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The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm's convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%. • Fast and stable convergence of IEGS distributed optimization problem. • ADMM with an adaptive penalty parameter guarantees the convergence. • Update rate further improves the sensibility of the penalty parameter. • Mean value and standard deviation of running iterations significantly decrease. [ABSTRACT FROM AUTHOR]

Hydrogen is considered a key energy vector and carrier for the decarbonization of global energy systems. However, the economics of green hydrogen systems hinder their widespread application. This paper presents a techno-economic analysis of a green hydrogen production system using high-temperature water electrolysis integrated with a concentrated solar power system (CSP-SOEC) for Western Australia. Real-time solar resource data with 30-min resolution for a typical meteorological year were used to assess the performance of the entire system. The intermittent nature of solar resources is accounted for by integrating the system with a thermal energy storage medium and performing the analysis in off-design mode. The validity of the electrolysis stack model is crucial for overall system performance, which was confirmed through experimental testing conducted on a 15-cell stack. The system was designed to generate a 1 MWe output, and the results showed that a field area of 29,000 m2 and thermal energy storage capacity of 382,500 kWh can fulfil the design criteria. The system generates 0.86 tonne/day of hydrogen at a cost of 8.87 US$/kg-H 2 with a solar-to-hydrogen efficiency of 13.80 %. The cost breakdown revealed that the storage medium has the most significant contribution. Moreover, the sensitivity of the system to the production capacity was analyzed, which showed that larger-scale hydrogen production systems have the potential to further reduce the cost. An 8 MWe system has the capacity to produce 7.18 tonne/day of hydrogen at a cost of 6.1 US$/kg-H 2. The molten salt is currently utilized only 39.3 % for the hydrogen production process. To optimize resource utilization, a cogeneration system is devised and assessed for simultaneous steam and hydrogen production. The results reveal that the cogeneration system can achieve an LCOH reduction of 9 % by reaching 8.07 US$/kg-H 2. These findings are invaluable for academic and industry stakeholders in making informed decisions and fostering the green hydrogen sector in Australia. • A techno economic assessment of green H 2 production for Australia is done. • The electrolysis model is corroborated using experimental results. • The cost of green H 2 for such system in Australia is 8.87 US$/kg-H 2. • Higher production capacity favors the system economics. • The cogeneration system reduces the cost by 9 %. [ABSTRACT FROM AUTHOR]

In response to the high uncertainty associated with residential net load due to the coupling of distributed photovoltaic generation and user demand, this paper proposed a novel cluster-based stacking ensemble learning model for net load interval prediction. Firstly, the k-means algorithm is employed to discover the similarity in user electricity consumption patterns. Then, a RIME optimization algorithm with local enhancement (LRIME) is developed to optimize the parameters and weights of the base learners in stacking ensemble learning. Subsequently, base learners with strong predictive capabilities and significant diversity are chosen as the first-layer predictive models, extreme learning machine (ELM) is utilized as the second-layer predictive model, ultimately resulting in the proposed stacking ensemble learning prediction model. And utilizing the bootstrap method to fit the volatility of point predictions, different prediction intervals are obtained at varying confidence levels, aiming to quantify the integrated uncertainty in photovoltaic generation and load. Testing on the open Ausgrid electricity load data in Australia provided robust validation of the proposed method's effectiveness. In comparison with other outstanding prediction models, the proposed ensemble model can effectively capture the uncertainty in integrating photovoltaic generation and user load. • A RIME optimization algorithm with local enhancement (LRIME) is developed. • A stacking ensemble model is proposed for residential net load interval prediction. • The proposed model demonstrates higher forecasting accuracy and reliability. [ABSTRACT FROM AUTHOR]

The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction. There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction. • The multivariate grey Bernoulli model is proposed to predict short-term power load. • The improved grey wolf algorithm is proposed to avoid falling into local optimum. • 3. The non-singular Caputo fractional derivative and Laplace transform are introduced. • The fractional-order operator is introduced to reduce the influence of randomness. [ABSTRACT FROM AUTHOR]

