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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]

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors. [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]

The energy transition to wind and solar opens up opportunities for green hydrogen as wind and solar generation tend to bring electricity prices down to very low levels. We evaluate whether green hydrogen can integrate well with wind and solar PVs to improve the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during periods of surplus renewable energy. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used system engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, so at present, it would not be viable without subsidy. [ABSTRACT FROM AUTHOR]

Australia has abundant volumes of forest residues that are a potential feedstock for supplying biomass as a renewable carbon carrier to the market. However, there remains an underutilization of this resource, even in mature bioeconomy markets. Several existing or perceived barriers can be attributed to the underdeveloped, forest-based bioeconomy in Australia. One of these is the limited understanding of feedstock supply costs. In this study, two ranking approaches were applied to identify the optimal biomass feedstock supply chain from field to conversion plant gate. A panel of experts embedded in the Australian bioeconomy were employed to first assign ranks to biomass supply chain items by cost intensity. Then, a layer of analytic hierarchical process (AHP) was used to weigh and rank various biomass supply pathways by efficiency. The results reveal that biomass extraction ranks the highest and biomass feedstock storage ranks the lowest, relative to other supply chain costs. Extracting and chipping material in the field attracted the most support from the experts in terms of efficiency, followed by transporting and chipping at the roadside and, finally, transporting and chipping at the conversion plant. This study provides insights for designers of the forest-based bioeconomy in Australia into relative cost drivers that may be applied to investment and industry decisions. It also provides a framework to support further investigations into forest biomass development and the management of biomass as a renewable carbon carrier at a time when Australia is transitioning from an energy policy focused on fossil fuels to a renewable energy strategy. [ABSTRACT FROM AUTHOR]

The increased uptake of variable renewable energy sources has increased electricity price volatility in many energy pool markets, providing an opportunity for storage systems to profit through energy arbitrage. Comparison between the cost or value of storage systems engaging in energy arbitrage should be performed on a levelised basis due to differences in system lifetime. Existing energy arbitrage models with bid/offer curves and imperfect forecasting are typically computationally expensive and are impractical for calculating lifetime levelised cost metrics. In this work, an open-source modular energy arbitrage model with bid and offer curve inputs was developed for a lithium-ion battery energy storage system (BESS) and pumped hydro system (PHS) to analyse lifetime levelised cost and revenue. The mixed integer linear program scheduling module included a new piece-wise linearised description of PHS charging behaviour for rapid optimisation. A one-at-a-time sensitivity analysis indicated that levelised cost and revenue were highly sensitive to discharging efficiency. In a case study based on Australia's National Electricity Market, imperfect forecasting with no risk hedging was found to increase levelised costs by up to 24% and decrease levelised revenue by up to 50% relative to perfect price forecasting, despite 95% of prices being forecast to be within $35/MWh of the actual trading price. BESS levelised costs were more significantly correlated with consistent low risk bids (Kendall Tau-b of 0.75), since the undiscounted capital costs contribute to a larger proportion of the overall costs than in the PHS systems. [ABSTRACT FROM AUTHOR]

The integration of a CSP tower system with a PV solar field, sharing a thermal energy storage, is modeled and discussed. The tower system uses a new-design solid particle fluidized bed receiver integrated with a thermal storage, where hot particles are directly collected to store daily energy for overnight production of electricity. The PV solar field is aimed to supply the daily energy demand; when there is a surplus of PV energy production, the electric energy is converted to heat and accumulated in the thermal storage too. The integration of the two energy systems is modeled, building efficiency functions for all the sub-components of the integrated plant (heliostat field, receiver, storage, power block, PV field). Yearly simulations are performed for two different locations, Spain and Australia, obtaining that a system with a peak power of 10 MWe CSP + 15 MWe PV can supply—with a limited curtailment—a fraction of more than 60% (respectively, 62% and 68%) of a realistic electric load with a peak demand around 10 MW, to be compared with the 45/47% of the same load obtained adopting a PV-only system with the same overall peak power. In the integrated system, PV directly supplies 40/41% of the load, the remaining 23/28% being produced by the power block (mainly fed by the CSP). [ABSTRACT FROM AUTHOR]

