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Growing widespread outages in power systems caused by natural disasters have highlighted the necessity of applying defensive approaches with the goal of prompt service restoration. In this context, this paper proposes a stochastic model with the goal of optimally using proactive operational actions before the upcoming disturbance hits. The actions considered in this study include network reconfiguration and crew prepositioning. To take the effective actions, the model simulates probable damages caused by the events via a set of scenarios generated by Monte Carlo simulation method. The proposed model aims at minimizing the expected load curtailment caused by the event over the scenarios. To... 

Growing widespread outages in power systems caused by natural disasters have highlighted the necessity of applying defensive approaches with the goal of prompt service restoration. In this context, this paper proposes a stochastic model with the goal of optimally using proactive operational actions before the upcoming disturbance hits. The actions considered in this study include network reconfiguration and crew prepositioning. To take the effective actions, the model simulates probable damages caused by the events via a set of scenarios generated by Monte Carlo simulation method. The proposed model aims at minimizing the expected load curtailment caused by the event over the scenarios. To... 

In the near future power systems, efficient management of uncertainties with considering the system constraints without any simplification will be a challenge for system operators. Considering AC constraints leads to providing more accurate schedule of generating units, which can have a significant impact on the reduction of operating costs. Although numerous studies have been done to convexify AC optimal power flow constraints, most of the models are non-linear, which can be intractable for large-scale systems. In this study, a novel linear robust AC model is introduced using a combination of the quadratic convex relaxation (QCR) and the Frank-Wolfe algorithm for linearising the AC... 

A three-dimensional model has been developed to describe the multiphase release of macromolecular drugs encapsulated in a hydrogel. The heterogeneity of network mesh size was considered by assigning varying diffusion coefficients to the network lattices randomly. Using a stochastic approach, the random nature of diffusion of drug molecules was captured within the network. The simplest form of distribution containing two diffusion coefficients was tested. To generate the drug release profiles for experimental validation under the limitation of computational cost, a simple scaling relationship was employed. Unlike the single-diffusivity model, the dual-diffusivity model showed good agreement... 

Using the original form of Capacitance-Resistance Model (CRM), as a waterflooding performance prediction tool, for modeling real reservoirs makes some unavoidable errors. Combination of this model with available data assimilation methods yields more powerful simulation tool with updating parameters over time. However, the inherent uncertainty arisen by modeling complex reservoirs with only a limited number of CRM parameters is not addressed yet. In this study, the model error behavior has been simulated through a physically-based dynamical system in which it has been correlated with the original model parameters. The ensemble-based Kalman filter (EnKF) data assimilation method has been... 

All realistic Multi-Criteria Decision Making (MCDM) problems face various kinds of uncertainty. Since the evaluations of alternatives with respect to the criteria are uncertain they will be assumed to have stochastic nature. To obtain the uncertain optimism degree of the decision maker fuzzy linguistic quantifiers will be used. Then a new approach for fuzzy-stochastic modeling of MCDM problems will be introduced by merging the stochastic and fuzzy approaches into the OWA operator. The results of the new approach, entitled FSOWA, give the expected value and the variance of the combined goodness measure for each alternative. Robust decision depends on the combined goodness measures of... 

In the last two decades, a number of architecture-based software development notations, methods, techniques, and tools were formulated. Of particular interest to the software architecture researchers and practitioners were the notations and languages for modeling software architectures, known as ADLs. Object Stochastic Activity Network (OSAN) is a new model which inherits useful features from Petri nets, stochastic activity networks and queue networks, which made it a powerful and flexible tool to model concurrent systems and evaluate and verify them. This paper introduces OSAN as a new analyzable architecture description language, which can be used both for modeling architectural aspects of... 

In order to design a decision-making framework in production environments, in this study, we use both the stochastic dynamic programming and Bayesian inference concepts. Using the posterior probability of the production process to be in state λ (the hazard rate of defective products), first we formulate the problem into a stochastic dynamic programming model. Next, we derive some properties for the optimal value of the objective function. Then, we propose a solution algorithm. At the end, the applications and the performances of the proposed methodology are demonstrated by two numerical examples. © 2007 Asian Network for Scientific Information  

As multi-hop wireless networks are attracting more attention, the need to evaluate their performance becomes essential. In order to evaluate the performance metrics of multi-hop wireless networks, including sending and receiving rates of a node as well as the collision probability, a model based on Stochastic Reward Nets (SRNs) is proposed. The proposed SRN models a typical node in such networks, considered as a general template to be applied to any wireless node. The SRN model of a single node is designed to take transmission effects of all neighboring nodes into account, while ignoring the ones whose transmission has no effect on the node under-study. Applying the proposed SRN to each node... 

