A Generalized hierarchical transactive energy management framework for residential microgrids

This work develops a transactive energy management system in order to automate the operation and efficiently utilize the energy generated from the solar PV unit and BESS in a single house as well as in the microgrid and provides cost-benefit analysis.

Adaptive intelligent systems for energy management of vehicles

The thesis demonstrated the architecture of adaptive intelligent systems for energy management that is capable of interacting with complex systems including the vehicle, environment, and driver components, as well as the interrelationships between these variables, to deliver fuel consumption improvements.

Coordinated energy management of vehicle air conditioning system

Whilst air conditioning systems increase thermal comfortableness in vehicles, they also raise the energy consumption of vehicles. Achieving thermal comfort in an energy-efficient way is a difficult task requiring good coordination between engine and the air conditioning system. This paper presents a coordinated energy management system to reduce the energy consumption of the vehicle air conditioning system while maintaining the thermal comfortableness. The system coordinates and manages the operation of evaporator, blower, and fresh air and recirculation gates to provide the desired comfort temperature and indoor air quality, under the various ambient and vehicle conditions, the energy consumption can then be optimized. Three simulations of the developed coordinated energy management system are performed to demonstrate its energy saving capacity.

Adaptive cruise control look-ahead system for energy management of vehicles

Cruise control in motor vehicles enhances safe and efficient driving by maintaining a constant speed at a preset level. Adaptive Cruise Control (ACC) is the latest development in cruise control. It controls engine throttle position and braking to maintain a safe distance behind a vehicle in front by responding to the speed of this vehicle, thus providing a safer and more relaxing driving environment. ACC can be further developed by including the look-ahead method of predicting environmental factors such as wind speed and road slope. The conventional analytical control methods for adaptive cruise control can generate good results; however they are difficult to design and computationally expensive. In order to achieve a robust, less computationally expensive, and at the same time more natural human-like speed control, intelligent control techniques can be used. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on ACC systems that reduces the energy consumption of the vehicle and improves its efficiency. The Adaptive Cruise Control Look-Ahead (ACC-LA) system works as follows: It calculates the energy consumption of the vehicle under combined dynamic loads like wind drag, slope, kinetic energy and rolling friction using road data, and it includes a look-ahead strategy to predict the future road slope. The cruise control system adaptively controls the vehicle speed based on the preset speed and the predicted future slope information. By using the ANFIS method, the ACC-LA is made adaptive under different road conditions (slope angle and wind direction and speed). The vehicle was tested using the adaptive cruise control look-ahead energy management system, the results compared with the vehicle running the same test but without the adaptive cruise control look-ahead energy management system. The evaluation outcome indicates that the vehicle speed was efficiently controlled through the look-ahead methodology based upon the driving cycle, and that the average fuel consumption was reduced by 3%.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Stochastic models of road geometry and wind condition for vehicle energy management and control

Modeling and simulation is commonly used to improve vehicle performance, to optimize vehicle system design, and to reduce vehicle development time. Vehicle performances can be affected by environmental conditions and driver behavior factors, which are often uncertain and immeasurable. To incorporate the role of environmental conditions in the modeling and simulation of vehicle systems, both real and artificial data are used. Often, real data are unavailable or inadequate for extensive investigations. Hence, it is important to be able to construct artificial environmental data whose characteristics resemble those of the real data for modeling and simulation purposes. However, to produce credible vehicle simulation results, the simulated environment must be realistic and validated using accepted practices. This paper proposes a stochastic model that is capable of creating artificial environmental factors such as road geometry and wind conditions. In addition, road geometric design principles are employed to modify the created road data, making it consistent with the real-road geometry. Two sets of real-road geometry and wind condition data are employed to propose probability models. To justify the distribution goodness of fit, Pearson's chi-square and correlation statistics have been used. Finally, the stochastic models of road geometry and wind conditions (SMRWs) are developed to produce realistic road and wind data. SMRW can be used to predict vehicle performance, energy management, and control strategies over multiple driving cycles and to assist in developing fuel-efficient vehicles.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

Analysis of different scenarios for residential energy management under existing retail market structure

This paper presents an analysis of a residential energy management scheme under existing retail market structure with the integration of solar photovoltaic (PV) and battery energy storage systems. In this paper, different scenarios are analyzed with an aim of achieving the most cost-effective solutions for the integration of solar PV and battery energy storage systems. The main idea behind these analyses is to obtain a grid independent residential energy management system through the reduction of purchasing energy from the existing power grid. The results from the analysis of different scenarios for a typical Australian house demonstrate that the shortage of energy during the high loads and excess of energy during the higher output of solar PV system can be reduced with the design of a proper energy management scheme employing a transactive energy management framework.

GHIRS: integration of hotel management systems by web services

Nowadays web services technology is widely used to integrate heterogeneous systems and develop new applications. In this paper, an application of integration of hotel management systems by web services technology is presented. The Group Hotel Integration Reservation System (GHIRS) integrates lots of systems of hotel industry such as Front Office system, Property Management system, Enterprise Information System (EIS), Enterprise Information Portal system (EIP), Customer Relationship Management system (CRM) and Supply Chain Management system (SCM) together. This integration solution can add or expand hotel software system in any size of hotel chains environment.

Adoption and maintenance of environmental management systems: critical success factors

The implementation of various management systems and standards has become an important activity for organisations irrespective of their size, sector or nature of the business. Even with a history of implementing change across the organisation, managers continue to experience resistance and challenges when implementing and maintaining the systems/standards, whether they be in quality, occupational health and safety, environmental or any other area. Based on a major research project recently completed, this paper presents the critical success factors for successful implementation of an environmental management system. The project involved three phases of fieldwork: preliminary interviews, a questionnaire survey and in-depth interviews. A brief summary of the findings from each of these phases of research is presented. Based on the findings, the paper discusses the critical success factors for the successful implementation and maintenance of EMS.

CH2 energy harvesting systems : economic use and efficiency

This paper looks at the City of Melbourne's new office development CH₂ as a case study of world class energy performance. In particular, the integrated design of conventionally independent systems has led to the potential to deliver significant savings to the Council and to deliver better environmental conditions to building occupants that in turn may contribute to satisfaction, well-being and Productivity. It is concluded that this project has the potential
to be an iconic example of effective implementation of ESD (environmental sustainable design) principles and therefore act as a demonstration project to others. Energy efficiency of more than 50% of current benchmarks for Melbourne is effected. Energy harvesting is defined as arising from squander, waste and nature, which is a new concept introduced in this paper to better describe the design decision process.

Environmental management systems auditing:Auditors' experiences in Australia

The last decade of the 20th century witnessed rapid growth in the adoption of various environmental management systems (EMS) within organisations. Environmental consultants and auditors play an intricate role in the  interpretation, implementation and certification of an EMS. This paper presents the experiences of eight leading auditors with respect to the adoption of EMS/ISO 14001 by Australian organisations. The benefits and issues relating to the auditing process are discussed.

Sustainable knowledge management systems: integration, personalisation and contextualisation

Many knowledge management (KM) systems have proven unsustainable to date, exhibiting low quantities and quality of knowledge, with systems falling into disuse. In this paper, we provide and explore a model for sustainable KM systems, focusing on the advantages to be gained from integrating knowledge work with everyday work practices, and enabling sensemaking through personalisation and contextualisation. We employ a discourse analysis of email as an exemplar of a sustainable KM system, thereby identifying a number of key characteristics for sustainable KM systems. Our model for sustainable KM systems adds to existing KM theory and, more immediately, assists companies by providing an understanding
of the kinds of characteristics likely to make KM systems more effective, and sustainable in the long term.