Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, MG (Micro-Grid) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a CLS (Chaotic Local Search) mechanism and a FSA (Fuzzy Self Adaptive) structure is utilized. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization).     

Physical mechanisms of heat transfer during single bubble nucleate boiling of FC-72 under saturation conditions. II: Theoretical analysis

This paper is the second part of a two-part study concerning the dynamics of heat transfer during the nucleation process of FC-72 liquid. The experimental findings on the nature of different heat transfer mechanisms involved in the nucleation process were discussed in part I. In this paper, the experimental results are compared with the existing boiling models. The boiling models based on dominance of a single mechanism of heat transfer did not match the experimental results. However, the Rohsenow model was found to closely predict the heat transfer through the microconvection mechanism that is primarily active outside the bubble/surface contact area. An existing transient conduction model was modified to predict the surface heat transfer during the rewetting process (i.e. transient conduction mechanism). This model takes into account the gradual rewetting of the surface during the transient conduction process rather than a simple sudden surface coverage assumption commonly used in the boiling literature. The initial superheat energy of the microlayer (i.e. microlayer sensible energy) was accurately calculated and found to significantly contribute in microlayer evaporation. This even exceeded the direct wall heat transfer to microlayer at high surface superheat temperatures. A composite model was introduced that closely matches our experimental results. It incorporates models for three mechanisms of heat transfer including microlayer evaporation, transient conduction, microconvection, as well as their influence area and activation time. The significance of this development is that, for the first time, all submodels of the composite correlation were independently verified using experimental results.     

Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem

This paper introduces a decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for the chaos control problem in a system without precise system model information. It has on-line learning ability to deal with the parametric uncertainty and disturbance by adjusting the control parameters and no constrained conditions and prior knowledge of the controlled plant is required in the design process. Also, a decoupled adaptive sliding mode controller is developed to control the chaotic Lorenz system for comparison. Finally, the effectiveness of the proposed decoupled adaptive sliding mode and DANF sliding mode controllers are demonstrated by some simulated results.     

Learning gender with support faces

Nonlinear support vector machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET (FacE REcognition Technology) face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques, such as radial basis function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the difference in classification performance with low-resolution "thumbnails" (21×12 pixels) and the corresponding higher-resolution images (84×48 pixels) was found to be only 1%, thus demonstrating robustness and stability with respect to scale and the degree of facial detail     

Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers’ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers’ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.     

Modeling and prioritizing demand response programs in power markets

One of the responsibilities of power market regulator is setting rules for selecting and prioritizing demand response (DR) programs. There are many different alternatives of DR programs for improving load profile characteristics and achieving customers’ satisfaction. Regulator should find the optimal solution which reflects the perspectives of each DR stakeholder. Multi Attribute Decision Making (MADM) is a proper method for handling such optimization problems. In this paper, an extended responsive load economic model is developed. The model is based on price elasticity and customer benefit function. Prioritizing of DR programs can be realized by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Considerations of ISO/utility/customer regarding the weighting of attributes are encountered by entropy method. An Analytical Hierarchy Process (AHP) is used for selecting the most effective DR program. Numerical studies are conducted on the load curve of the Iranian power grid in 2007.     

Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants’ corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos’ optimal bilateral contracts is proposed and the impacts of these contracts on GenCos’ optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model.     

Studying the rheology,optical clarity and surface tension of an acrylic/melamine automotive clearcoat loaded with different additives

Acrylic/melamine based automotive clearcoats were prepared by inclusion of functional and conventional additives. The conventional additives used were based on polysiloxane chains without any reactive groups. On the other hand, the functional additives were based on polyacrylate and polysiloxane backbones. The additives were added to the clearcoat formulations at different concentrations. Surface tension, optical properties and rheological behaviors of the clearcoats were studied by tensiometer, gonio-spectrophotometer, rheometric mechanical spectrometer (RMS) and Brookfield techniques. Results showed that addition of both conventional and functional additives reduced the surface tension of the clearcoat up to a certain value of the additive concentration. It was seen that functional additives could reduce surface tension much greater than the conventional ones. The coating clarity and transparency were not affected in presence of conventional additives. However, the functional additives, especially the one having higher molecular weight, reduced the coating transparency. The clearcoat viscosity was increased using functional additives. A shear thickening behavior of the clearcoats loaded with functional additives was seen. The conventional additives did not change the clearcoat viscosity. It was concluded that the additive functionality, molecular weight and chemical structure were influential parameters affecting the final properties.     

Effect of different titania phases on the hydrogen gas sensing features of polyaniline/TiO2 nanocomposite

This paper presents a resistance-based hydrogen gas sensor using polyaniline (emeraldine)/TiO₂ nanocomposite (PT–NC) thin film. It is demonstrated that different gas sensing features can arise when various TiO₂ phases (anatase and rutile) are applied. The different wt% of TiO₂ phases were dispersed into an acidic solution of aniline monomers and PT–NCs were synthesized by an in-situ self-assembly chemical oxidative polymerization method of aniline. PT–NCs deposited on an epoxy glass substrate having Cu-interdigited electrodes for hydrogen gas sensing at air pressure and room temperature. Our results show that the better sensitivity of the sensor strongly depends on the sensor surface morphology and its components. Furthermore, hydrogen gas sensing mechanism of the sensor based contact areas between Pani chains and TiO₂ grains was studied.