Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

An Investigation Into Improving Off-Design Performance in a Turbocharger Turbine Utilizing Non-Radial Blading

Off-design performance now plays a vital role in the design decisions made for automotive turbocharger turbines. Of particular interest is extracting more energy at high pressure ratios and lower rotational speeds. In this region of operation the U/C value will be low and the rotor will experience high values of positive incidence at the inlet. The positive incidence causes flow to separate on the suction surface and produces high blade loading at inlet, which drives tip leakage. A CFD analysis has been carried out on a number of automotive turbines utilizing non-radial fibred blading. To help improve secondary flows yet meet stress requirements a number of designs have been investigated. The inlet blade angle has been modified in a number of ways. Firstly, the blading has been adjusted as to provide a constant back swept angle in the span wise direction. Using the results of the constant back swept blading studies, the back swept blade angle was then varied in the span wise direction. In addition to this, in an attempt to avoid an increase in stress, the effect of varying the leading edge profile of the blade was investigated. It has been seen that off-design performance is improved by implementing back swept blading at the inlet. Varying the inlet angle in the span wise direction provided more freedom for meeting stress requirements and reduces the negative impact on blade performance at the design point. The blade leading edge profile was seen to offer small improvements during off-design operation with minimal effects on stress within the rotor. However, due to the more pointed nature of the leading edge, the rotor was less tolerant to flow misalignment at the design point.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.