, distribution system operator's–transmission system operator's perspectives, as it will help to identify the likely impacts due to high‐EV charger proliferation at household locations. The results indicate that if a 3.68 kW charger is used at the domestic level, it is possible for 40% of total household consumers to connect EVs directly to the distribution network without any power quality breaches. Furthermore, the proliferation of EV can be increased up to 100% if constrained to the start, and middle portions of the network (relative to the feeder substation transformer). For higher charger capacities (up to 11 kW), a bottleneck is presented regarding a resultant voltage unbalance factor. [ABSTRACT FROM AUTHOR]

With the increase penetration of new energy and electric vehicles (EVs), the nodal voltage violations (NVVs) problem is widely concerned. Voltage predictive regulation methods can prevent NVVs and improve the performance of traditional voltage control. A fast and reliable forecasting method for NVVs is a key requirement to support this application. However, it is time‐consuming to forecast NVVs using a Monte Carlo simulation‐based probabilistic load flow method and the traditional deterministic method based on load forecasting has low reliability due to it not considering the uncertainty of load forecasting errors. A novel fast forecasting method for NVVs with shorter computational time and higher reliability is proposed. In the proposed method, the uncertainty of the load forecasting is taken into account and the impact of active/reactive power change on NVVs can be quantified like the traditional deterministic method. Thus, this method can assist in correcting voltage regulation plans so that voltage constraints can be satisfied as much as possible. Finally, the IEEE 69‐bus test system and real historical load series are used to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, a privacy‐based demand response (DR) trading scheme among end‐users and DR aggregators (DRAs) is proposed within the retail market framework and by distribution platform optimiser. This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs' and customers' preferences. A bi‐level programming model is formulated in a day‐ahead market within retail markets. In the upper‐level problem, the total operation cost of the distribution system is minimised. The production volatility of renewable energy resources is also taken into account in this level through stochastic two‐stage programming and Monte–Carlo simulation method. In the lower‐level problem, the electricity bill for customers is minimised for customers. The income from DR selling is maximised based on DR prices through secure communication of household energy management systems and DRA. To solve this convex and continuous bi‐level problem, it is converted to an equivalent single‐level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper‐level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network. [ABSTRACT FROM AUTHOR]

This study investigates the performance of a decode‐and‐forward‐relay‐based hybrid free space optical (FSO)/radio frequency (RF) system with selection combining. The proposed hybrid system considers an FSO sub‐system as a default transmission option and RF sub‐system is utilised when FSO is in outage. For the proposed system, the outage threshold signal‐to‐noise ratio (SNR) is an important parameter and the optimum value for the same has been calculated to achieve the target average symbol error rate (SER) and high capacity. The presented model considers the effect of transmit beam waist, detector size, and jitter variance explicitly for the FSO link. Transmit beam waist and detector size are optimised to minimise the average SER considering various weather conditions and link distances. A unified performance analysis is addressed and exact expressions for outage probability and average SER are obtained. Simple asymptotic expressions are also derived for high SNR to obtain the diversity order. Large coding gains are observed for optimum beam waist compared to any arbitrary beam waist for same average SER. Significant performance improvement is also observed for the proposed system compared to the individual FSO and RF systems. The theoretical and asymptotic analyses are verified by Monte‐Carlo simulations. [ABSTRACT FROM AUTHOR]

During the power system restoration there is a high possibility that the load demand is greater than the normal system operation for thermostatic loads. This demand increment, called Cold Load Pick-up (CLPU) has been a critical concern to utilities and will slow down the restoration process. Thus, it is imperative to predict the CLPU demand to perform restoration process more precisely. The use of new loads such as electric vehicles (EVs) has made new problems. In this paper, first the negative impact of EVs after an outage, on CLPU demand is investigated and then it is shown that the behavior of EVs during restoration service is the same as CLPU phenomenon. In doing so, the proposed approach uses Monte Carlo simulation method to predict EVs recharging demand. Since there will be some other new loads in the future that may have CLPU performance, a new approach is proposed to predict the CLPU demand of any load, without the need to have its exact model. ANFIS method is used to estimate CLPU demand based on previous outage cases in the feeder. The proposed approach is implemented in MATLAB® and simulation results confirm its ability in load prediction for restoration service. [ABSTRACT FROM AUTHOR]

