جستجوی مقالات مرتبط با کلیدواژه "uncertainty" در نشریات گروه "فناوری اطلاعات"

Renewable energy sources are particularly important in clean energy transitions and must be considered in Generation Expansion Planning (GEP) problems due to low cost, ease of installation, and ability to implement Demand Response (DR) programs. However, challenges such as the stochastic nature of renewable energy sources, consumer unawareness regarding participation in DR programs, and difficulties in integrating some resources have posed challenges to the use of these resources in the GEP problem. This paper addresses these challenges by using the Weibull distribution function to model wind power plants' uncertainty and rewards and penalties to motivate consumer participation in the GEP problem. To achieve these objectives, initially, the adequacy assessment of the generation system is performed analytically using the reliability index, which includes Expected Energy Not Supplied (EENS), considering the forced outage rate of generators in the DIgSILENT power factory through Python programming. Subsequently, an optimized GEP model is presented to enhance the generation system's adequacy against short-term demand for the next year. In this model, wind farms along with the DR program are integrated and optimized using the genetic algorithm, employing Python programming. The genetic algorithm selects the number of existing turbines in the wind power plant and the level of consumer participation needed to reduce the EENS to the desired value at the minimum cost. Validation of the proposed model is conducted on a 9-bus network. The strength of the presented method lies in its applicability to real-world networks modeled in the DIgSILENT power factory.

Today, cloud markets, especially Amazon, have attracted a lot of attention from users due to the provision of Spot Virtual Machines (SVMs). It has several advantages for both sides of the market. On the one hand, Amazon can generate revenue from its underutilized virtual machines. On the other hand, the customer can get the SVM as needed at a dynamic price through an auction method. Providing optimal bidding strategies in such a market is a crucial challenge. The bidding price is affected by uncertain parameters such as the price of SVMs, the number of available SVMs, the number of current customers, and their bidding values. In this paper, we use Information Gap Decision Theory (IGDT) to determine the best bidding strategy. Our proposed method includes both risk-averse and risk-neutral strategies. The evaluation results based on historical Amazon EC2 prices confirm the effectiveness of the proposed method in the presence of uncertain prices. It has high performance compared to the baseline methods in terms of robustness cost, uncertainty budget, and execution time.

With the presence of distributed energy resources in the microgrid, the problem of load-frequency control (LFC) becomes one of the most important concerns. With changing the parameters of the microgrid components as well as the disturbances forced to the grid, designing a suitable LFC becomes more difficult. In this paper, the design of a Robust model predictive controller (RMPC) based on the linear matrix inequality as a secondary controller LFC system is discussed for controlling a microgrid on the shipboard. The main purpose of the proposed method is to improve the frequency stability of the microgrid in the presence of disturbances and the uncertainty of its parameters. The proposed controller simulation results, in several different scenarios, considering The uncertainty of the microgrid parameters as well as the input disturbances are compared. The main controllers are the fuzzy proportional-integral type1 and 2, and multi-objective multi-purpose functions optimized with the MOFPI (MBBHA), MOIT2FPI (MBHA) algorithm. The effectiveness of the proposed method in terms of The response speed and reduction of fluctuations and overcome uncertainties of the parameters, as well as robustness to disturbances, are discussed. Simulation is implemented in MATLAB software. The proposed method reduces the frequency oscillations caused by disturbances on the microgrid by 68% (68% improvement over other methods used in this field). Also, using this method, the damping speed of microgrid frequency fluctuations is increased by 53% (performance improvement).

In most real-world issues, we are dealing with situations where accurate data and complete information are not available. One way to deal with these uncertainties in real life is to use Grey System Theory (GST). In this paper, a linear programing problem in a grey environment with interval Grey Numbers (GN) is considered. Most of the proposed methods for solving grey linear programing problems are done by using GN whitening and turning the problem into a common linear programing problem. However, in this paper we seek to solve the grey linear programing problem directly without turning it into a regular linear programing problem in order to maintain uncertainty in the original problem data in the final answer. For this purpose, by proving the desired theorems, we propose a method based on the initial simplex algorithm to solve grey linear programing problems. This method is simpler than the previous methods. We emphasize that the proposed concept is useful for real and practical situations. To illustrate the efficiency of the method, we solve an example of Grey Linear Programming (GLP).

This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.

Linear programming problems with interval grey numbers have recently attracted some interest. In this paper, we study linear programs in which right hand sides are interval grey numbers. This model is relevant when uncertain and inaccurate factors make difficult the assignment of a single value to each right hand side. Some methods have been developed for solving these problems. In this paper, we propose a new approach for solving interval grey number linear programming problems is introduced without converting them to classical linear programming problems. A numerical example is provided to illustrate the proposed approach.

By introducing Colpitts oscillator as a chaotic system, this paper deals with back-stepping control method and investigates the restrictions and problems of the controller where non-existence of a suitable response in the presence of uncertainty is the most important problem to note. In this paper, the back-stepping sliding mode method is introduced as a robust method for controlling nonlinear Colpitts oscillator system with chaotic behavior. Thereafter, we simulated the proposed method and compared its advantages with that of the previous method.  The experimental results show that the most important advantages of the proposed method are making system robust in case of uncertainties and disturbances, and also having a fast response.

In this paper, a novel filter is provided that estimates the states of any nonlinear system, both in the presence and absence of uncertainty with high accuracy. It is well understood that a robust filter design is a compromise between the robustness and the estimation accuracy. In fact, a robust filter is designed to obtain an accurate and suitable performance in presence of modelling errors. So in the absence of any unknown or time-varying uncertainties, the robust filter does not provide the desired performance. The new method provided in this paper, which is named hybrid robust cubature Kalman filter (CKF), is constructed by combining a traditional CKF and a novel robust CKF. The novel robust CKF is designed by merging a traditional CKF with an uncertainty estimator so that it can provide the desired performance in the presence of uncertainty. Since the presence of uncertainty results in a large innovation value, the hybrid robust CKF adapts itself according to the value of the normalized innovation. The CKF and robust CKF filters are run in parallel and at any time, a suitable decision is taken to choose the estimated state of either the CKF or the robust CKF as the final state estimation. To validate the performance of the proposed filters, two examples are given that demonstrate their promising performance.

In traditional data envelopment analysis (DEA) the uncertainty of inputs and outputs is not considered when evaluating the performance of a unit. In other words, effects of uncertainty on optimality and feasibility of models are ignored. This paper introduces a new model for measuring the efficiency of decision making units (DMUs) having interval inputs and outputs. The proposed model is based on interval DEA (IDEA) in which the inputs and outputs are limited to be within uncertainty bounds. In this model, the inputs and outputs take fixed values for each DMU such that the sum of efficiencies is maximized. The DMUs are evaluated by the same production possibility set (PPS). The efficiency is measured based on the proposed conservatism level for each input and output. Indeed, the inputs and outputs are defined by the presented conservatism level. The proposed model is integrated measuring all the DMUs efficiencies simultaneously. These efficiency scores lie between the optimistic and pessimistic cases introduced by Despotis and Similar (2002) [11].