Wing Hong Lau

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.

A speedy cardiovascular diseases classifier using multiple criteria decision analysis.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.

A Regulation Policy of EV Discharging Price for Demand Scheduling

The widespread penetration of electric vehicles (EVs) will cause an increase in energy demand. To achieve an effective demand response approach, the participation of EV users is a crucial factor. Nevertheless, the discharging price found in the literature is generally set in accordance with the spot electricity price. A fair pricing strategy is thus desirable to motivate EV users to regulate their charging behavior for less bill payment, and at the same time to achieve load regulation as well as benefit for power companies. The charging and discharging prices should be cohesively equilibrated to achieve the task. This paper presents a new V2G (Vehicle-to-Grid) pricing policy by incorporating the system load condition, maximum power limit, and price rate for user load in a fair manner. The price setting follows a hierarchical optimization procedure between the operator and end users with the aim to maximize operators profit and balance end users’ bill and comfort level. The optimization for the operator is achieved via genetic algorithm whereas the energy management of each user is decomposed as a single power optimization problem. Simulation results have confirmed the benefit of liberating the discharging price from the user load tariff and have verified the effectiveness of the proposed method.

A new schedule-controlled strategy for charging large number of EVs with load shifting and voltage regulation

Electric vehicles (EVs) is an attractive solution to replace traditional petrol cars. The penetration of EVs posts a challenge for power systems since it would inevitably incur undesirable voltage drops. EV load shedding can be performed to regulate the grid voltage. The EV charging location is also an important factor when shifting the EV loads. This paper presents a new schedule-controlled framework with the aims of load shifting and voltage regulation to control EV charging operations for radial distribution networks. The new strategy consists of two stages, of which the initial phase performs EV scheduling in consideration of both EV constraints and power system limitations, and the second control layer checks for voltage violations with essential EV rescheduling. A voltage sensitivity analysis method is employed to alleviate the impacts of EV charging locations. The control scheme is evaluated through a modified standard IEEE 33 nodes test feeder. The results demonstrate the control scheme can effectively satisfy normal demands of EVs and achieve the voltage regulation purpose.

A Schedule-Control Aided Strategy for Charging Large Number of EVs Under Normal and Line Failure Scenarios

Electric vehicle (EV) becomes a popular choice for its zero air pollutant and high energy efficiency. Nevertheless, the massive penetration of EVs can cause problems, including voltage drop and peak amplification. The charging pattern of EVs also poses a challenge to the reconfiguration work of the power system when failure occurs. Therefore, an EV schedule-control-based strategy is designed to address these issues in order to achieve voltage regulation and load shifting under both normal operation and failure scenarios. The framework involves two agents: 1) a two-stage voltage control agent schedules EVs to perform load shifting and voltage regulation under normal condition and 2) a fault control agent deals with line failure scenarios to recover the power supply of out-of-service basic and EV loads. A three-level queue table mechanism is designed to collaboratively perform EV scheduling. The influence of EV charging locations on the voltage variations of other nodes is considered and alleviated through a voltage sensitivity analysis method. Moreover, graph theory is employed to perform the network reconfiguration process to deal with line failure situations. The effectiveness of the scheme to restore the power supply while maintaining reliable system voltage level has been verified with the simulation results based on a modified IEEE 30 nodes test feeder.

Modeling and simulating communications of Multiagent Systems in Smart Grid

Multiagent Systems (MAS) are used extensively in Smart Grid research as a means of developing distributed control systems comprised of a network of communicating units. Many previous works have made implicit or explicit assumptions that lead to simplified modeling and simulation of the underlying communication systems. While these assumptions are valid under some cases, they may not hold true when system size and complexity scale up exponentially, which can be anticipated given the rapid development of smart grid technologies. In this paper, we present a more comprehensive modeling and simulation approach that accounts for the MAS related protocols as described in the FIPA (Foundation for Intelligent Physical Agents) specification; together with a co-simulation platform for the investigation of multi-disciplinary interdependencies between MAS, communication, and electrical systems. To show the benefits of comprehensive modeling and simulation, we demonstrate how different network configurations affect system performance in a Fault Location, Isolation and Service Restoration (FLISR) scenario.

Modeling and co-simulation of IEC61850-based microgrid protection

Microgrid protection schemes are different from those used in conventional distribution networks. With the incorporation of distributed generation (DG), and the ability for bi-directional power flow and island operation; the fault current and delay parameters of protective relays need to be recalculated and updated in response to the dynamic operation of microgrid. Performance evaluation (e.g. fault clearance time and recovery time) and parameter estimation (fault current and trip delay) for such a protection scheme have been difficult because they require comprehensive modeling of electrical network, IED behaviors, and IEC61850-based communication. In this paper, we present a centralized scheme with detailed modeling and co-simulation of all these aspects. We also demonstrate how the analysis of the simulation results helps overcoming the forth-mentioned difficulties.

HLA based co-simulation framework for multiagent-based smart grid applications

Multiagent Systems (MAS) are used extensively in Smart Grid research as a mean of distributed control. The integration of power system, communication network and MAS based control system creates complex dynamics difficult to comprehend. In this paper, a co-simulation framework for the investigation of the multi-disciplinary interdependencies between the three systems will be proposed. The proposed design combines the power system simulator PSCAD/EMTDC, network simulator OPNET, and the well-recognized MAS platform JADE. In order to promote extensibility and reusability of simulation modules, as well as to enforce causality constraint, our design is in compliance with the High Level Architecture (HLA, IEEE 1516) standard.

Nonlinear switching control for suppressing the spread of avian influenza

This paper proposes a novel method of killing birds and applying vaccines for suppressing the spread of avian influenza via a nonlinear switching control approach. The switching strategy is based on the population of the susceptible birds and the population of the susceptible humans. There are four switching cases. For the first three switching cases, the elimination control force and the quarantine control force are equal to either zero or one. For the last switching case, they are equal to one minus a scalar divided by the population of the susceptible birds and one minus another scalar divided by the population of the susceptible humans, respectively. The system state vectors of the avian influenza model are guaranteed to reach the desirable equilibrium point. Also, the positivity requirements on the system states as well as the constraints on both the lower bounds and the upper bounds of both the elimination control force and the quarantine control force are guaranteed to be satisfied. Computer numerical simulation results show that the proposed control strategy is very effective and efficient.

An intelligent scatter search (ISS) algorithm for scheduling of charging a single EV

Electric vehicles (EVs) becomes popular for energy saving and environmental protection in light of sustainable development in recent years. The wide spread popularity of EVs relies on an effective strategy for charging the battery. Hence, efficient and robust algorithms are essential for implementing the charging strategies. In this paper, an intelligent scatter search (ISS) framework utilizing filter-SQP (sequential quadratic programming) and mixed-integer SQP techniques as local solvers is proposed for handling EV charging with either flexible or constant charging power under both V2G and G2V support. Simulations have been carried out to verify the effectiveness of the proposed ISS algorithm. The results demonstrated that its performance is better than other approaches including GS (global search), GA (genetic algorithm), PSO (particle swarm optimization), and SS/F (scatter search algorithm with local solver fmincon). In addition, its computational load is rather low in regulating the EV charging and thus is very suitable to minimize the operation cost for EV owners.