In order to help flexible multi‐state switch (FMSS) access the power system, a reliability evaluation method for multi‐terminal interconnected power distribution systems with FMSS is proposed. First, according to the physical structure and operation mode of FMSS, a reliability model of FMSS is established. Then, by exploring how FMSS access strategy changes the system operation mode as well as network structure, a load recovery method for disconnected side is designed and the factors affecting the system reliability are refined. Finally, the sequential Monte Carlo is applied to evaluate the reliability of the multi‐terminal interconnected distribution system with FMSS, and the influence of FMSS access strategy on the system reliability is quantitatively analysed in two different systems. The cases verify the effectiveness of the method, and the results show that: (i) the access of FMSS and the proposed load recovery strategy can significantly enhance the system reliability, (ii) the access position, capacity and reliability of FMSS affect system reliability, (iii) the improvement of system reliability is optimal when the FMSS with a large capacity connected to the end of the system, and close to important loads. This research can provide theoretical support for the planning of flexible interconnected power distribution system. [ABSTRACT FROM AUTHOR]

A spectral algorithm for processing staggered‐pulse repetition time (SPRT) signals in weather radar is introduced. It includes new approaches for ground clutter filter and hydrometeor spectral moments estimation. The algorithm uses ideas similar to GMAP but applied to non‐uniform sampled signals. This work is focused on staggered sequences 2/3, but can be extended to other staggered sequences. Monte Carlo experiments were used to evaluate the performance of the spectral moments estimators for simulated weather signal, in scenarios with and without the presence of ground clutter. When clutter is present, a study using different clutter‐to‐signal ratios was carried out, showing that the method can deal with a wide range of situations and is appropriate for implementation in real scenarios. A comparison against GMAP‐TD was performed, showing similar estimation results for both algorithms and a fivefold processing speed improvement for the proposed method. The performance was also validated using real weather data RMA‐12 from a radar located in San Carlos de Bariloche, Argentina. The proposed algorithm has an easy implementation and is a good candidate for real‐time implementations. [ABSTRACT FROM AUTHOR]

The study proposes a new hybrid multi-objective evolutionary optimisation algorithm based on decomposition and local dominance for meter placement in distribution system state estimation. The evenly distributed qualitative and diverse solutions on the Pareto front are required for a decision-maker for selecting a final optimal solution. Such a Pareto front can be achieved by obtaining the balance between convergence and diversity of multi-objective optimisation algorithm. Therefore, the proposed method combined dominance and decomposition techniques, modelled meter placement as a constrained combinatorial multi-objective optimisation. The meter placement is designed as a trade-off between three objectives that are minimising the cost of the meters, average relative percentage error (ARPE) of voltage magnitude and ARPE of voltage angle. As the meter placement problem is a combinatorial optimisation, the binomial distribution-based Monte Carlo method is utilised to initialise the population, which aims to improve the diversity, as a consequence it improves the convergence, which is a by-product of this method. The results of the proposed method are compared with multi-objective evolutionary algorithm based on decomposition, non-dominated sorting genetic algorithm-II and with multi-objective hybrid particle swarm optimisation-krill herd algorithm, multi-objective hybrid estimation of distribution algorithm-interior point method and demonstrated on PG&E 69-bus distribution system and Practical Indian 85-bus distribution system. [ABSTRACT FROM AUTHOR]

With the increasing power demand of end-users, demand side management has been an important resource to been applied to the optimal day-ahead scheduling of distribution network. This work deeply studies the mechanism of multi-type demand response projects participating in load management. The authors particularly design the novel compensation mechanisms of interruptible load (IL) and transferable load (TL). The step-wise price-quantity offer package including basic response stage and elastic stages is devised for IL while the two-dimensional alternating function based on transferable time and transferable capacity is set up to quantify the load shifting cost for TL. To model uncertainties of distributed generation output power and response behaviour of end-users, a large number of stochastic scenarios are generated by the Monte Carlo method and Latin Hypercube Sampling and clustered into some typical scenarios by K-means method. Then, the optimal day-ahead stochastic scheduling model of the distribution network is proposed based on the obtained typical scenarios. In addition, three indexes including the contribution degree, confidence degree and strategy confidence degree are put forward to evaluate the rationality of the scheduling decision. Finally, the numerical results on the IEEE 33-bus network verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers. Beta regression is popular when the dependent variable is restricted to the interval [0, 1], such as rates and proportions. This paper suggests Liu estimators that may be used in the presence of multicollinearity. We show that the Liu estimator outperforms the MLE in a simulation study. Finally, using an empirical application on chemical data, we show the benefit of the new approach. [ABSTRACT FROM AUTHOR]

