Profile-Based Load Scheduling in Wireless Energy Harvesting Sensors for Data Rate Maximization

In this paper, power management algorithms for energy harvesting sensors (EHS) that operate purely based on energy harvested from the environment are proposed. To maintain energy neutrality, EHS nodes schedule their utilization of the harvested power so as to save/draw energy into/from an inefficient battery during peak/low energy harvesting periods, respectively. Under this constraint, one of the key system design goals is to transmit as much data as possible given the energy harvesting profile. For implementational simplicity, it is assumed that the EHS transmits at a constant data rate with power control, when the channel is sufficiently good. By converting the data rate maximization problem into a convex optimization problem, the optimal load scheduling (power management) algorithm that maximizes the average data rate subject to energy neutrality is derived. Also, the energy storage requirements on the battery for implementing the proposed algorithm are calculated. Further, robust schemes that account for the insufficiency of battery storage capacity, or errors in the prediction of the harvested power are proposed. The superior performance of the proposed algorithms over conventional scheduling schemes are demonstrated through computations using numerical data from solar energy harvesting databases.

Decentralized peak power control based on short-term forecast using modified Kalman filter

This paper proposes a method of short term load forecasting with limited data, applicable even at 11 kV substation levels where total power demand is relatively low and somewhat random and weather data are usually not available as in most developing countries. Kalman filtering technique has been modified and used to forecast daily and hourly load. Planning generation and interstate energy exchange schedule at load dispatch centre and decentralized Demand Side Management at substation level are intended to be carried out with the help of this short term load forecasting technique especially to achieve peak power control without enforcing load-shedding.

Design and Testing of a Control System for Supply Side Electrical Load Management

India's energy demand is increasing rapidly with the intensive growth of economy. The electricity demand in India exceeded the availability, both in terms of base load energy and peak availability. The efficient use of energy source and its conversion and utilizations are the viable alternatives available to the utilities or industry. There are essentially two approaches to electrical energy management. First at the supply / utility end (Supply Side Management or SSM) and the other at the consumer end (Demand Side Management or DSM). This work is based on Supply Side Management (SSM) protocol and consists of design, fabrication and testing of a control device that will be able to automatically regulate the power flow to an individual consumer's premise. This control device can monitor the overuse of electricity (above the connected load or contracted demand) by the individual consumers. The present project work specially emphasizes on contract demand of every consumer and tries to reduce the use beyond the contract demand. This control unit design includes both software and hardware work and designed for 0.5 kW contract demand. The device is tested in laboratory and reveals its potential use in the field.

Space-Vector-Based Hybrid PWM Technique to Reduce Peak-to-Peak Torque Ripple in Induction Motor Drives

Constant-volts-per-hertz induction motor drives and vector-controlled induction motor drives utilize pulsewidth modulation (PWM) to control the voltage applied on the motor. The method of PWM influences the pulsations in the torque developed by the motor. A space-vector-based approach to PWM facilitates special switching sequences involving the division of active state time. This paper proposes a space-vector-based hybrid PWM technique, which is a combination of the conventional and special switching sequences. The proposed hybrid PWM technique results in a lower peak-to-peak torque ripple than conventional space vector PWM(CSVPWM) at high speeds of an induction motor drive. Furthermore, the magnitude of the dominant torque harmonic due to the proposed hybrid PWM is significantly lower than that due to CSVPWM at high speeds of the drive. Experimental results from a 3.75-kW sensorless vector-controlled induction motor drive under various load conditions are presented to support analytical and simulation results.