The rapid growth of the energy and transport sectors has led to an increase in fuel consumption, resulting in a significant rise in greenhouse gas emissions. Switching to renewable energy sources and replacing internal combustion engines with electric vehicles (EVs) can significantly reduce greenhouse gas emissions. In recent years, the electrification of the transportation sector has become a primary focus of research and development efforts. However, if EVs are charged using conventional energy sources, we are unable to fully capitalize on their potential to reduce emissions. Charging EVs using renewable energy sources is the optimal solution. Otherwise, the increased number of EVs on the roads can significantly impact the stability of existing electric grids. As a result, smart homes with EV charging stations are becoming increasingly popular worldwide. This review focuses on the concept of grid-connected rooftop solar photovoltaic (PV) smart homes integrated with EVs and energy management systems in Australia. Australia can reduce emissions in the building and transport sectors by electrifying a range of vehicles and ultimately powering them with 100% renewable energy sources. The benefits of EV integration alongside rooftop solar systems for smart homes with house-to-vehicle or vehicle-to-house, as well as vehicle-to-grid or grid-to-vehicle (bidirectional EV charging) capabilities are also explored in this article. By adopting these systems, these smart homes can provide energy schemes for commercial use, ultimately contributing to the owner's economic benefit. [ABSTRACT FROM AUTHOR]

Australia has committed to reducing its greenhouse gas emissions in a manner consistent with limiting anthropogenic climate change to no more than 2 degrees Celsius. One of the ways in which this commitment is being realised is through a shift towards variable renewable energy (VRE) within Australia's National Electricity Market (NEM). Substituting existing dispatchable thermal plant with VRE requires consideration of long-term energy resource adequacy given the unpredictability of solar and wind resources. While pumped hydro and battery storage are key technologies for addressing short-term mismatches between resource availability and demand, they may be unable to cost effectively address 'energy droughts'. In this article, we present a time sequential solver model of the NEM and an optimal firming technology plant mix to allow the system to be supplied by 100% VRE. Our conclusion is that some form of fuel-based technology (most likely hydrogen) will probably be required. This has important implications for Australian energy policy. [ABSTRACT FROM AUTHOR]

The national electricity market (NEM) of Australia is reforming via the rapid uptake of variable renewable energy (VRE) integration concurrent with the retirement of conventional synchronous generation. System strength has emerged as a prominent challenge and constraint to power system stability and ongoing grid connection of VRE such as solar and wind. In order to facilitate decarbonization pathways, Australia is the first country to evolve system strength and inertia frameworks and assessment methods to accommodate energy transition barriers, and other parts of the world are now beginning to follow the same approach. With the evolvement of the system strength framework as a new trending strategy to break the transition barriers raised by renewable energy project development and grid connection studies, this paper provides a high-level overview of system strength, covering such fundamental principles as its definition, attributes, and manifestations, as well as industry commentary, cutting-edge technologies and works currently underway for the delivery of a secure and reliable electricity system with the rapid integration of inverter-based resources (IBRs) in the NEM grid. The intent of this study is to provide a comprehensive reference on the engineering practices of the system strength challenge along with complementary technical, regulatory, and industry perspectives. [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]

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]

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]

We establish a framework to examine the feasibility of using local vegetation for bioenergy power systems in small-scale applications and remote settings. The framework has broad application, and we present a specific case here to demonstrate the process. Our case study is the Tiwi Islands in northern Australia, where a large Acacia mangium plantation is a potential source of biofuel feedstock. Two types of technology were considered: 1. Bio-oil from pyrolysis in diesel generators and 2. Direct combustion coupled with a steam turbine. The biomass was characterized and found to have adequate properties for an energy crop, with a lower heating value of about 18 MJ/kg and entire tree ash content of 2%. Measurements from trees that were damaged from wildfires had similar results, showing potential value recovery for a plantation after unplanned fire. In comparison to a petroleum diesel-based generator, the bio-oil system was 12% more expensive. The direct combustion system was found to be the most economical of those explored here, costing as low as 61% of the bio-oil system. Additional social and environmental benefits were identified, including local employment opportunities, improved energy security and reduced greenhouse gas emissions. Our findings of high techno-economic potential of bioenergy systems, especially through direct combustion, are widely applicable to on-demand renewable energy supply in remote communities. [ABSTRACT FROM AUTHOR]

