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Previously published studies on Australia's energy security did not examine the country's domestic energy security situation in tandem with its international energy statecraft. This paper fills this research gap. In order to provide a robust analysis of a country's strategic options in the energy sector, it is paramount to balance domestic and international dimensions, along with internal and external factors. The paper utilizes the strengths, weaknesses, opportunities, and threats (SWOT) method to review Australia's strategic options by balancing the inward-looking, domestic risk minimization dimension (energy security), with the outward-looking, international power projection dimension (energy statecraft). The paper also applies the 4A framework (availability, accessibility, affordability, and acceptance) to assess Australia's energy security performance. On the one hand, the results demonstrate that Australia has many strengths and opportunities as a reliable and stable energy supplier, endowed with traditional and renewable energy resources, and critical minerals. On the other hand, numerous internal weaknesses and external threats may affect Australia's strategic options in the future. The most pressing issue is the historical lack of strategic government intervention in the energy market, which has, paradoxically, resulted in domestic energy accessibility and affordability crisis. The market-based approach is also the main reason why Australia has not transformed its energy resources into capabilities to be used as instruments of statecraft. The paper uses the SWOT analysis and the 4A assessment as the basis for discussion on how Australia can transform its energy sector weaknesses and threats into strengths and opportunities, to benefit the national interest. [ABSTRACT FROM AUTHOR]

A significant contribution to the reduction of carbon emissions will be enabled through the transition from a centralised fossil fuel system to a decentralised, renewable electricity system. However, due to the intermittent nature of renewable energy, storage is required to provide a suitable response to dynamic loads and manage the excess generated electricity with utilisation during periods of low generation. This paper investigates the use of stationary hydrogen-based energy storage systems for microgrids and distributed energy resource systems. An exploratory study was conducted in Australia based on a mixed methodology. Ten Australian industry experts were interviewed to determine use cases for hydrogen-based energy storage systems' requirements, barriers, methods, and recommendations. This study suggests that the current cost of the electrolyser, fuel cell, and storage medium, and the current low round-trip efficiency, are the main elements inhibiting hydrogen-based energy storage systems. Limited industry and practical experience are barriers to the implementation of hydrogen storage systems. Government support could help scale hydrogen-based energy storage systems among early adopters and enablers. Furthermore, collaboration and knowledge sharing could reduce risks, allowing the involvement of more stakeholders. Competition and innovation could ultimately reduce the costs, increasing the uptake of hydrogen storage systems. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

Due to the variability and partial unpredictability of renewable energy sources (RES), generation reserve sizing is key for managing operational risks and enhancing grid reliability. Most existing approaches for reserve sizing estimate the forecasting error for RES using either simple point forecasts, or probabilistic forecasts that assume a predefined parametric model of the underlying distribution, such as a Gaussian distribution. However, these approaches are unable to accurately model the probability of critical events that occur in the tails of the distributions, which is vital for operational risk management. Further, they do not account for multiple dynamic factors that may impact operational risk. In this paper, we introduce a data-driven dynamic probabilistic reserve sizing method based on the artificial intelligence technique of dynamic Bayesian belief networks. The method considers the actual underlying distribution of forecasting errors from the availability of conventional generators (forecasting errors of their available generation capacity), demand, RES, and prevailing conditions such as weather and market prices. In addition, we introduce a new dynamic metric for calculating the reliability level of the power grid in order to provide a real-time stochastic decision support tool for power system operators. The proposed method is demonstrated using seven years of real historical data with a granularity of 30 min from the power system in Australia, and has been implemented and is currently used by the Australian system operator. [ABSTRACT FROM AUTHOR]

Proponents of low-carbon transformation face an uphill battle to reconfigure incumbent energy systems against powerful interests protecting the status quo. Australia's electricity sector is an example of such a system, wedded to fossil fuels and backed by much of the country's political establishment. This paper addresses the potential for low-carbon transformation in Australia's electricity sector. It explores the complex and uncertain dynamics shaping the sector and outlines how these can be understood through the lens of strategic agency – with an eye for opportunities to drive a shift to renewables. It reviews the dynamics of change and re-stability shaping Australia's electricity sector between 2006 and 2015 and applies a resource-based view of transformative agency to analyse these dynamics. Results show the sector suffers multiple stressors and emphasise disruptive changes ‘in the pipeline’. Critically, many of these transformative dynamics stem from factors outside the influence of the electricity sector and its supporters. While results do not point to a clear trajectory or outcome of transformation, they indicate the uptake of small-scale solar photovoltaic (PV) systems by households and small business will play a defining role in the sector's future configuration. Furthermore, the exploration of transformative dynamics affecting the electricity sector through a resource-based lens shows that many opportunities exist for strategic agents to intervene in support of a disruptive shift to renewables. The paper suggests a range of mechanisms that agents could use to undermine the strength of fossil energy in the electricity sector and encourage a shift to renewables. [ABSTRACT FROM AUTHOR]