Due to the wide applications of three-dimensional graphene (3DG) foam in bio-sensors, stretchable electronics, and conductive polymer composites, predicting its mechanical behavior is of paramount importance. In this paper, a novel multiscale finite element model is proposed to predict the compressive modulus of 3DG foams with various densities. It considers the effects of pore size and structure and the thickness of graphene walls on 3DG foams' overall behavior. According to the scanning electron microscope images, a unit cell is selected in the microscale step to represent the incidental arrangement of graphene sheets in 3DG foams. After derivation of equivalent elastic constants of the... 

This paper presents a two-stage stochastic planning model for transmission systems and distributed energy resources (DERs) considering power system resiliency. The model takes into account both normal and emergency conditions as well as the duration of each condition. The proposed model is formulated as a mixed-integer linear program to minimize the total investment cost of transmission lines and DERs, expected values of generators' operation cost in normal and emergency situations, and load shedding cost in emergency condition. The emergency scenarios are considered as moderate, severe, and complete damage states, which have different recovery times for transmission assets. The Benders... 

Making proper decisions in today's complex world is a challenging task for decision makers. A promising approach that can support decision makers to have a better understanding of complex systems is agent-based modeling (ABM). ABM has been developing during the last few decades as a methodology with many different applications and has enabled a better description of the dynamics of complex systems. However, the prescriptive facet of these applications is rarely portrayed. Adding a prescriptive decision-making (DM) aspect to ABM can support the decision makers in making better or, in some cases, optimized decisions for the complex problems as well as explaining the investigated phenomena. In... 

Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time... 

Over the past decades, there has been a dramatic increase in the frequency of natural disasters, which are the leading causes of large-scale power outages. This paper, therefore, assesses the significance and role of optimal tie-line construction in improving the service restoration performance of unbalanced power distribution systems in the aftermath of high-impact low-probability incidents. In doing so, a restoration process aware stochastic mixed-integer linear programming model is developed to find the optimal locations for new tie-line construction in unbalanced three-phase distribution systems. In particular, the restoration process of distribution systems, including the fault... 

This paper introduces a day-ahead clearing model for the simultaneous energy and ancillary services market in high penetration of renewable energy sources (RESs) considering fast-start generators’ behaviors as non-spinning reserve providers. For managing the uncertainties of RESs, the system operator takes the advantages of the stochastic model of security-constrained unit commitment (SCUC) problem considering plausible scenarios. The non-convexities due to the startup costs, minimum power outputs, and commitment variables make the stochastic SCUC problem and market-clearing non-convex. Meanwhile, with traditional market-clearing methods for energy and ancillary services based on marginal... 

Over the past decades, there has been a dramatic increase in the frequency of natural disasters, which are the leading causes of large-scale power outages. This paper, therefore, assesses the significance and role of optimal tie-line construction in improving the service restoration performance of unbalanced power distribution systems in the aftermath of high-impact low-probability incidents. In doing so, a restoration process aware stochastic mixed-integer linear programming model is developed to find the optimal locations for new tie-line construction in unbalanced three-phase distribution systems. In particular, the restoration process of distribution systems, including the fault... 

Wind power producers have recently gained a significant penetration in the electric power network, which might cause some challenges for the system operation due to the uncertainties in the generated power. In this way, wind power owners may offer different services, such as providing reserve capacity besides their conventional role of delivering just energy. Currently, conventional generators are the main responsible for providing some reserve in case of uncertainty in renewable generation and demand changes. Therefore, this article introduces a novel approach that allows wind power producers to participate in both energy and reserve markets alongside conventional generators. A two-stage... 

A deterministic energy supply model with bottom-up structure has limited capability in handling the uncertainties. To enhance the applicability of such a model in an uncertain environment two main issues have been investigated in the present paper. First, a binomial lattice is generated based on the stochastic nature of the source of uncertainty. Second, an energy system model (ESM) has been reformulated as a multistage stochastic problem. The result of the application of the modified energy model encompasses all uncertain outcomes together and enables optimal timing of capacity expansion. The performance of the model has been demonstrated with the help of a case study. The case study has... 

Persian Gulf is a semi-enclosed highly saline reverse estuary that is exposed to the risk of oil spills in offshore oil and gas activities. In the early 2000s, a specific version of NOAA's Trajectory Analysis Planner (TAP II) was developed for Persian Gulf to assist regional organizations in preparing oil spill contingency plans. In this research, a new stochastic model is developed to cover the limitations of TAP II. The new model is based on an advanced trajectory model, which is now linked with high resolution spatiotemporal data of the wind and sea current. In a case study, the developed model is compared with TAP II, and evaluated by multiple tests designed for analysis of uncertainty,... 

Oil spills are serious environmental issues that potentially can cause adverse effects on marine ecosystems. In some marine areas, like the Baltic Sea, there is a large number of wrecks from the first half of the 20th century, and recent monitoring and field work have revealed release of oil from some of these wrecks. The risk posed by a wreck is governed by its condition, hazardous substances contained in the wreck and the state of the surrounding environment. Therefore, there is a need for a common standard method for estimating the risks associated with different wrecks. In this work a state-of-the-art model is presented for spatial and stochastic risk assessment of oil spills from...