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Hydrogen is a potential future solution addressing global warming, climate change, and energy security challenges. It efficiently allows for storing excess renewable energy and generating heat and electricity without increasing greenhouse gas emissions. It can create a practical pathway to replace fossil fuel vehicles and move toward a net-zero transport sector. Various hydrogen strategies and industry roadmaps have been developed to identify key goals, set targets for progress, and develop strategic plans to remove barriers. As identified through these documents, the development of consistent national and international codes and standards and the creation of clear procedures and regulations are essential for the safe implementation and adaptation of hydrogen systems, leading to the successful process of hydrogen sector. This paper provides a comprehensive and critical review of the different aspects of hydrogen safety to advance hydrogen regulations, codes, and standards. While it summarises the key international hydrogen frameworks, the main focus of the paper is on Australian plans, progress, and working groups for hydrogen deployment that will help future green energy society. • The safety developments affect the cost of Australian hydrogen scaling targets. • Training and safety developments are essential to achieve public trust in Australia. • Better community engagement is needed to move from hydrogen planning to its development. • Data-based tools assist the development of hydrogen safety procedures in Australia. [ABSTRACT FROM AUTHOR]

Hydrogen supplying to industrial users is currently the major hydrogen business worldwide and the demand for hydrogen is almost entirely supplied from fossil fuels. In the last years a widespread interest on hydrogen has grown as energy vector for the decarbonization of multiple sectors, including industry, transport and buildings. Nevertheless, the impact of natural gas and other fossil fuels substitution with hydrogen is highly affected by the mix of different technologies and energy sources applied for hydrogen generation. The paper aims to investigate current CO 2 emissions related with hydrogen generation in Australia and Italy by means of PEM and alkaline technologies; and to evaluate the potential impact considering cell characteristics variation and 3 scenarios based on energy mix. A sensitivity analysis is performed to identify the critical parameters. Based on experimental data, the energy consumption for hydrogen production using PEM technology is more sensitive to cell voltage compared to current density, which indicates the importance of cell manufacturing and electrolyte resistance. In addition, by performing sensitivity analysis regarding energy sources scenarios it is found that carbon dioxide emission in Australia is more sensitive to renewable energy sources rather than Italy. [Display omitted] • Importance of manufacturing and electrolyte resistance because of high PEM energy consumption sensitivity to cell voltage. • Predicted emission in 2030 is 5–10 kg CO 2 using PEM/alkaline technology in Italy. • 18% reduction of national electricity grid CO 2 emission in Australia is predicted. (In 2030 in comparison to 2019). [ABSTRACT FROM AUTHOR]

Australia has significant offshore wind resources that can support ever-increasing power demand and hence provide a long-term contribution to the net-zero target by 2050. Although some offshore wind farm projects are proposed in Australia, the industry is not mature enough to justify the benefits and display economic opportunities. More specifically, the techno-economic analysis of offshore wind energy has not been systematically mapped in Australia. Therefore, the investigation of the spatial variation of technical feasibility and economic potential of offshore wind is vital to identify the potential regions and to develop strategies for future offshore wind farm developments in Australia. This paper presents detailed mappings of the techno-economic potential of offshore wind energy in Victoria-Tasmania's exclusive economic zone. Offshore wind energy resources are assessed in the paper in terms of energy availability and variability and the feasibility of various offshore wind technologies is investigated, including turbines, foundation types and transmission topologies. In addition, the high-fidelity life-cycle cost models and two innovative evaluation matrices are developed to assess the economic potential of offshore energy farms in South-eastern Australia. The impact of local power system regulation costs and future carbon prices on the viability of offshore wind is also investigated in this paper. It is concluded that the detailed mapping approach and analysis in the paper can provide a systematic tool for industry partners, investors and policymakers at the pre-planning stage of developing offshore renewable energy systems. • The technical feasibility of offshore wind is mapped under various constraints. • The novel LACE and high-fidelity LCC assessment models are developed. • The techno-economic potential is systemically mapped in South-eastern Australia. • The impacts of electricity market regulation and future carbon prices are studied. [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]

Offshore wind energy and wave energy are two exploitable sources of renewable energy available in the same marine environment. A co-located energy harvesting system of these two resources has the potential to play a leading role in the recent renewable energy mix mandate. A co-located system of mature offshore wind technology and a nascent wave energy technology can exploit the synergies of the two technologies, including the reduced cost, the easy operation and maintenance, and more importantly the potential of decreased power variability. This paper deals with a multi-site analysis of wind and wave power resources, and the estimation of exploitable energy through different wind turbines and wave energy converters (WECs). Results are presented for NSW Australia, but the devised strategy is generic and can be implemented in any region. Ten WECs, with different operating principles, are used for the estimation of the exploitable wave power, and the best performing converter is considered for the co-located wind-wave farm. A power fluctuation factor is introduced to quantify the reduction in power variability of the co-located wind-wave farm. Different percentage mixes of wind and wave energy are evaluated for the co-located wind-wave farm in the multi-site analysis. •A multi-site analysis of wind and wave for estimation of exploitable energy. •Performance evaluation of wind and wave energy conversion technologies. •Power variability representation with power fluctuation factor for a co-located farm. •Characterization of percentage share of wind and wave in a co-located farm. [ABSTRACT FROM AUTHOR]