Rich scattering of ultraviolet signals enables the non‐line‐of‐sight outdoor wireless connectivity and eliminates the line‐of‐sight requirement of free‐space optical links. Besides scattering losses, ultraviolet signals also suffer from the atmospheric turbulence‐induced fading. One of the strong fading mitigation techniques is the spatial diversity, which has been extensively studied in the context of wireless radio frequency and infrared networks. In this study, spatial diversity techniques for non‐line‐of‐sight ultraviolet communication links are considered. In particular, multiple‐input‐single‐output (MISO), single‐input‐multiple‐output (SIMO), and multiple‐input‐multiple‐output (MIMO) non‐line‐of‐sight ultraviolet communication systems are derived. The BER performance of the single‐input‐single‐output (SISO) link is also derived as a benchmark. The accuracy of the derivations is confirmed through Monte Carlo simulations. The results demonstrate the significant performance improvements in spatial diversity schemes over SISO counterparts. The authors further investigate the effect of spatial correlation on the performance of non‐line‐of‐sight ultraviolet links with spatial diversity. They demonstrate that the spatial correlation degrades the performance significantly, and efficient separation between apertures is crucial to achieve the maximum diversity gain. [ABSTRACT FROM AUTHOR]

In this study, a dual‐hop cooperative communication system with radio frequency (RF) energy harvesting was investigated in two cases of linear and non‐linear energy harvesting models. In the proposed system, the signal is transmitted directly from a source node to a destination node or sent with support of selected relay nodes. While both the source node and the destination node are powered normally, the relay nodes are powered by harvesting technique. To choose the best relay node, a selection combination method was applied at the destination node in both cases of amplify‐and‐forward and decode‐and‐forward protocols. To evaluate the system performance, the outage probability of the cooperative communications over independent identically distributed Nakagami‐m was derived and analysed with arbitrary m parameter whereas this parameter was fixed to be integral value in previous research studies. Furthermore, the approximate and asymptotic operations are applied to simplify the outage probability expressions. The simulation program was developed based on the Monte Carlo method and MATLAB software with two aims of evaluating the system performance and verifying the theoretical results. The simulation results demonstrate that the analysis and evaluation of the study are considerably accurate. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has rapidly increased due to many technical, environmental and economical benefits. One of the DG application challenges is to find a proper area to incorporate the DG associated to a particular location. In this study, a central photovoltaic distributed generation (PVDG) topology is proposed to distribute the optimal sizes to the optimal locations. Uncertainties of load demand and renewable power generation are also taken into consideration of the optimisation problem. This study determines the deterministic and probabilistic penetration limits based on the distribution network topologies, considering the PVDG significant impact on active power losses reduction and voltage profiles improvement. The effectiveness of the proposed topology was validated on 33‐ and 69‐bus distribution networks adopting Monte Carlo simulation method, Newton‐Raphson load flow method and biogeography based optimisation. From the results, the voltage profiles, active power loss reduction, DG capacity required and penetration limit have shown better performances on the central PVDG topology over the bus dedicated PVDG topology. [ABSTRACT FROM AUTHOR]

This Letter investigates the impacts of hardware impairments (HIs) and channel estimation errors (CEEs) on the secrecy performance of satellite communication networks with one legitimate user and one eavesdropper. Particularly, the authors derive the closed‐form expression for the secrecy outage probability (SOP) of the considered secrecy satellite communication networks. Besides, in order to research the impact of the HIs and CEEs at high signal‐to‐noise ratios, they obtain the asymptotic expression for the SOP, which gives efficient and fast method to evaluate the effect of the considered system parameters on the secrecy system performance. At last, Monte Carlo computer results are given to verify the effects of HIs and CEEs on the secrecy system performance. [ABSTRACT FROM AUTHOR]

