Modelling and evaluation of control schemes for enhancing load shift of electricity demand for cooling devices

Load balancing in electricity grids becomes a more sophisticated problem by the increased availability of time-varying stochastic supply of electricity from conversion of renewable resources like wind or sunlight. Due to the fact that large quantities of electrical energy cannot be stored easily, demand side management by shifting electrical loads is one attempt to cope with this problem. In this paper we discuss and compare two types of control signals to use the thermal storage of electrical household appliances as balancing power. As the system of our research consists of a high number of controllable refrigerators with independent parameters and behaviour, we investigate the synergetic behaviour by a simulation model. For this objective we analyze a simulation model of controllable refrigerators with respect to their ability to shift their energy demand depending on parameterized external signals. We show that both types of control signals can be used for short term reserves with delivery within 15 min of time, but they differ in possible shapes of the resulting load curves and in the reaction time of the controlled system.In addition to the simulation model we develop a model of the synergetic behaviour of an ensemble of refrigerators' reaction on control signals. This mathematical model predicts the electricity demand of ensembles of controlled appliances. As it reduces the simulation model's complexity it could be used in a sophisticated control strategy, e.g. in a model predictive control approach. The general attempt to integrate the load shift potential of cooling devices into the control of an electricity grid can probably be transferred to other electrical appliances with thermal storage capacities.     

A magnetoelectric energy harvester and management circuit for wireless sensor network

This paper presents an electromagnetic energy harvesting scheme by using a composite magnetoelectric (ME) transducer and a power management circuit. In the transducer, the vibrating wave induced from the magnetostrictive Terfenol-D plate in dynamic magnetic field is converged by using an ultrasonic horn. Consequently more vibrating energy can be converted into electricity by the piezoelectric element. A switching capacitor network for storing electricity is developed. The output of the transducer charges the storage capacitors in parallel until the voltage across the capacitors arrives at the threshold, and then the capacitors are automatically switched to being in series. More capacitors can be employed in the capacitor network to further raise the output voltage in discharging. For the weak magnetic field environment, an active magnetic generator and a magnetic coil antenna under ground are used for producing an ac magnetic field of 0.2–1 Oe at a distance of 25–50 m. In combination with the supply management circuit, the electromagnetic energy harvester with a rather weak power output (about 20 μW) under an ac magnetic field of 1 Oe can supply power for wireless sensor nodes with power consumption of 75 mW at a duration of 620 ms.     

The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs

Rapid prediction tools for reservoir over-year and within-year capacities that dispense with the sequential analysis of time-series runoff data are developed using multiple linear regression and multi-layer perceptron, artificial neural networks (MLP-ANNs). Linear regression was used to model the total (i.e. within-year + over-year) capacity using the over-year capacity as one of the inputs, while the ANNs were used to simultaneously model directly the over-year and total capacities. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation (Cv) of annual and monthly runoff, minimum monthly runoff, the demand ratio and reservoir reliability. The results showed that all the models performed well during their development and when they were tested with independent data sets. Both models offer faster prediction tools for reservoir capacity at gauged sites when compared with behaviour simulation. Additionally, when the predictor variables can be evaluated at un-gauged sites using e.g. catchment characteristics, they make capacity estimation at such un-gauged sites a feasible proposition.     

Hybrid agent-based simulation for policy evaluation of solar power generation systems

To encourage the adoption of solar power as well as new technological improvements in solar industry, state and federal governments have employed various kinds of incentives over the past decades, such as rebates, tax return opportunities, and Net Metering credits. At the same time, however, the governments concern regulations to avoid highly steep growth of solar energy without considering necessary supporting structure such as storage components, which will increase the electricity price and threaten the stability of existing transmission systems. The goal of this research is to develop a decision support tool to analyze the effectiveness of various policies (both incentives as well as regulations) on the proper growth rate of distributed photovoltaic (PV) systems avoiding the instability of the transition system or steep rising of the electricity price. To this end, we propose a hybrid two-level simulation modeling framework, which is significantly more detailed than the simplified structures commonly used in most policy evaluations. The lower-level model concerns the calculation of PV system payback period of individual household based on hourly electricity generation (PV) and consumptions, incentive levels, PV module price, and hourly electricity price (grid). The higher-level model, running on a weekly basis for 20 years, concerns the household adoption behaviors of the PV systems influenced by various factors, including payback period, household income, word-of-mouth effect and advertisement effect. Agent-based and system dynamics modeling techniques are leveraged in both levels. The proposed models have been developed for residential areas at two different regions in the US based on real data, which have been used to illustrate the impact of policies in different regions.     

