Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty

In view of the intermittency and uncertainty associated with both the electricity production sector of restructured power system and their competitive markets, it is necessary to develop an appropriate risk managing scheme. So that it is desirable to trade-off between optimum utilization of intermittent generation resources (i.e. renewable energy resources), uncertain market prices and related risks in order to maximize participants' benefits and minimize the corresponding risks in the multi-product market environment. The main goal of this paper is to investigate risk management by introducing a novel multi-risk index to quantify expected downside risk (EDR) which is caused by both the wind power and market price uncertainties. Value-at-Risk (VaR) method is used to assess the mentioned risk issue by the proposed weighted EDR, so that an optimal trade-off between the profit and risk is made for the system operations. Also, the roulette wheel mechanism is employed for random market price scenario generation wherein the stochastic procedure is converted into its respective deterministic equivalents. Moreover, the autoregressive integrated moving average (ARIMA) model is employed to characterize the stochastic wind farm (WF) generation by predetermined mean level and standard deviation of wind behavior as well as temporal correlation. The problem is formulated as a mixed-integer stochastic framework for a hydro-wind power system scheduling and tested on a generation company (GENCO).     

Continuous-time state-space unsteady aerodynamic modeling based on boundary element method

In this paper a continuous-time state-space aerodynamic model is developed based on the boundary element method. Boundary integral equations governing the unsteady potential flow around lifting bodies are presented and modified for thin wing configurations. Next, the BEM discretized problem of unsteady flow around flat wing equivalent to the original geometry is recast into the standard form of a continuous-time state-space model considering some auxiliary assumptions. The system inputs are time derivative of the instantaneous effective angle of attack and thickness/camber correction terms while the outputs are unsteady aerodynamic coefficients. To validate the model, its predictions for aerodynamic coefficients variations due to the various unsteady motions about different wing geometries are compared to the results of the direct BEM computations and verified numerical and theoretical solutions. This comparison indicates a good agreement. Since the resulting aerodynamic model is in the continuous-time domain, it is particularly useful for optimization and nonlinear analysis purposes. Moreover, its state-space representation is the appropriate form for an aerodynamic model in design or control applications.     

Multi-objective,multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair

Natural disasters such as earthquakes impose destructive effects in the form of human injuries and damage to properties each year. Damage caused by the earthquake can disrupt traffic and highway systems, block vehicles and relief operations and make distribution operations difficult. Therefore, the repair of damaged roads in the least possible time so that distribution of relief can be done is a significant natural phenomenon after the disaster. In this study, a new mathematical integer nonlinear multi-objective, multi-period, multi-commodity model is suggested to locate the distribution centers, for timely distribution of vital relief to the damaged areas, vehicles routing and emergency roadway repair operations. It minimizes the travel time and total cost and increases reliability of the routes. To solve the designed problem, two meta-heuristic algorithms, namely non-dominated sorting genetic algorithm-II (NSGAII) and multi-objective particle swarm optimization (MOPSO), are offered. Then, the accuracy of mathematical models and efficiency of algorithms are assessed through numerical examples in detail.

     

An analysis approach to handle uncertain multi-criteria group decision problems in the framework of interval type-2 fuzzy sets theory

Neural Computing and Applications - Uncertainty is one of the most important aspects of any decision-making process. One of the tools lately used in uncertain decision making is interval type-2...     

Oxidation of Methylene via Sn-adsorbed Boron Nitride Nanocage (B30N30): DFT Investigation

Silicon - In this study, by using of density functional theory calculations, the oxidation of methylene on surface of Tin-doped boron nitride nanocage via Langmuir Hinshelwood and Eley Rideal...     

Robot selection by a multiple criteria complex proportional assessment method under an interval-valued fuzzy environment

Recent research is recognizing that multiple criteria analysis should take account of the concepts of uncertainty and risk. In some cases, precise determination of the exact value of alternatives and weights of criteria is difficult. Consequently, to deal with these potential problems, their values are regarded as fuzzy and intervals. This paper proposes an interval-valued fuzzy multiple criteria complex proportional assessment (IVF-COPRAS) method that can reflect both a subjective judgment and objective information in real-life situations. In this method, the performance rating values versus selected criteria as well as the weights of conflicting criteria are linguistic variables represented by interval-valued triangular fuzzy numbers. Moreover, performances of alternatives against subjective criteria are described via linguistic variables and then transformed into interval-valued triangular fuzzy numbers. Finally, an application example for the robot selection problem is given in this paper to show this decision-making steps and the effectiveness of the proposed method in manufacturing companies.     

Thermodynamic design of a cascade refrigeration system of liquefied natural gas by applying mixed integer non-linear programming

Liquefied natural gas (LNG) is the most economical way of transporting natural gas (NG) over long distances. Liq-uefaction of NG using vapor compression refrigeration system requires high operating and capital cost. Due to lack of systematic design methods for multistage refrigeration cycles, conventional approaches to determine op-timal cycle are largely trial-and-error. In this paper a novel mixed integer non-linear programming (MINLP) model is introduced to select optimal synthesis of refrigeration systems to reduce both operating and capital costs of an LNG plant. Better conceptual understanding of design improvement is illustrated on composite curve (CC) and exergetic grand composite curve (EGCC) of pinch analysis diagrams. In this method a superstruc-ture representation of complex refrigeration system is developed to select and optimize key decision variables in refrigeration cycles (i.e. partition temperature, compression configuration, refrigeration features, refrigerant flow rate and economic trade-off). Based on this method a program (LNG-Pro) is developed which integrates VBA, Refprop and Excel MINLP Solver to automate the methodology. Design procedure is applied on a sample LNG plant to illustrate advantages of using this method which shows a 3.3% reduction in total shaft work consumption.     

Geometry of fiber distribution in Marl yarn using the computational method

In this research, the structure of a type of bi-component composite yarns so called Marl yarn was studied. Fibers packing density factors within the yarn cross section as well as the effect of twisting on the resultant yarn were investigated. Samples of Marl acrylic yarns were provided using different twisting levels. Images of the cross sections of the produced yarn were obtained using the transverse sectioning method. The images were captured using the optical microscopy. The prepared images of the yarn cross section were then analyzed using image processing methods. In the first stage, yarn central coordinates and radiuses were calculated. In order to investigate the uniformity of fibers distribution and packing density within the yarn cross section, the fiber distances from the yarn center within the small radial and polar elements were calculated and the coefficient of variations (CV %) was determined. The maximum value of the fibers packing density was determined. The results showed that the fibers were mostly concentrated within the central region of the yarn; this was followed by the decrease in the fibers packing density while moving into the yarn’s outer layers. It was also concluded that increasing the intensity of the applied twist would result in higher fibers packing as well as the decrease in the yarn radius.