This study presents a probabilistic transmission expansion planning model incorporating distributed series reactors, which are aimed at improving network flexibility. Although the whole problem is a mixed‐integer non‐linear programming problem, this study proposes an approximation method to linearise it in the structure of the Benders decomposition (BD) algorithm. In the first stage of the BD algorithm, optimal number of new transmission lines and distributed series reactors are determined. In the second stage, the developed optimal power flow problem, as a linear sub‐problem, is performed for different scenarios of uncertainties and a set of probable contingencies. The Benders cuts are iteratively added to the first stage problem to decrease the optimality gap below a given threshold. The proposed model utilises the Monte Carlo simulation method to take into account uncertainty of wind generations and demands. Several case studies on three test systems are presented to validate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a single loop 3rd order discrete‐time Sigma–Delta Modulator (∑ΔM) using a single operational amplifier (Op‐Amp). The proposed ∑ΔM utilises delay‐based discrete‐time integrators and a single Op‐Amp is used to implement all the integrators of the proposed design. The delay‐based design helps in relaxing the settling time requirement of an integrator, also the error due to slewing in the output of the integrator is avoided. A switched capacitor Op‐Amp based proposed design is simulated in standard 180~nm CMOS technology with a resolution of 10.75 bits. The proposed ∑ΔM achieves a signal‐to‐noise‐distortion ratio (SNDR) of 66.48 dB and dynamic range (DR) of 70 dB. An input signal of −4 dBFS magnitude at a frequency of 2.125 kHz is sampled with a sampling frequency of 1.024 MHz. The proposed circuit is designed for a bandwidth (BW) of 20 kHz with an over‐sampling‐ratio (OSR) of 25.6. The proposed modulator achieves a figure‐of‐merit (FOMS/FOMW) of 156.48 dB/0.290 (pJ/conversion‐step) with the total power consumption of 20 µW at 1.8 V supply voltage (VDD). A Monte‐Carlo simulation for the SNDR of the proposed design is done and the achieved mean and standard deviation (SD) is 66.09 dB and 1.025 respectively for 1000 samples. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaics (RPVs) have an impact on the ageing failure rate and reliability of transformers. The advantages and disadvantages of different penetrations of rooftop photovoltaic on the reliability of the transformers are studied. For the first time, the adverse effects of high penetration of RPVs on the transformer failure and power system reliability are examined. Regarding the growing interest in the widespread use of renewable energies on account of global warming, studying the negative effects of DG units presence is so important to predict the future of power system conditions. Monte Carlo simulation is used for the probabilistic nature of clearness index and reliability evaluation. Well‐being criteria that show the different operation modes of the power system is used to identify the at‐risk, marginal and healthy mode of the power system in the presence of different penetrations of rooftop photovoltaic. The results show that the maximum penetration of rooftop photovoltaic for improving the reliability of the power system is 5 kW per house in the feeder of Isin in Bandar Abbas, in Iran. [ABSTRACT FROM AUTHOR]

Large‐scale renewable energy (RE) integration in the power systems is displacing conventional sources of generation, thus reducing the system's inherent inertia and primary frequency response (PFR) capabilities. This reduced response may decline the system's ability to stabilise frequency after inevitable events such as loss of generation or load and thus requires additional PFR. To meet the PFR requirements, fast‐acting resources like energy storage systems (ESS) and demand response (DR) (transferrable and interruptible loads) need to be integrated within the system. In this regard, a novel stochastic security‐constrained unit commitment framework is proposed to assess the potential of ESS and DR to ensure additional PFR requirements and handle post contingency frequency dynamics. Moreover, the proposed methodology analyses the impact of uncertain RE integration on system frequency dynamics using Bayesian inference statistics. Case studies are carried out on 39 bus New England and 118 bus IEEE test systems, incorporating post fault frequency security parameters. Simulation results analyse the PFR contribution of ESS and DR and indicate a substantial reduction in system operating cost and RE curtailment with their integration. The proposed analysis would help system operators to maintain required PFR under high RE penetration, and handle unavoidable contingencies. [ABSTRACT FROM AUTHOR]

Extending current deterministic tools to incorporate significant stochastic wind power is becoming an important as well as challenging task for present‐day power system decision‐making. This study proposes a novel probabilistic assessment method to assess the available transfer capability (ATC). Usually, repeated ATC evaluations with an exhaustive set of samples are needed to obtain converged results by the Monte Carlo simulation. To alleviate the computation burden, a statistically‐equivalent surrogate model for the ATC solution is constructed based on the canonical low‐rank approximation (LRA). By implementing LRA for the base case and a set of enumerated contingencies, the uncertainties of wind power generation and load, as well as transmission equipment outages, are addressed efficiently. With the proposed method, the probabilistic ATC is characterised, and the most influential uncertain factors are identified, which helps to determine a suitable ATC level. The effectiveness of the proposed method is validated via case studies with a modified IEEE 118‐bus system. [ABSTRACT FROM AUTHOR]