A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation

Microscopic Traffic Simulation Model based on Cellular Automata (CA) has become more and more popular since it was firstly introduced by Creamer and Ludwig in 1986. Cellular automata are simpler to implement on computers, provide a simple physical picture of the system and can be easily modified to deal with different aspects of traffic. However, in a traditional traffic CA model, the spatial resolution of CA and temporal resolution of simulation are low. Take TRANSIMS for example. The size of cellular automata is 7.5 m and the time step equals 1 s. In such a case, if a vehicle drives at a speed of 4 cells per s, the speed difference between 95 km/h (3.5 1 7.5 m/s) and 121 km/h (4.4999 1 7.5 m/s) will not be distinguished by simulation models. And the temporal resolution of 1 s makes the system hard to model different drivers’ reaction time, which plays a very important role in vehicular movement models. In this paper, a microscopic traffic cellular automata model based on road network grids is proposed to overcome the low spatial and temporal resolutions of traditional traffic CA models. In our model, spatial resolution can be changed by setting different grid size for lanes and intersections before or during simulation and temporal resolution can be defined according to simulation needs to model different drivers’ reaction time, whereas the vehicular movement models are still traditional CA models. By doing so, the low spatial and temporal resolution of CA model can be overcome and the advantages of using CA to simulate traffic are preserved. The paper also presents analyses of the influences on simulation of different 1D lane grid size, 2D intersection grid size and different combinations of temporal resolution and mean drivers’ reaction time. The analysis results prove the existence of spatial and temporal resolution thresholds in traffic CA models. They also reveal that the size of grids, the combinations of different temporal resolutions and mean drivers’ reaction time do pose influences on the speed of vehicles and lane/intersection occupancy, but do not affect the volume of traffic greatly.     

Development and friction estimation of the Half-Toroidal Continuously Variable Transmission: A wind generator application

This paper presents the modeling and the computer simulation of a continuously variable transmission (CVT), applicable to a (2 kWatt power) wind turbine, as well as the evaluation of the suggested mechanism. Transmission in conventional wind turbines offers a constant ratio, as a result the rotational speed of the generator's shaft varies depending on the wind speed, so the generator usually operates at lower rpm than registered, leading to low power output. In contrast, wind turbines equipped with a CVT system offer a wider range of the power generator's shaft speed.The proposed mechanism is expected to provide a constant output shaft speed for any wind speed, maximizing the power produced. Furthermore, it is expected that the system will be able to collect more energy than conventional wind turbines and wind turbines equipped with CVT do, starting from a lower wind speed. This paper describes the operating principle of the proposed mechanism, followed by the mechanical designs of the individual components. Dynamical and Frictional Analysis of the proposed mechanism and its components are presented. The modeling of the CVT was performed using CATIA, and simulation carried out using the MSC Adams 2013 student edition.     

Domain decomposition based coupling between the lattice Boltzmann method and traditional CFD methods – Part II: Numerical solution to the backward facing step flow

The lattice Boltzmann method (LBM) and traditional finite difference methods have separate strengths when solving the incompressible Navier–Stokes equations. The LBM is an explicit method with a highly local computational nature that uses floating-point operations that involve only local data and thereby enables easy cache optimization and parallelization. However, because the LBM is an explicit method, smaller grid spacing requires smaller numerical time steps during both transient and steady state computations. Traditional implicit finite difference methods can take larger time steps as they are not limited by the CFL condition, but only by the need for time accuracy during transient computations. To take advantage of the strengths of both methods, a multiple solver, multiple grid block approach was implemented and validated for the 2-D Burgers’ equation in Part I of this work. Part II implements the multiple solver, multiple grid block approach for the 2-D backward step flow problem. The coupled LBM–VSM solver is found to be faster by a factor of 2.90 (2.87 and 2.93 for Re = 150 and Re = 500, respectively) on a single processor than the VSM for the 2-D backward step flow problem while maintaining similar accuracy.     

