Search

Global concerns around climate change and the volatility of conventional fuel prices have prompted researchers and technologists to make significant efforts to identify and exploit alternative energy sources that are cleaner and more sustainable. Wind energy has seen considerable development among these alternative energy sources, mainly due to its abundance and global availability for extraction and the existing knowledge within the aviation and aerospace fields. Many nations, including European countries, already operate offshore wind farms (OWFs) and are progressively carrying out new projects and expanding on other projects. The Australian offshore environment provides unique opportunities for wind energy extraction, particularly along the southern coast of mainland Australia and the regions around Tasmania, where substantially strong winds blow most of the year. A significant challenge to establishing wind farms is the selection of site locations with optimal outputs. This can become a complex decision-making problem if there are numerous options and no information from previous projects. This paper aims to develop a decision-making framework to select the optimal location for installing OWFs while addressing financial, performance-related, and availability-related objectives. This paper adopts a game-theoretical approach to develop a decision-support tool to account for the interdependencies of influencing factors and possible conflicts amongst the parties. The game model is applied to an OWF development case study in the Bass Strait, known for its dominant and strong winds. • A framework to find the optimal location for installing Offshore Wind Farms (OWFs). • Game theory decision-making methodology for offshore renewable energy applications. • Define the influencing factors and conflicts amongst the interested parties. • Apply to OWFs development in Bass Strait, known for its dominant and strong winds. [ABSTRACT FROM AUTHOR]

Abstract: Narratives and discourses are central to how we interpret and understand the world. The capacity to construct and disseminate compelling stories about particular issues is hence critical to an agent''s capacity to advance their interests. This paper examines some of the main narratives through which the conversation about renewable energy in Australia takes place. We label these narratives feasibility (‘Pie in the Sky’); security (‘Keeping the Lights on’); cost (‘Costing the Earth’); and employment (‘Jobs Carnage’). Some of the most effective narratives are those that are constructed around some of the ‘legitimate’ concerns that pertain to an issue. The renewable energy narratives identified in this paper build on ‘reasonableness’ and ‘common sense’ concerns, and their effectiveness is determined as much by contextualising information absent from the energy conversation as present. While our focus is Australia, some of the narrative dynamics revealed have application to energy politics more broadly. [Copyright &y& Elsevier]

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to the retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users. The performance of the IRMAC is studied and compared with various state-of-the-art methods. The proposed method reached an accuracy of 96% in identifying rooftop PV users and 94.4% in identifying electric heating users, which is comparable to the best solution based on deep learning, while the speed of the inference model is a thousand times faster. Last but not least, the proposed method is a transparent algorithm, which can tackle the concerns regarding the agnostic decision-making process when policies prohibit some machine learning methods. [ABSTRACT FROM AUTHOR]

This paper provides a critical study of current Australian and leading international policies aimed at supporting electrical energy storage for stationary power applications with a focus on battery and hydrogen storage technologies. It demonstrates that global leaders such as Germany and the U.S. are actively taking steps to support energy storage technologies through policy and regulatory change. This is principally to integrate increasing amounts of intermittent renewable energy (wind and solar) that will be required to meet high renewable energy targets. The relevance of this to the Australian energy market is that whilst it is unique, it does have aspects in common with the energy markets of these global leaders. This includes regions of high concentrations of intermittent renewable energy (Texas and California) and high penetration rates of residential solar photovoltaics (PV) (Germany). Therefore, Australian policy makers have a good opportunity to observe what is working in an international context to support energy storage. These learnings can then be used to help shape future policy directions and guide Australia along the path to a sustainable energy future. [ABSTRACT FROM AUTHOR]

Despite considerable mitigation efforts, global emissions from the electricity sector continued to grow in recent years. In Australia, the electricity sector is the largest CO 2 -emitting industry, contributing 35% of the country's total greenhouse gas emissions. The Australian government targets an 80% reduction of greenhouse gas emissions by 2050 relative to 2010. With a large variety and quantity of renewable energy resources, it is technically feasible and seems indispensable that Australia's electricity sector be largely decarbonised by 2050 in order to achieve this target. In this paper, scenario-based hybrid Life-Cycle Assessment is applied to calculate the economy-wide carbon footprints of seven electricity generation technologies in scenarios with differing renewable electricity penetration. This work is the first to apply a full life-cycle approach to scenario analysis of electricity generation in Australia. The findings are at the higher end of previously reported carbon footprint intensity ranges and above median values. However, even when taking into account indirect emissions along the technologies' life-cycles, the results indicate that the employment of different renewable energy technologies can potentially save a considerable fraction of Australia's greenhouse gas emissions. This makes renewables an essential option for climate change mitigation. [ABSTRACT FROM AUTHOR]