A Net Zero Energy Building (NZEB) is a term, subject to ambiguity, that could be used to describe a building with characteristics such as equal energy generation to usage, significantly reduced energy demands, energy costs equalling zero or net zero greenhouse gas (GHG) emissions. Despite lacking an authoritative definition of NZEBs, this relatively new emerging concept in Australia provides significant opportunities to reduce GHG emissions, energy usage and operational energy costs for buildings owners. This paper aims to explore the existing NZEB models, assess the progression of NZEB literature, identify key policies encouraging NZEB development and recognise potential areas of NZEB research. [ABSTRACT FROM AUTHOR]

This paper investigates the least-cost greenhouse gas (GHG) abatement pathway for a coupled energy system. We consider the roles of diverse abatement technologies, such as renewable electricity generation, biomass and carbon capture and storage (CCS), as well as sources of operational flexibility, such as peaking generators, electricity storage, transmission and electrolysis. Scenario and sensitivity analyses are applied to explore plausible pathways to energy system decarbonization. All modelled pathways to a zero-emissions energy system feature an accelerating transition from fossil fuels to renewable energy with diverse technologies playing a role. This includes variable renewable generators (VRG), CCS, and sources of operational flexibility on different timescales. Specifically, synchronous condensers appear to be a cost-effective option to control system frequency, inter-seasonal hydrogen storage in engineered caverns can play a significant and complementary role to that of pumped hydro and battery storage, and technologies that capture and sequester atmospheric carbon, including biomass-to-CCS and direct air capture (DAC), can also play a significant role in the transition to net-zero emissions. [ABSTRACT FROM AUTHOR]

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]

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]

This paper proposes a rule-based power management algorithm to enable the dispatch of a utility-scale PV power plant consisting of a hybrid energy storage system, in accordance with the Australian national electricity rules. This algorithm is purposely designed to regulate the instantaneous power of a PV plant with the same level of dispatchability as conventional power plants. Additionally, the proposed algorithm is robust under large forecasting errors (up to 60%) of solar irradiance and it can be easily implemented in practice. Specifically, this algorithm can be executed within seconds even when considering detailed market operation and model nonlinearities. A 30 MW PV plant, including a 7.5 MW/1.25 MWh vanadium redox battery and a 1.5 MW/0.25 MWh suppercapacitors bank, has been modeled using MATLAB/Simulink and PLECS software environment. The simulation results were based on various scenarios and confirmed the effectiveness of the proposed power management algorithm. It has been shown that even in the worst-case scenario of solar forecasting error of 60%, the proposed algorithm still managed to deliver over 94% of the available energy for a given solar irradiance profile of one month. [ABSTRACT FROM AUTHOR]

To be sustainable in energy usage in the future, there are two aspects that need to be considered: the energy supply or generation and the consumption side, including the closely linked construction and building industries which consume a large amount of energy. Essential requirements for energy efficiency are to produce less greenhouse gas emissions and to rely more on renewable energy sources for future sustainability. Policies for mitigation of the environment impact are having effects on both the supply and demand. While the former requires more alternate sources in smart grids and improved technologies for carbon capture and storage, the latter involves the reduction of energy wastes and greenhouse gas (GHG) emissions as prerequisites to green certification within the construction and building sector. Thus, access to sustainable, affordable, and secure energy is one of the major global strategic priorities to maintain and improve public health, sustain economic growth, and mitigate the effects of climate change. Toward this goal, many countries, including Australia, are investing in clean, efficient, reliable energy systems for a prosperous and environmentally sustainable future. Hence, exploring various options to ensure energy security by diversification of energy sources is an important step in meeting the future requirements and delivering clean energy to different industry sectors. This paper discusses options to manage the use of energy sources in the power generation and construction industries. Options for mitigation of environmental impact and for achievement of sustainable energy usage, such as building design with BIM, are discussed. [ABSTRACT FROM AUTHOR]

This paper examines the cost-effectiveness of subsidies (feed-in tariffs and renewable energy credits) paid for by electricity consumers to support the uptake of roof top photovoltaic (PV) systems by households in Australia. We estimate annual payback periods, and then regress these against the actual uptake of household PV and associated emission reductions, creating a relationship not apparent in other research. Sensitivity analysis reveals that the declining cost of PV panels had most impact on PV uptake followed by feed-in tariffs, renewable energy credits and the increasing cost of household electricity tariffs. Our modelling shows that feed-in tariffs were higher than necessary to achieve the resultant levels of PV uptake and that the low cost of PV panels and comparatively high electricity tariffs are likely to result in a continuing strong uptake of household PV in Australia. Our modelling shows that subsidies peaked in 2011 and 2012, with payback periods of three to four years, having since increased to five to six years. Emission reduction costs are expected to reduce from over AU$200 per t CO 2 e in 2013 to between AU$65 and AU$100 per t CO 2 e in 2020. Household PV reduced Australia’s emissions by 3.7 million t CO 2 e in 2013 (1.7% of Australia’s total emissions) and is expected to reach eight million tonnes (3.7% of Australia’s total emissions) by 2020. [ABSTRACT FROM AUTHOR]