Optimization of the implant diameter and length in type B/2 bone for improved biomechanical properties: A three-dimensional finite element analysis

In this paper, effects of the implant diameter and length on the maximum equivalent stresses were evaluated in jaw bones, and maximum displacements examined in an implant–abutment complex by a finite element method. The implant diameter ranged from 3.0 mm to 5.0 mm, and implant length ranged from 6.0 mm to 16.0 mm. Results suggested that under axial load, the maximum equivalent stresses in cortical and cancellous bones decreased by 77.4% and 68.4% with the increasing of diameter and length respectively. Under buccolingual load, those decreased by 64.9% and 82.8%, respectively. The maximum displacements of implant-abutment complex decreased by 56.9% and 78.2% under axial and buccolingual load respectively. When the diameter exceeded 3.9 mm and the length exceeded 9.5 mm, the minimum stress/displacement was obtained. The evaluating targets were more sensitive to the diameter change than that of the length. Data indicated that the implant diameter affected stress distribution in jaw bone more than length did; and an implant diameter exceeding 3.9 mm and implant length exceeding 9.5 mm was the optimal selection for type B/2 bone in a cylinder implant by biomechanical considerations.     

Simulation of solid thermal explosion and liquid thermal explosion of dicumyl peroxide using calorimetric technique

Dicumyl peroxide (DCPO), is produced by cumene hydroperoxide (CHP) process, is utilized as an initiator for polymerization, a prevailing source of free radicals, a hardener, and a linking agent. DCPO has caused several thermal explosion and runaway reaction accidents in reaction and storage zone in Taiwan because of its unstable reactive property. Differential scanning calorimetry (DSC) was used to determine thermokinetic parameters including 700 J g^–1 of heat of decomposition (ΔH_d), 110 °C of exothermic onset temperature (T₀), 130 kJ mol^–1 of activation energy (E_a), etc., and to analyze the runaway behavior of DCPO in a reaction and storage zone. To evaluate thermal explosion of DCPO with storage equipment, solid thermal explosion (STE) and liquid thermal explosion (LTE) of thermal safety software (TSS) were applied to simulate storage tank under various environmental temperatures (T_e). T_e exceeding the T₀ of DCPO can be discovered as a liquid thermal explosion situation. DCPO was stored under room temperature without sunshine and was prohibited exceeding 67 °C of self-accelerating decomposition temperature (SADT) for a tank (radius = 1 m and height = 2 m). SADT of DCPO in a box (width, length and height = 1 m, respectively) was determined to be 60 °C. The TSS was employed to simulate the fundamental thermal explosion behavior in a large tank or a drum. Results from curve fitting demonstrated that, even at the earlier stage of the reaction in the experiments, ambient temperature could elicit exothermic reactions of DCPO. To curtail the extent of the risk, relevant hazard information is quite significant and must be provided in the manufacturing process.     

Hybrid real-coded genetic algorithm for data partitioning in multi-round load distribution and scheduling in heterogeneous systems

Data partitioning and scheduling is one the important issues in minimizing the processing time for parallel and distributed computing system. We consider a single-level tree architecture of the system and the case of affine communication model, for a general m processor system with n rounds of load distribution. For this case, there exists an optimal activation order, optimal number of processors m* (m * ≤ m), and optimal rounds of load distribution n* (n * ≤ n), such that the processing time of the entire processing load is a minimum. This is a difficult optimization problem because for a given activation order, we have to first identify the processors that are participating (in the computation process) in every round of load distribution and then obtain the load fractions assigned to them, and the processing time. Hence, in this paper, we propose a real-coded genetic algorithm (RCGA) to solve the optimal activation order, optimal number of processors m* (m * ≤ m), and optimal rounds of load distribution n* (n * ≤ n), such that the processing time of the entire processing load is a minimum. RCGA employs a modified crossover and mutation operators such that the operators always produce a valid solution. Also, we propose different population initialization schemes to improve the convergence. Finally, we present a comparative study with simple real-coded genetic algorithm and particle swarm optimization to highlight the advantage of the proposed algorithm. The results clearly indicate the effectiveness of the proposed real-coded genetic algorithm.     

P2P-based resource discovery in dynamic grids allowing multi-attribute and range queries

A key point for the efficient use of large grid systems is the discovery of resources, and this task becomes more complicated as the size of the system grows up. In this case, large amounts of information on the available resources must be stored and kept up-to-date along the system so that it can be queried by users to find resources meeting specific requirements (e.g. a given operating system or available memory). Thus, three tasks must be performed, (1) information on resources must be gathered and processed, (2) such processed information has to be disseminated over the system, and (3) upon users’ requests, the system must be able to discover resources meeting some requirements using the processed information. This paper presents a new technique for the discovery of resources in grids which can be used in the case of multi-attribute (e.g. {OS = Linux & memory = 4 GB}) and range queries (e.g. {50 GB < disk-space < 100 GB}). This technique relies on the use of content summarisation techniques to perform the first task mentioned before and strives at the main drawback found in proposals from literature using summarization. This drawback is related to scalability, and is tackled by means of using Peer-to-Peer (P2P) techniques, namely Routing Indices (RIs), to perform the second and third tasks.Another contribution of this work is a performance evaluation conducted by means of simulations of the EU DataGRID Testbed which shows the usefulness of this approach compared to other proposals from literature. More specifically, the technique presented in this paper improves on the scalability and produces good performance. Besides, the parameters involved in the summary creation have been tuned and the most suitable values for the presented test case have been found.     