It can be expected that the power systems of the future will be significantly different from today’s, especially due to increasing renewable energy sources (RESs), storage systems, and price-responsive users leading to large uncertainty and complexity. The operation of these future grids (FGs) at levels of renewable energy approaching 100% will require all the usual stability analysis along with new issues in a much more complex situation than has been encountered in the past. In fact, how close we can get to this desired level will likely be dependent on the assessed stability limits. Therefore, in this study, we use a novel scenario-sensitivity-contingency based framework to evaluate the system stability along possible evolution pathways towards high renewable FGs. As a case study, we carry out our studies based on proposed future scenarios and sensitivities for the Australian FG. Using a simulation platform that encompasses market simulation, load flow calculation and stability assessment altogether, the impact of grid strength, level of prosumers, and utility storage on the stability of the FG is studied and quantified indices for long term stability have been devised. The results of this study enable us to address the underlying stability issues of the FGs. [ABSTRACT FROM AUTHOR]

We report the upper and lower bounds for the levelized cost of high-temperature industrial process heat, supplied from electricity generated with solar-photovoltaic (PV) and wind turbines in combination with either thermal or electric battery storage using hourly typical meteorological year (TMY) data, in systems sized to supply between 80% and 100% of continuous thermal demand at a site in the northern part of Western Australia. The system is chosen to supply high-temperature air as the heat transfer media at temperatures of 1000 °C, which is a typical temperature for an alumina or a lime calcination plant. A simplified model of the electrical energy plant has been developed using performance characteristics of real PV and wind systems and TMY data of renewable energy resources. This was used to simulate a large sample of possible system configurations and find the optimal combination of the renewable resources and storage systems, sized to provide renewable shares (RES) of between 80% and 100% of the yearly demand. This allowed the upper and lower bounds to be determined for the cost of heat based on two scenarios in which the excess energy is either dumped (upper bound) or exported to the electricity grid (lower bound) at the average generating cost. The lower bound of the levelized cost of energy ( L C O E L ), which occurs for the system employing thermal storage, was estimated to range from USD 10/GJ to USD 24/GJ for RES from 80 to 100%. The corresponding upper bound (L C O E U ), also estimated for the system using thermal storage, are between USD 16/GJ and USD 31/GJ, for RES between 80% and 100%. The utilization of electric battery storage instead of thermal storage was found to increase the LCOE values by a factor of two to four depending on the share of renewable energy. Compared with current Australian natural gas cost, none of the systems assessed configurations is economical without either a cost for CO2 emissions or a premium for low-carbon products. The estimated cost for CO2 emission that is needed to reach parity with current natural gas prices in Australia is also presented. [ABSTRACT FROM AUTHOR]

Harnessing sustainably sourced forest biomass for renewable energy is well-established in some parts of the developed world. Forest-based bioenergy has the potential to offset carbon dioxide emissions from fossil fuels, thereby playing a role in climate change mitigation. Despite having an established commercial forestry industry, with large quantities of residue generated each year, there is limited use for forest biomass for renewable energy in Queensland, and Australia more broadly. The objective of this study was to identify the carbon dioxide mitigation potential of replacing fossil fuels with bioenergy generated from forest harvest residues harnessed from commercial plantations of Pinus species in southeast Queensland. An empirical-based full carbon accounting model (FullCAM) was used to simulate the accumulation of carbon in harvest residues. The results from the FullCAM modelling were further analysed to identify the energy substitution and greenhouse gas (GHG) emissions offsets of three bioenergy scenarios. The results of the analysis suggest that the greatest opportunity to avoid or offset emissions is achieved when combined heat and power using residue feedstocks replaces coal-fired electricity. The results of this study suggest that forest residue bioenergy is a viable alternative to traditional energy sources, offering substantive emission reductions, with the potential to contribute towards renewable energy and emission reduction targets in Queensland. The approach used in this case study will be valuable to other regions exploring bioenergy generation from forest or other biomass residues. [ABSTRACT FROM AUTHOR]