Christopher Lund

The marginal cost of reliability improvement (MCRI) is a very useful measure to compare the cost-effectiveness of various standalone microgrid (SMG) systems. This measure helps in decision making on reliability level and imports and exports between SMGs. The MCRI can elucidate how a SMG system is going to deal with the change of reliability requirements by customers and energy traders. This paper proposes an MCRI evaluation algorithm for a microgrid (MG) over its 25-year lifespan. A case study is evaluated, which consists of renewable energy resources (RES) and a battery energy storage system (BESS) as reliability improvement (RI) alternatives. Two sensitivity analysis study are performed to answer the following research questions: What if is the cost of energy resources changes? and What if demand response (DR) is included as an alternative to RI. Furthermore, whether maximum reliability can be achieved with 100% renewable generating resources is also evaluated. The Monte Carlo Simulation (MCS) method is used to model the equipment failure. The linear regression approach is used to create an equation for loss of load reduction (LOLR), for the addition of resource mix as a function of LOLR and for the addition of individual RI alternatives. A Matlab optimization tool is used to find the MCRI.

The clustering of historical electricity consumption data is an effective means of developing representative load profiles for long-term energy planning. This paper presents a multi-dimensional approach for clustering, considering scattering and separation metrics and the number of clusters. A novel hybrid approach to solve the clustering function is also proposed: a combination of Invasive Weed Optimization (IWO) and wavelet mutation strategy. The hybrid method is applied to half-hourly metered electricity consumption data from the Civic Centre of a large local (municipal) government in Perth, Western Australia, to create representative seasonal load profiles. The novel clustering approach is then tested against the well-known k-means method using Davies-Bouldin and silhouette indices. In each seasonal clustered profile, the hybrid method is found to outperform the k-means method. The hybrid method has been identified as an effective clustering approach for analyzing the behavior of loads and assisting the identification of suitable energy efficiency initiatives.

This paper proposes a new reliability evaluation method for renewable energy (RE) rich microgrid (MG) systems using a holistic approach by modelling the availability of renewable resources such as solar irradiation and wind along with the equipment availability during each hour. Where an electric network relies heavily on renewable resources the availability of such resources will significantly affect the reliability of the whole system. In addition, energy storage systems (ESSs) impact the reliability of a microgrid. In this paper, the mean time to failure (MTTF) of a RE rich MG system is evaluated based on the MTTF of equipment for different levels of renewable energy generation and energy storage. The loss of load frequency (LOLF) in each hour due to equipment failure, due to resource unavailbalitly, and the system failure due to combined effects were counted in a simulation study of 25 years.

Sustainable Energy Management is a critical aspect of improving efficiency and sustainability in the built environment and industry. It forms the basis for most of the cost-effective emission reductions for any given entity and improving the interior climate of a large entity. It, therefore, becomes very important to understand the baseline and monitor energy consumption periodically to proactively manage it, and as required address the gaps in improvement. An Energy Information Management System (EIMS) therefore becomes an indispensable tool for this purpose. Based on a review of the literature and practical experience, this paper provides a refreshed insight into the technologies and features that such a system should have. The latest development in the Internet of Things (IoT) technologies has led to the development of stronger capabilities for data acquisition and communication for an optimized and cost-effective energy system. The enhancement in data analytics and modeling capabilities further add to the versatility in offerings and insights that such a system may provide. Therefore, it is important that an EIMS not only provides a strong information delivery mechanism subsequent to data collection but also includes expert knowledge that ensures that the stakeholders of this system receive purposeful and useful knowledge and information, not just data. This study identifies the essential characteristics of a smart EIMS for a large entity which normally includes multiple buildings and complexes with different applications, such as city councils. The features analyzed cover the required hardware and software to collect, analyze and translate energy and exterior data into information at the required level of security to identify gaps easily, predict performance and make quick decisions, resulting in an enhancement of energy efficiency and interior climate.