Impact of Ultra Wide Band emission on WiMAX systems at 2.5 and 3.5 GHz

In this work, results of an analytical analysis to assess the effect of Ultra Wide Band (UWB) emissions on the Worldwide Interoperability for Microwave Access system (WiMAX) are presented. The WiMAX range is evaluated with and without the UWB interference. Free space propagation model is used to calculate the UWB signal power that interferes with WiMAX systems. It is shown that, for the case of single UWB transmitter, WiMAX system can easily tolerate UWB interference when the UWB EIRP is ?83 dBm/MHz or less for a distance between the UWB transmitter and the WiMAX receiver of 1 m or higher at 3.5 GHz frequency in order to have only 5% range reduction. To make this possible, multi band UWB should not transmit in the first and the second bands. This will have a detrimental effect on UWB technology. For 2.5 GHz WiMAX, the range reduction will be 5% when the UWB EIRP is ?87 dBm/MHz.     

ARC-H: Adaptive replacement cache management for heterogeneous storage devices

Heterogeneous storage architectures combine the strengths of different storage devices in a synergistically useful fashion, and are increasingly being used in mobile storage systems. In this paper, we propose ARC-H, an adaptive cache replacement algorithm for heterogeneous storage systems consisting of a hard disk and a NAND flash memory. ARC-H employs a dynamically adaptive management policy based on ghost buffers and takes account of recency, I/O cost per device, and workload patterns in making cache replacement decisions. Realistic trace-driven simulations show that ARC-H reduces service time by up to 88% compared with existing caching algorithms with a 20 Mb cache. ARC-H also reduces energy consumption by up to 81%.     

微网内储能单元的建模及协调控制

通过仿真建模的方式研究电池类储能单元与电网负荷交互的特性以及储能单元对实现微电网电量自平衡的重要作用。根据锂离子电池特性,从电化学角度分析建立等效电路模型,通过对实际3．4V／3Ah锂离子电池充放电曲线的分析计算来确定电池模型参数,模型仿真曲线与实际充放电曲线拟合程度高。进一步构建储能单元模型,模拟仿真微电网中负载发生突变的情况,可以观察到储能单元对于微电网能量自平衡的贡献。     

A distributed optical fiber sensor for temperature detection in power cables

The power transfer capacity of an underground power cable is limited by high-temperature regions that occur along the cable. It is very difficult to determine and control these ‘hot spots’. Optimum use and temperature profile control of power cables before and during load transmission can be achieved with real-time processing of temperature data. There are various methods developed for this purpose such as conventional point temperature measurement method, where a large number of sensors and connectors are required, and methods based on mathematical models which can only approach real values by approximation.In this study, temperature detection in an XLPE insulated 154 kV power cable is performed using a distributed sensing method where the optical fiber itself behaves as a sensor. Therefore, there is no need for the devices of conventional method. Moreover, contrary to methods based on mathematical models, where it is difficult to predict environmental variations, this method considers the variations with a temperature resolution of ±1 °C. Distributed temperature sensing (DTS) method, detection system configuration and required system parameters are explained in the paper. Experimental results obtained for 126 and 412 m cables show a temperature resolution of ±1 °C and a spatial resolution of 1.22 m. Simulations for a 10 km cable are also given. Results show that DTS is a reliable method for both short and long range cable systems.     

Device characteristics for Pt–Ti–O gate Si–MISFETs hydrogen gas sensors

A novel Pt–Ti–O-gate Si–metal–insulator–semiconductor field-effect transistor (MISFET) hydrogen gas sensor has been proposed by Usagawa and Kikuchi (2010) [1]. The sensors consist of unique gate structures composed of Ti and oxygen accumulated regions around Pt grains on top of a novel mixing layer of nanocrystalline TiO_x and superheavily oxygen-doped amorphous Ti formed on SiO₂/Si substrates. The optimum Pt/Ti thickness and annealing conditions for most hydrogen safety monitoring sensor systems are obtained by annealing Pt(15 nm)/Ti(5 nm)-gate Si–MOS structures in air around 400 °C for 2 h. One of the advantages of the Pt–Ti–O-gate Si–MISFETs after 10 min of air-diluted 1000-ppm hydrogen exposure at 115 °C are reproducible and uniform threshold voltage of V_th in addition to large sensing amplitudes at a practically important hydrogen concentration range between 100 ppm and 1%. The analysis of device characteristics of the Pt–Ti–O-gate Si–MISFETs hydrogen sensors concludes that the oxidation process of the Ti layer is consistently explained by an oxidation model that the oxygen invasion into Ti layer comes from open air through Pt grain boundaries and at the same time Ti will evacuate into the Pt surface through Pt grain boundaries. During the course of this process, the invading oxygen will be balanced with the evacuating Ti so that the Ti layer keeps nearly the same thickness with the as grown states. Ti and oxygen will remains around Pt grains named Ti and oxygen merged corridors.     