Increasing Critical Sensitivity of the Load/Unload Response Ratio before Large Earthquakes with Identified Stress Acclumulation Pattern

The Load/Unload Response Ratio (LURR) method is proposed for short-to-intermediate-term earthquake prediction [Yin, X.C., Chen, X.Z., Song, Z.P., Yin, C., 1995. A New Approach to Earthquake Prediction — The Load/Unload Response Ratio (LURR) Theory, Pure Appl. Geophys., 145, 701–715]. This method is based on measuring the ratio between Benioff strains released during the time periods of loading and unloading, corresponding to the Coulomb Failure Stress change induced by Earth tides on optimally oriented faults. According to the method, the LURR time series usually climb to an anomalously high peak prior to occurrence of a large earthquake. Previous studies have indicated that the size of critical seismogenic region selected for LURR measurements has great influence on the evaluation of LURR. In this study, we replace the circular region usually adopted in LURR practice with an area within which the tectonic stress change would mostly affect the Coulomb stress on a potential seismogenic fault of a future event. The Coulomb stress change before a hypothetical earthquake is calculated based on a simple back-slip dislocation model of the event. This new algorithm, by combining the LURR method with our choice of identified area with increased Coulomb stress, is devised to improve the sensitivity of LURR to measure criticality of stress accumulation before a large earthquake. Retrospective tests of this algorithm on four large earthquakes occurred in California over the last two decades show remarkable enhancement of the LURR precursory anomalies. For some strong events of lesser magnitudes occurred in the same neighborhoods and during the same time periods, significant anomalies are found if circular areas are used, and are not found if increased Coulomb stress areas are used for LURR data selection. The unique feature of this algorithm may provide stronger constraints on forecasts of the size and location of future large events.

A modified TRIMP to quantify the in-season training load of team sport players

Thatcher, Rhys, et al., 'A modified TRIMP to quantify the in-season training load of team sport players', Journal of Sport Sciences, (2007) 25(6) pp.629-634 RAE2008

Load-balancing for mesh-based applications on heterogeneous cluster computers

This paper discusses load-balancing issues when using heterogeneous cluster computers. There is a growing trend towards the use of commodity microprocessor clusters. Although today's microprocessors have reached a theoretical peak performance in the range of one GFLOPS/s, heterogeneous clusters of commodity processors are amongst the most challenging parallel systems to programme efficiently. We will outline an approach for optimising the performance of parallel mesh-based applications for heterogeneous cluster computers and present case studies with the GeoFEM code. The focus is on application cost monitoring and load balancing using the DRAMA library.

Delamination threshold load for dynamic impact on plates

A criterion is derived for delamination onset in transversely isotropic laminated plates under small mass, high velocity impact. The resulting delamination threshold load is about 21% higher than the corresponding quasi-static threshold load. A closed form approximation for the peak impact load is then used to predict the delamination threshold velocity. The theory is validated for a range of test cases by comparison with 3D finite element simulation using LS-DYNA and a newly developed interface element to model delamination onset and growth. The predicted delamination threshold loads and velocities are in very good agreement with the finite element simulations. Good agreement is also shown in a comparison with published experimental results. In contrast to quasi-static impacts, delamination growth occurs under a rapidly decreasing load. Inclusion of finite thickness effects and a proper description of the contact stiffness are found to be vital for accurate prediction of the delamination threshold velocity

Domestic fridge-freezer load aggregation to support ancillary services

Grid operators and electricity retailers in Ireland manage peak demand, power system balancing and grid congestion by offering relevant incentives to consumers to reduce or shift their load. The need for active consumers in the home using smart appliances has never been greater, due to increased variable renewable generation and grid constraints. In this paper an aggregated model of a single compressor fridge-freezer population is developed. A price control strategy is examined to quantify and value demand response savings during a representative winter and summer week for Ireland in 2020. The results show an average reduction in fridge-freezer operating cost of 8.2% during winter and significantly lower during summer in Ireland. A peak reduction of at least 68% of the average winter refrigeration load is achieved consistently during the week analysed using a staggering control mode. An analysis of the current ancillary service payments confirms that these are insufficient to ensure widespread uptake by the small consumer, and new mechanisms need to be developed to make becoming an active consumer attractive. Demand response is proposed as a new ancillary service called ramping capability, as the need for this service will increase with more renewable energy penetration on the power system.