Abstract: Coal fired electricity is a major factor in Australia’s greenhouse gas emissions (GHG) emissions. The country has adopted a mandatory renewable energy target (MRET) to ensure that 20% of electricity comes from renewable sources by 2020. In order to support the MRET, a market scheme of tradable Renewable Energy Certificates (RECs) has been implemented since 2001. Generators using biomass from eligible sources are able to contribute to GHG emission reduction through the substitution of coal for electricity production and are eligible to create and trade RECs. This paper quantifies the potential biomass resources available for energy generation from forestry and agriculture in the Green Triangle, one of the most promising Australian Regions for biomass production. We analyse the cost of electricity generation using direct firing of biomass, and estimate the required REC prices to make it competitive with coal fired electricity generation. Major findings suggest that more than 2.6 million tonnes of biomass are produced every year within 200 km of the regional hub of Mount Gambier and biomass fired electricity is viable using feedstock with a plant gate cost of 46 Australian Dollars (AUD) per tonne under the current REC price of 34 AUD per MWh. These findings are then discussed in the context of regional energy security and existing targets and incentives for renewable energies. [Copyright &y& Elsevier]

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO. [ABSTRACT FROM AUTHOR]

One of the central concerns of sociologists of science has been the significance attached to received scientific collectivities such as the 'discipline 'and 'specialty' This has ranged from implicit acceptance of their centrality in scientific practice, to a rejection based on the identification of such collectivities as constructs of minimal significance to scientific work. This paper investigates the significance of collectivities in respect to solar energy research in Australia. On the basis of this study it is argued that the significance of received collectivities has been exaggerated; they do influence the way the researcher's world is perceived and organized, but this contribution is made within a network of multiple, overlapping and often -- at the margins -vaguely defined collectivities. [ABSTRACT FROM AUTHOR]

Despite the global context, only 6% of Australia's total energy consumption was derived from renewables, while 86.3% of electricity was generated from fossil fuels. However, this trend has been disrupted by the recent decommissioning and closure of a fleet of ageing coal power plants, along with the country's international commitments to reduce fossil fuel emissions. Currently, Australia is at a crucial stage of its transition to renewable energy adoption. With the purpose of providing an updated depiction of renewable energy, related policy and the way ahead in the Australian context, this research paper presents a systematic review of contemporary literature from the perspectives of energy potential, utilisation and policy incentives. The research analysed a total of 118 documents using an interpretivist epistemological lens; emergent findings offer an evidence-based, lucid account of advancements in the renewable energy market. Polynomial regression analysis is also conducted on secondary data obtained from the literature to predict trends in the electricity generated by different sources of renewable energy. The work also identifies several areas that require attention, including: (1) the most recent time-varying feed-in tariffs (TV FiTs) which offer the innate potential to alter consumer attitudes toward power usage at peak times, thereby improving grid security; (2) the increasingly popular integration of waste-to-energy with other sources of renewable energy emerges as a viable complementary solution to meet energy demands; (3) the increasing utilisation of subsidised geothermal energy installations which are set to rise exponentially in the future; and (4) the use of Public Private Partnerships (PPP) for developing renewable energy generation infrastructure and how this investment may facilitate the national transition to renewable energy adoption. This research contributes to the existing body of knowledge by raising awareness of the current state of renewable energy in Australia, along with proposing pragmatic recommendations for overcoming any challenges posed. As a comprehensive reference, this study provides practitioners and policymakers with thorough, reliable and collated information on Australia's current renewable energy position. [ABSTRACT FROM AUTHOR]

• The proposed method can solve voltage issues and enable higher PV penetration. • PV imbalance makes traditional methods less effective on overvoltage mitigation. • A joint centralized-decentralized method is developed for unbalanced PV scenarios. • The new approach controls fast PV fluctuations in a coordinated and distributed way. Due to random connection of small-size single-phase rooftop photovoltaic (PV) generators in residential areas, low voltage distribution systems tend to have unbalanced PV integration across phases. In such situations, traditional reactive power compensation methods may cause unintended Voltage-Reactive Power responses across phases due to line coupling, which consequently affects their effectiveness on overvoltage mitigation. Such overvoltage problems significantly hinder the future integration of clean and renewable solar energy resources in power networks. In this paper, the limitations of traditional methods in PV imbalance scenarios are revealed and comprehensively analyzed by a voltage sensitivity method for the first time. On this basis, an innovative joint centralized-decentralized method is developed to overcome the PV imbalance-induced voltage regulation challenge. Specifically, the inverter's reactive power response to PV power variations in different phases is adaptively scheduled in order to avoid adverse effect of inter-phase Voltage-Reactive Power interaction on voltage regulation. The effectiveness of the proposed method is validated by time-series simulations with recorded data and a real-life low voltage distribution system in Australia. With the traditional method (i.e. power factor droop control strategy), 93 minutes' overvoltage can be observed in the case study, which will trigger overvoltage protection and disconnect PV inverters from the grid. While, the proposed method can always control the system voltage within the allowable range, and it consequently avoids PV disconnection. By solving this bottleneck issue, more PV systems can be integrated into distribution networks, which consequently contributes to CO 2 emission reduction in future. [ABSTRACT FROM AUTHOR]