Comparing the United Kingdom's Renewables Obligation and Australia's Renewable Energy Target, this paper reconstructs the market for green certificates in which penalties are imposed on missed certificates. Our analysis shows that excess demand in the Renewables Obligation makes the penalty the minimum certificate price, but excess supply in the Renewable Energy Target makes the penalty the maximum certificate price. Further, excess supply also implies that the sales of certificates are not guaranteed in the Renewable Energy Target. Therefore, compared to the Renewables Obligation, generators face greater risk under the Renewable Energy Target as there is more uncertainty about the price of certificates and the possibility of sales. • Reconstruct demand and supply curves for TGC models. • Excess demand (supply) makes the penalty the minimum (maximum) certificate price. • The recycling mechanism pushes up the certificate price in the RO. • The refunding mechanism pushes down the certificate price in the RET. • Generators under TGC with excess supply face greater price risk and volume risk. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) is a viable technique to address food waste (FW) problems by converting FW into sustainable energy. Despite the advantages of the AD process, large-scale AD plants like in Germany and the USA have not been developed in Australia. Therefore, this paper aims to study the technological, economic, and environmental feasibility of sustainable energy production from household FW in Australia. In addition, this paper discusses the different waste to energy (WtE) technologies along with the operational parameters as well as the challenges in developing a biogas plant. The energy and economic potential analysis of the AD process indicates that the processing of 10% FW from Australian states and territories can generate 1.22 GWh to 35.4 GWh electricity which can subsequently earn AUD 0.54 million to AUD 15.7 million revenue per year. The greenhouse gas (GHG) emissions analysis indicates that conversion of the 10% of Australian FW has the potential to lower GHG emissions by 639,852 tonnes per annum. Hence, it can be said that FW plays a vital role as a promising source of sustainable energy and is capable of benefiting the country's economy significantly and reducing GHG emissions. • FW is a potential source of sustainable energy. • AD is a useful waste to energy process for producing clean energy. • Conversion of FW into energy can significantly lower GHG emissions. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as wind farms and solar power plants are replacing conventional coal-based synchronous generators (SGs) to achieve net-zero carbon emissions worldwide. SGs play an important role in enhancing system strength in a power system to make it more stable during voltage/frequency disruptions. However, traditional coal-fired SGs are being decommissioned in many parts of the world, owing to stringent environmental regulations and low levelized cost of energy of renewables. Consequently, maintaining system strength in a renewable energy-dominated power system has become a major challenge, and without adequate mitigation techniques, low system strength can potentially cause widespread power outages. This paper provides an overview of system strength and its measurement techniques in a power system with a large number of renewable energy sources (RESs), for example solar and wind farms. The review includes the system strength measurement techniques, mitigation approaches, and future challenges. • A comprehensive review on system strength for Australia. • Integration of power systems with large number of renewable energy sources. • Measurement and mitigation techniques for system strength shortfalls. • Analysis of future challenges and research directions on system strength. [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]

The Asia-Pacific has experienced prodigious growth in energy use and is by far the world's largest greenhouse-gas emitting region. Australia has played a leading role in meeting the region's energy and resource needs, becoming the world's largest exporter of coal, liquefied natural gas, iron ore, and alumina. Our analysis shows that these exports are tied to sizeable consequential emissions at the point of use or processing, accounting for about 8.6% of the total greenhouse gas emissions of the Asia-Pacific. The paper investigates three pathways by which Australia could instead export zero-carbon energy and products: direct exports of renewable electricity via sub-sea cables, exports of zero-carbon fuels such as green hydrogen, and the export of "green" metals processed from Australian ores using renewable energy. Carrying out robust, high-level calculations we find that Australia has the land and renewable energy resources to become a key exporter of these commodities. Realization of this potential relies on ongoing cost reductions, growing demand-side interest linked to meeting ambitious emission reduction targets in the region, and the development of cross-border frameworks for clean energy trade. If it were to achieve this goal, Australia could make a sizeable contribution to regional decarbonization via renewable-energy based exports. • Consequential emissions from existing key Australian exports are estimated. • The potential for replacement zero-carbon exports from Australia is analyzed. • Land, water, and energy requirements for new zero-carbon exports are calculated. • There is the potential to reduce Asia-Pacific greenhouse gas emissions by about 8.6%. • Around 2% of Australia's land mass would be required for solar and wind farms. [ABSTRACT FROM AUTHOR]

• Novel ensemble framework which improves short-term load forecasting accuracy. • Model divides the problem into several subtasks based on peak and off-peak conditions. • Classification and regression models were developed to solve each subtask. • Framework is validated on real-world operational demand across regions in Australia. • Model has a substantially higher accuracy (up to 25.4% in MAE) over other methods. [Display omitted] To keep the balance between electricity demand and supply as well as infrastructure planning, it is important to accurately forecast the electricity demand. This has become a challenging task due to increasing share of renewable energy and prosumers (i.e. consumers who produce electricity) in the electricity grid. This paper develops a cooperative ensemble framework which divides the forecasting problem into several subtasks based on peak and off-peak conditions. Each subtask is then solved using multiple forecasting models that include classification and regression. The developed framework is finally validated on real-world operational demand across the National Electricity Market (NEM) of Australia. The performance is comprehensively compared against various state-of-the-art techniques in the field, which indicates up to 25.4% mean absolute error (MAE) improvement. [ABSTRACT FROM AUTHOR]