Integration of growing self-organizing map and continuous genetic algorithm for grading lithium-ion battery cells

This study attempts to employ growing self-organizing map (GSOM) algorithm and continuous genetic algorithm (CGA)-based SOM (CGASOM) to improve the performance of SOM neural network (SOMnn). The proposed GSOM + CGASOM approach for SOMnn is consisted of two stages. The first stage determines the SOMnn topology using GSOM algorithm while the weights are fine-tuned by using CGASOM algorithm in the second stage. The proposed CGASOM algorithm is compared with other two clustering algorithms using four benchmark data sets, Iris, Wine, Vowel, and Glass. The simulation results indicate that CGASOM algorithm is able to find the better solution. Additionally, the proposed approach has been also employed to grade Lithium-ion cells and characterize the quality inspection rules. The results can assist the battery manufacturers to improve the quality and decrease the costs of battery design and manufacturing.     

Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code

One of the main difficulties using multi-point statistical (MPS) simulation based on annealing techniques or genetic algorithms concerns the excessive amount of time and memory that must be spent in order to achieve convergence. In this work we propose code optimizations and parallelization schemes over a genetic-based MPS code with the aim of speeding up the execution time. The code optimizations involve the reduction of cache misses in the array accesses, avoid branching instructions and increase the locality of the accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of loops using a shared-memory programming model (OpenMP) and a coarse-grain distribution of load among several computational nodes using a distributed-memory programming model (MPI). Convergence, execution time and speed-up results are presented using 2D training images of sizes 100 × 100 × 1 and 1000 × 1000 × 1 on a distributed-shared memory supercomputing facility.     

Efficient multicast schemes for 3-D Networks-on-Chip

3-D Networks-on-Chip (NoCs) have been proposed as a potent solution to address both the interconnection and design complexity problems facing future System-on-Chip (SoC) designs. In this paper, two topology-aware multicast routing algorithms, Multicasting XYZ (MXYZ) and Alternative XYZ (AL + XYZ) algorithms in supporting of 3-D NoC are proposed. In essence, MXYZ is a simple dimension order multicast routing algorithm that targets 3-D NoC systems built upon regular topologies. To support multicast routing in irregular regions, AL + XYZ can be applied, where an alternative output channel is sought to forward/replicate the packets whenever the output channel determined by MXYZ is not available. To evaluate the performance of MXYZ and AL + XYZ, extensive experiments have been conducted by comparing MXYZ and AL + XYZ against a path-based multicast routing algorithm and an irregular region oriented multiple unicast routing algorithm, respectively. The experimental results confirm that the proposed MXYZ and AL + XYZ schemes, respectively, have lower latency and power consumption than the other two routing algorithms, meriting the two proposed algorithms to be more suitable for supporting multicasting in 3-D NoC systems. In addition, the hardware implementation cost of AL + XYZ is shown to be quite modest.     

Multi-joint histogram based modelling for image indexing and retrieval

This paper presents a novel approach for image retrieval, named multi-joint histogram based modelling (MJHM), in which the joint correlation histograms are constructed between the motif and texton maps. Firstly, the quantized image is divided into non-overlapping 2 × 2 grids. Then each grid is replaced by a scan motif and texton values to construct the transformed motif and texton maps (images) respectively. The motif transformed map minimizes the local gradient and texton transformed map identifies the equality of grayscales while traversing the 2 × 2 grid. Finally, the correlation histograms are constructed between the transformed motif and texton maps. The performance of the proposed method (MJHM) is tested by conducting two experiments on Corel-5K and Corel-10K benchmark databases. The results after investigation show significant improvements in terms of precision, average retrieval precision (ARP), recall and average retrieval rate (ARR) as compared to multi-texton histogram (MTH), smart content based image retrieval system (CMCM) and other state-of-the-art techniques for image retrieval.