Optimising voltage levels to a controlled stable level at a facility can not only reduce the cost of energy but also enhance equipment performance, prolong equipment life, reduces maintenance costs and reduce greenhouse gas emissions. Voltage optimisation (VO) technology has been widely used in a number of different industries locally and internationally but not yet to a large extent within the red meat processing sector in Australia to optimise the network voltage levels. Therefore, to determine whether VO can be implemented, and whether it is technically and economically viable for red meat processing sites, this study investigates the effectiveness of VO technology in an Australian abattoir through extensive case studies. The investigation explores the most suitable VO technology considering the implications for the plant and equipment under operation at a typical red meat processing plant by using the published literature and through rigorous analysis based on a case study at the meat processing site in Western Australia. From the case study analysis, it is evident that power quality issues such as under voltage, overvoltage, and harmonic distortion can be reduced as well as significant energy savings can be achieved with the optimum selection of voltage level and VO technology.

This study concentrates on finding a possible method of annealing amorphous silicon solar modules degraded by prolonged exposure to light. The aim of annealing is the recovery of initial efficiency. This should be done on the module's work site or through simple indoor maintenance. Ideally the annealing temperature should be as low as possible and the annealing time as short as possible. The annealing process of laboratory cells and commercially available triple junction solar modules was performed at temperatures 70°C - 110°C The degree of efficiency recovery as a function of temperature and time of exposure to heating was investigated. The influence of factors affecting the rate of degradation and recovery such as short-circuiting, work under load or exposure to light, were also taken into account.

Hydrogenated amorphous silicon solar cells (a-Si:H) with a modified intrinsic layer (in this case a so called "gettering layer") show an interesting pattern of photo-degradation during long exposure to simulated sunlight. This work presents some initial results from a careful examination of the behaviour of minority carrier lifetimes and spectral responses of standard, normal cells and cells with a modified intrinsic layer at all stages throughout the photo-degradation. The observed changes in the spectral responses and minority carrier lifetimes during degradation are remarkably different in the two types of solar cells under investigation. Cells with a modified intrinsic layer seem to be more stable during all stages of prolonged photo-degradation. We suspect that processes involving the formation of light induced defects and their migration inside the intrinsic layer can at least partially explain the differences in changes observed in these samples.

A survey of the needs of the renewable energy industry in Australia for education and training has been carried out. It has identified a strong demand for training at University, technical college and community levels. Several key areas of training such as energy efficiency and energy management need more attention. Respondents indicated a need for initial training courses and ongoing short courses to keep practitioners up to date. There was also strong support for renewable energy education for the community and for schools in order to raise awareness and assist market development.

The rapid expansion of the renewable energy industry is producing a huge demand for welltrained professional people to design, install and maintain new systems. There is also a substantial need for policy analysts, researchers and teachers with experience in renewables. Many professional people are now seeking to move their career paths into the renewable energy field and they require advanced, on-the-job training. It is no longer sufficient to add a few topics or units on renewables to a conventional science or engineering course. Renewable energy education is emerging as a new discipline in its own right, with its own interdisciplinary curriculum that meets the criteria for sustainable development. Educating the community is also vital for creating a greater awareness of, and confidence in, renewable energy systems, and for training people to use them properly. This paper describes some new approaches to renewable energy education that seek to meet the needs of industry and society for high-quality, environmentally friendly and socially responsible energy supply systems.

There is a pressing global need for more trained scientists, engineers, policy makers, technicians and tradespeople with the knowledge and skills to develop, implement and maintain renewable and energy efficient systems and policies. This training requires the development of high quality, flexible renewable energy training courses that can be delivered by both face-to-face and self-paced distance education. The Internet’s unique features, including its ability to display not just static, but dynamic, information to anywhere in the world almost simultaneously means it can be used to significantly enhance the learning experience of students. In the light of this the Australian Cooperative Research Centre for Renewable Energy (ACRE) in conjunction with Murdoch University and the Brisbane Institute of Technical and Further Education (BIT) are developing a number of internationally focussed distance education and Internet based post secondary, and tertiary level renewable energy courses. The individual modules, or units, that make up these courses have been developed for maximum flexibility with the option of delivery face-to-face or through distance education. There are a number of challenges in delivering renewable energy training by conventional correspondence based distance education. These challenges can be reduced, and the quality of learning significantly enhanced by the use of the Internet. Some of the advantages of using the Internet to enhance post secondary renewable energy education will be discussed. A number of examples of how the Internet has been used to enhance ACRE's post secondary and tertiary level online modules and units will be shown. A number of Internet based resources useful for renewable energy training will also be given.