جستجوی مقالات مرتبط با کلیدواژه "uncertainty modeling" در نشریات گروه "فنی و مهندسی"

Wind power has been considered a future alternative to fossil energy resources. However, due to its stochastic nature, the integration of wind power plants (WPPs) into power systems poses some reliability problems such as a mismatch between load profile and efficient wind power generation. This issue can be alleviated by considering the correlation between hourly load and wind speed variations in the planning phase. To this end, a reliability-based wind power planning procedure is proposed and formulated as a stochastic programming problem. The objective function is the minimization of total costs, including capital investment, operating and maintenance, and customer energy not served costs. A new hybrid method that combines features of the load-duration curve and the K-means clustering algorithm is proposed to model the uncertainty of the input data. A shuffled frog-leaping algorithm is used to solve the proposed model. The simulation results indicate that the amount of adaptation between hours with high loads and those with high wind speeds markedly affects the selection of wind sites as optimal locations for WPP installation. Considering this issue can also improve power system reliability in the presence of WPPs.

The considerable development of the electricity market subjects in recent years has provided a complex and more competitive environment for the participants. Each participant in this environment adopts a special strategy to maximize its profit or minimize its energy costs considering the significant constraints. In this paper, a short term optimal scheduling of thermal units, hydropower units, wind turbines, and pumped storage units has been proposed based on the energy market guidelines. The main objective of this research is to minimize the thermal energy production costs considering the uncertainty parameters along with the maximum utilization of clean energy production in the system. In order to evaluate the research goals, IEEE 5-bus standard test system is selected as the case study, which is equipped with both conventional and clean energy resources. In addition, probabilistic behaviors related to energy demand and wind production have been considered. Results proved the effectiveness of this model in minimizing the energy cost of thermal units.

The traditional approaches of modeling and estimation of highly skewed deposits have led to incorrect evaluations, creating challenges and risks in resource management. The low concentration of the rare earth element (REE) deposits, on one hand, and their strategic importance, on the other, enhances the necessity of multivariate modeling of these deposits. The wide variations of the grades and their relation with different rock units increase the complexities of the modeling of REEs. In this work, the Gazestan Magnetite-Apatite deposit was investigated and modeled using the statistical and geostatistical methods. Light and heavy REEs in apatite minerals are concentrated in the form of fine monazite inclusions. Using 908 assayed samples, 64 elements including light and heavy REEs from drill cores were analyzed. By performing the necessary pre-processing and stepwise factor analysis, and taking into account the threshold of 0.6 in six stages, a mineralization factor including phosphorus with the highest correlation was obtained. Then using a concentration-number fractal analysis on the mineralization factor, REEs were investigated in various rock units such as magnetite-apatite units. Next, using the sequential Gaussian simulation, the distribution of light, heavy, and total REEs and the mineralization factor in various realizations were obtained. Finally, based on the realizations, the analysis of uncertainty in the deposit was performed. All multivariate studies confirm the spatial structure analysis, simulation and analysis of rock units, and relationship of phosphorus with mineralization.

Closed loop supply chain design is to provide an optimal platform for efficient and effective supply chain management. It is an essential and strategic operation management problem in supply chain management, and usually includes multiple and conflicting objectives. A new mixed integer non-linear programming model for a multi-objective closed loop supply chain network design problem in the paper industry is developed under uncertainty. The objective functions are to minimize the total cost, maximize the total volume flexibility and minimize the total number of vehicles hired in order to fulfill the paper industry’s policies towards a cleaner and green environment. Also, a novel hybrid solution is presented based on stochastic programming, robust optimization and fuzzy goal programming. A numerical example utilizing the real data from the paper industry in East Azerbaijan of Iran is designed and the model performance is assessed. Furthermore, a recently developed Dragonfly Algorithm (DA) employed to solve the given problem in large scales and compared with Genetic Algorithm (GA). The results indicated that the DA achieved better performance compared with the GA.

This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling of BESSs so that the total daily profit of distribution company (Disco) can be maximized. In this study, the power generation of REGSs such as photovoltaic resources (PVs) and the network electricity prices are studied through their uncertainty natures. The probability distribution function (PDF), is used to account for uncertainties in this paper. Also, the Monte Carlo simulation (MCS) is applied to generate different scenarios of network electricity prices and solar irradiation of PVs. Optimal scheduling of BESSs can be performed by genetic algorithm (GA). In this paper, firstly, the charging and discharging state of BESSs (positive or negative sign of battery power) is determined according to the variable amount of the electricity prices and power produced from PVs, which have been obtained from the Monte Carlo simulation. Then by using the GA, optimal amount of BESSs is determined. Therefore, a hybrid MCS-GA is used to solve this problem. Numerical examples are presented to illustrate the optimal charging/discharging power of the battery for maximizing the total daily profit.

