Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.     

基于SVM的煤与瓦斯突出预测模型及应用

为有效预测矿井内煤与瓦斯突出的危险程度,对其影响因素做了分析与探讨,分别构建了基于粒子群优化算法以及遗传算法支持向量机的煤与瓦斯突出预测模型,并且通过实例对两种模型预测的准确性进行了验证。分别利用单项以及综合指标、BP神经网络以及PSO-SVM模型、GA-SVM模型,对寺河煤矿二号井的突出区域进行预测比较。结果表明,PSO-SVM的预测模型不仅可以在小样本数据中预测出煤与瓦斯突出程度的大小,而且综合预测结果更加精确,其在解决矿井内煤与瓦斯突出的小样本数据中显示出更加强大、通用的性能。     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems

Airline disruptions incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated aircraft and crew schedule recovery problem. A two stage heuristic algorithm for the integrated recovery problem is proposed. In the first stage, the integrated aircraft recovery and flight-rescheduling model with partial crew consideration is built. This model is based on the traditional multi-commodity network model for the aircraft schedule recovery problem. The objective of this model also includes minimization of the original crew connection disruption. In the second stage, the integrated crew schedule recovery and flight re-scheduling model with partial aircraft consideration is built. We proposed a new multi-commodity model for the crew schedule recovery. The main advantage of such model is that it is much more efficient to integrate the flight-scheduling and aircraft consideration. New constraints are incorporated to guarantee that the aircraft connections generated in the stage 1 are still feasible. Two stages are run iteratively until no improvement can be achieved. Experimental results show that our method can provide better recovery solutions compared with the benchmark algorithms.     

Enhancement of optical and electrical properties of SILAR deposited ZnO thin films through fluorine doping and vacuum annealing for photovoltaic applications

Undoped and fluorine doped ZnO thin films were deposited onto glass substrates using successive ionic layer adsorption and reaction (SILAR) technique and then annealed at 350 °C in vacuum ambience. The F doping level was varied from 0 to 15 at% in steps of 5 at%. The XRD analysis showed that all the films are polycrystalline with hexagonal wurtzite structure and preferentially oriented along the (002) plane. Crystallite sizes were found to increase when 5 at% of F is doped and then decreased with further doping. It was seen from the SEM images that the doping causes remarkable changes in the surface morphology and the annealing treatment results in well-defined grains with an improvement in the grain size irrespective of doping level. All the films exhibit good transparency (>70%) after vacuum annealing. Electrical resistivity of the film was found to be minimum (1.32×10⁻³ Ω cm) when the fluorine doping level was 5 at%.     

Stability and cytotoxicity study of NiFe2O4 nanocomposites synthesized by co-precipitation and subsequent thermal annealing

In this article, NiFe₂O₄ nanoparticles (NPs) were prepared by co-precipitation method with subsequent thermal annealing leading to obtainment NPs with average sizes of 78 nm. Structure and magnetic analysis were performed by X-ray diffraction, transmission electron microscopy, Mössbauer spectroscopy and vibrating sample magnetometry techniques. Stability of NiFe₂O₄ NPs was evaluated in PBS solution during 20 days. The toxicity of prepared NPs was evaluated in vitro using different cancer cell lines: HeLa (cervical cancer cell), PC-3 (prostate cancer cell). Fibroblasts like cells of L929 obtained from subcutaneous adipose tissue of mouse were used as normal cells. Results indicate successful synthesis of NiFe₂O₄ NPs that exhibit low cytotoxicity in concentration range from 1 to 100 μg/ml. Presented physical and biological results indicate the possibility of application investigated magnetic nanoparticles in hyperthermia, targeted drug delivery, magnetic resonance imaging or cell separation.     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.