Sharif Digital Repository

This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The Progressive Hedging Algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases.... 

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as... 

This paper proposes a stochastic mixed-integer programming (SMIP) model for the reliability-constrained unit commitment (RCUC) problem. The major objective of the paper is to examine both features of accuracy and efficiency of the proposed SMIP model of RCUC. The spinning reserve of generating units is considered as the only available reserve provision resource; however, the proposed formulation can be readily extended to comprise the other kind of reserve facilities. Expected load not served (ELNS) and loss of load probability (LOLP) are accommodated as the reliability constraints. Binding either or both reliability indices ensures the security of operation incorporating the stochastic... 

Make-to-order firms use different strategies, such as dynamic pricing and due date management, to influence their performance. In these strategies, orders are segmented into classes based on their sensitivity to lead time and price. Quoting different prices and lead times to different classes of customer can increase a firm's profit and its capacity utilization. Most research in this area does not consider the effects of production constraints on price and lead time decisions. In this paper, we consider the role of flexibility in dynamically choosing the price, lead time and segmentation of customers in make-to-order environments with limited production capacity and multi-period horizon... 

Incorporating the finite-element method for the modeling of the SQUID NDE response to a predefined defect pattern, an adaptive algorithm has been developed for the reconstruction of unknown defects using an optimization algorithm for updating of the forward problem. The defect reconstruction algorithm starts with an initial estimation for the defect pattern. Then the forward problem is solved and the obtained field pattern is compared with the measured signal from the SQUID NDE system. The result is used by an optimization algorithm to update the defect structure to be incorporated in the FEM forward problem for the next iteration. Since the mentioned model based inverse algorithm normally... 

We present an Autoregressive (AR) modeling method for video signal analysis based on 2D Wavelet Statistics. The video signal is assumed to be a combination of spatial feature time series that are temporally approximated by the AR model. The AR model yields a linear approximation to the temporal evolution of a stationary stochastic process. Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial features. Wavelet transform efficiently resembles the Human Visual System (HVS) characteristics and captures more suitable features, as compared to color histogram features. The AR model describes each spatial feature vector as a linear... 

Route choice modeling is one of the most important parts of traffic assignment problem. Recently, this model is used to describe the reactions of drivers to Traveler Information Systems in order to develop accurate Advanced Traffic Management and Information System (ATMIS). Therefore accurate model is necessary. In this paper we proposed a new model based on Type-II fuzzy logic to model route choice problem. This model can take account of the imprecision, uncertainties and vagueness lying in the dynamic choice process and makes more accurate modeling of drivers' behavior than deterministic, stochastic and Type-I fuzzy models. In our proposed model we consider average speed and cost... 

Considerable developments in the real-time telemetry of demand-side systems allow independent system operators (ISOs) to use reserves provided by demand response (DR) in ancillary service markets. Currently, many ISOs have designed programs to utilize the reserve provided by DR in electricity markets. This paper presents a stochastic model to schedule reserves provided by DR in the wholesale electricity markets. Demand-side reserve is supplied by demand response providers (DRPs), which have the responsibility of aggregating and managing customer responses. A mixed-integer representation of reserve provided by DRPs and its associated cost function are used in the proposed stochastic model.... 

In poorly developed fractured rocks, the contribution of individual fracture on rock conductivity should be considered. However, due to the lack of data, a deterministic approach cannot be used. The conventional way to model discrete fractures is to use a Poisson process, with prescribed distribution, for fracture size and orientation. Recently, a stochastic approach, based on the idea that the elastic energy due to fractures follows a Boltzmann distribution, has been used to generate realizations of correlated fractures in two dimensions. The elastic energy function has been derived by applying the appropriate physical laws in an elastic medium. The resulting energy function has been used... 

The quantification of carbon dioxide (CO2) dissolution in oil is crucial in predicting the potential and long-term behavior of CO2 in reservoir during secondary and tertiary oil recovery. Accurate predicting carbon dioxide molecular diffusion coefficient is a key parameter during carbon dioxide injection into oil reservoirs. In this study a new model based on adaptive neuro-fuzzy inference systems (ANFIS) is designed and developed for accurate prediction of carbon dioxide diffusivity in oils at elevated temperature and pressures. Particle Swarm Optimization (PSO) as population based stochastic search algorithms was applied to obtain the optimal ANFIS model parameters. Furthermore, a simple... 

An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that... 

An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that... 

This paper develops an economic production quantity model for a multi-product multi-objective inventory control problem with fuzzy-stochastic demand and backorders. In this model, the annual demand is represented by trapezoidal fuzzy random numbers. The centroid defuzzification and the expected value methods are applied to defuzzify and make decisions in a random environment. In the case where the warehouse space is limited, the Lagrangian relaxation procedure is first employed to determine the optimal order and the maximum backorder quantities of the products such that the total inventory cost is minimized. The optimal solution obtained by the proposed approach is compared with that... 

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment.... 

Electric spring (ES) as a breakthrough in power electronics has led to a revolution in demand-side management. This paper presents flexible power management (FPM) of a networked microgrid (MG) in the presence of renewable energy sources (RESs) and flexibility sources (FSs). The FSs include the novel topology of the integrated unit of ES with electric vehicles (EVs) parking (IUEE) and incentive-based demand response program (DRP). The proposed FPM model is formulated as an optimization problem that minimizes the difference between the expected energy cost and the expected profit of FSs' flexibility subject to the AC optimal power flow (AC-OPF), RESs, FSs, and MG flexibility constraints. In... 

The paper presents the experimental and analytical model analysis of a steel-girder arch bridge. The field test is carried out by ambient vibration testing under traffic excitations. Both the peak picking method in the frequency domain and the stochastic subspace identification method in the time domain are used for the output-only model identification. A good agreement in identified frequencies has been found between the two methods. It is further demonstrated that the stochastic subspace method provides better mode shapes. The three-dimensional finite element models are constructed and an analytical model analysis is then performed to generate natural frequencies and mode shapes in the... 

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms' behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for...