As a matter of course, power market uncertainties escalation is by product of power industry restructure on one hand and the unrivalled penetration of renewable energies on the other. Generally, the decision making process in such an uncertain environment faces with different risks. In addition, the performance of real power markets is very close to oligopoly markets, in which, some market players exercise market power to influence the power market and this matter brings some risks to other players. Hence, each market player must consider these market features to choose his best decision. So, in case of such an uncertain environment, GENCO's bidding strategy would be a complicated and error-prone process. This paper aims to ease this issue suggesting the use of probabilistic bidding strategy of generation units by the unscented transformation (UT) method. The proposed method can consider the correlation between variables, where it models the coalition between market participants. Using the proposed methodology, a market participant can choose a desired range of profit; then, set his decision to manage his profit by reducing his risks. Finally, the proposed methodology is examined through some case studies done in a standard test system. Simulation results show that executing market power by some market players disturbs the competition in the market.

Truncated Gaussian Simulation (TGS) is a well-known method to generate realizations of the ore domains located in a spatial sequence. In geostatistical framework geological domains are normally utilized for stationary assumption. The ability to measure the uncertainty in the exact locations of the boundaries among different geological units is a common challenge for practitioners. As a simple and informative example of such a boundary, one can consider the boundary between ore and waste materials in an ore deposit. This boundary addresses the percentages of the ore and the waste, and also affect the future economy of mine and all precedent mine designs and mine plans. Deterministic approaches, based on interpretation of geological phenomenon, provide just one scenario of ore-waste variation, and do not offer a model for uncertainty of boundaries. On the other hand, geostatistical simulations, based on stochastic models, can measure the uncertainty of such a boundary. Through different techniques for spatial simulation of the categorical data (geological domains) truncated gaussian simulation has been proved to be versatile when geological units have sequential geometries and/or there are few number of indicators (ore and waste). This study addresses the application of TGS for conditional simulation of ore and waste domains in Golgohar iron ore deposit. Separation of the ore and waste domains has affected the ore tonnage estimation and resource evaluation. Various simulations can be considered as the spatial realizations of ore and waste. TGS can generate realizations of the domains and measure the uncertainty of ore-waste boundary. The accuracy of result has been checked through performance evaluation section and different scenarios (e.g. best, average and worst). The best scenario is the one with the most accuracy that is calculated from confusion matrix. The scenario No. 44 with 96 million cubic meters tonnage has an accuracy over 86 percent that is proposed as the best scenario for future mine design and planning.

The earned Value Management (EVM) is a project management technique used to measure project progress by integrating management efficiently of the three most important elements in a project; cost, schedule and scope. This paper presents an evidential reasoning (ER) based model for estimating the Earned Value (EV) of the projects activities with uncertainties in progress data. Since that subjective nature of EV measurement can incorporate into errors and uncertainties which cause biased judgments; and as the uncertainty is inherent in real-life activities, the developed ER based model is very useful to evaluate the EV of a project where uncertainty arises. A case study is provided to illustrate how the new model will be used and can be implemented in reality.

A common problem arising in project management is the fact that the baseline schedule is often disrupted during the project execution because of uncertain parameters. As a result, project managers are often unable to meet the deadline time of the milestones. Robust project scheduling is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during project execution. In order to apply robust scheduling principles to real projects, one should make assumptions close to the actual conditions of the project as much as possible. In this paper, in terms of uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust scheduling methods under different conditions. Finally, we formulate recommendations regarding the best method of robust scheduling based on the results of these experiments.

In many practical distribution networks, managers face significant uncertainties in demand, local price of building facilities, transportation cost, and macro and microeconomic parameters. This paper addresses design of distribution networks in a supply chain system which optimizes the performance of distribution networks subject to required service level. This service level, which is considered for each arbitrary request arriving at a distribution center (facility), has a (pre-specified) small probability of being lost. In this mathematical model, customer’s demand is stochastic that follows uniform distribution. In this model, inter-depot transportation (transportation between distributions centers (DCs)), capacities of facilities, and coverage radius restrictions are considered. For this restriction, each DC cannot service all customers. The aim of this model is to select and optimize location of plants and DCs. Also, the best flow of products between DCs and from plants to DCs and from DCs to customers will be determined. The paper presents a mixed integer programming model and proposed an exact solution procedure in regard to Benders’ decomposition method.

We consider a generalization of the classical quadratic assignment problem, where coordinates of locations are uncertain and only upper and lower bounds are known for each coordinate. We develop a mixed integer linear programming model as a robust counterpart of the proposed uncertain model. A key challenge is that, since the uncertain model involves nonlinear objective function of the uncertain data, classical robust optimization approaches cannot be applied directly to construct its robust counterpart. We exploit the problem structure to develop exact solution methods and present some computational results.