Load Control Timescales Simulation in a Multi-Agent Smart Grid Platform

Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers’ consumption profile, helping to reduce peak demand. Aiming to support small players’ participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques – the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Use of water-miscible retinyl palmitate as markers of chylomicrons gives earlier peak response of plasma retinyl esters compared with oil-soluble retinyl palmitate

Delayed peak response of plasma retinyl esters (RE) relative to plasma triacylglycerols (TAG) and apolipoprotein (Apo) B-48 responses following a fat load supplemented with vitamin A raised doubts about the use of vitamin A to label dietary-derived lipids and lipoproteins. The present study compared the use of water-miscible and oil-soluble retinyl palmitate (RP) as markers of dietary-derived lipoproteins in healthy subjects along with the measurements of postprandial plasma TAG and ApoB-48 responses to investigate whether the delayed peak response observed was due to delayed intestinal output of RE from oil-based solutions. Nine healthy female subjects were given a standard test meal containing a dose (112 mg) of RP in either water-miscible or oil-soluble form in random order, on two separate occasions after a 12 h overnight fast. The results showed that the mean plasma RE concentrations reached a peak significantly later than mean plasma TAG and ApoB-48 concentrations when oil-soluble RP was consumed, whereas plasma RE peaked earlier relative to plasma TAG and ApoB-48 responses when water-miscible RP was used. The results suggested a more rapid absorption with a significantly higher and earlier peak response of plasma RE when water-miscible RP was consumed. This was in contrast to the delayed initial appearance and later sustained higher concentrations of plasma RE during the late postprandial period when oil-soluble RP was consumed. The RE response to the water-miscible RP showed better concordance with plasma TAG response than that of oil-soluble RP.

Constrained principal component analysis reveals functionally connected load-dependent networks involved in multiple stages of working memory

Constrained principal component analysis (CPCA) with a finite impulse response (FIR) basis set was used to reveal functionally connected networks and their temporal progression over a multistage verbal working memory trial in which memory load was varied. Four components were extracted, and all showed statistically significant sensitivity to the memory load manipulation. Additionally, two of the four components sustained this peak activity, both for approximately 3 s (Components 1 and 4). The functional networks that showed sustained activity were characterized by increased activations in the dorsal anterior cingulate cortex, right dorsolateral prefrontal cortex, and left supramarginal gyrus, and decreased activations in the primary auditory cortex and "default network" regions. The functional networks that did not show sustained activity were instead dominated by increased activation in occipital cortex, dorsal anterior cingulate cortex, sensori-motor cortical regions, and superior parietal cortex. The response shapes suggest that although all four components appear to be invoked at encoding, the two sustained-peak components are likely to be additionally involved in the delay period. Our investigation provides a unique view of the contributions made by a network of brain regions over the course of a multiple-stage working memory trial.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Grip and load force coordination in cyclical isometric manipulation task is not affected by the feedback type

Background: The relationship between normal and tangential force components (grip force - GF and load force - LF, respectively) acting on the digits-object interface during object manipulation reveals neural mechanisms involved in movement control. Here, we examined whether the feedback type provided to the participants during exertion of LF would influence GF-LF coordination and task performance. Methods. Sixteen young (24.7 ±3.8 years-old) volunteers isometrically exerted continuously sinusoidal FZ (vertical component of LF) by pulling a fixed instrumented handle up and relaxing under two feedback conditions: targeting and tracking. In targeting condition, FZ exertion range was determined by horizontal lines representing the upper (10 N) and lower (1 N) targets, with frequency (0.77 or 1.53 Hz) dictated by a metronome. In tracking condition, a sinusoidal template set at similar frequencies and range was presented and should be superposed by the participants' exerted FZ. Task performance was assessed by absolute errors at peaks (AEPeak) and valleys (AEValley) and GF-LF coordination by GF-LF ratios, maximum cross-correlation coefficients (r max), and time lags. Results: The results revealed no effect of feedback and no feedback by frequency interaction on any variable. AE Peak and GF-LF ratio were higher and rmax lower at 1.53 Hz than at 0.77 Hz. Conclusion: These findings indicate that the type of feedback does not influence task performance and GF-LF coordination. Therefore, we recommend the use of tracking tasks when assessing GF-LF coordination during isometric LF exertion in externally fixed instrumented handles because they are easier to understand and provide additional indices (e.g., RMSE) of voluntary force control. © 2013 Pedão et al.; licensee BioMed Central Ltd.