Fifth-generation district heating and cooling (5GDHC) systems have the potential to provide simultaneous heating and cooling, allowing for energy exchange between users with different needs. However, their viability in mild climates with a higher share of cooling demand remains unclear. In this paper, we propose a framework for assessing the engineering, economic and environmental performance of a 5GDHC system compared to a state-of-the-art business-as-usual solution and demonstrate it through a practical case study for a university campus in Melbourne, Australia. When accessible heat sources and sinks are available, the 5GDHC system provides a cost-effective solution, with annual cost savings between 9 and 29 % and GHG emissions reduction between 25 and 58 % compared to an already advanced business-as-usual system. Additionally, by using peak off-peak tariffs and an hourly emission factor for the electricity consumed, we demonstrate the 5GDHC operational flexibility in pursuing different objectives, such as minimising cost or emissions, respectively. The results suggest that 5GDHC systems are an economically and environmentally viable solution in milder climates, and a successful implementation of 5GDHC in Australia can create new market opportunities and pave the way for its adoption in other countries with similar climatic conditions and no established history of district heating systems. • 5GDHC systems are analysed in a new context with a milder climate. • A multi-dimensional assessment framework is proposed. • The framework is demonstrated through a practical case study in Melbourne, Australia. • Flexibility is assessed through varying electricity tariffs and emission factors. • The strong performance of the 5GDHC system offers insights into similar contexts. [ABSTRACT FROM AUTHOR]

As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent operation of the grid system is facing significant challenges. Therefore, accurately forecasting PV power output at various time scales is particularly urgent. To meet this demand, this paper proposes an LSTM-Informer model based on an improved Stacking ensemble algorithm (ISt-LSTM-Informer). The proposed model improves the k-fold cross validation in the traditional Stacking algorithm to a time-series cross validation for integrating time-series forecasting models. Simultaneously, it utilizes long short-term memory (LSTM) and Informer as the base models. By integrating the advantages of the two base models, the ISt-LSTM-Informer achieves accurate short and medium-term PV power forecasting. To validate the effectiveness of the model, a historical dataset from a PV system located in Uluru, Australia, is used for various types of experiments. Among them, comparative experiments validate the superiority of the model. Compared with five other methods, the ISt-LSTM-Informer obtains 21 optimal results for the four evaluation metrics of RMSE, MAE, MAPE, and R 2 across eight forecasting time scales. In addition, different combinations of base models are conducted to verify the advantages of the Stacking ensemble algorithm and the two base models, respectively. • A novel multi-timescale photovoltaic power forecasting model is proposed. • Time-series cross validation is introduced into the Stacking algorithm. • LSTM and Informer are utilized as the base models of the Stacking algorithm. • Various methods are compared to verify the proposed model's effectiveness. • The model's predictive accuracy is illustrated through various visualization methods. [ABSTRACT FROM AUTHOR]

Value-added trade has increased rapidly in recent decades in the context of globalisation. This paper aims to measure the energy embodied in the value-added trade and the ecological relationships in the networks of the international construction industries. A hybrid method which combines ecological network analysis and the input–output method has been adopted. The direct energy intensities in most countries ranged from 0 to 3.0 trillion joules per million US$ and the dominant relationships were control and exploitation in the selected 15 years. For the construction sector, the negative relationships were dominant at 63.9% in Australia and 79.19% in China. The ratio of positive relationships increased from 24.3% to 31.5%, while the ratio of negative relationships declined from 75.7% to 68.5% over the 15 years. The mutualism index ranged from 0.72 to 0.98 over the 15 years, maintaining a value less than 1. In contrast, the synergism index had an increase of 12.2 over the 15 years. The results indicate that both direct energy intensity and the value-added ratio are important factors in relation to the energy embodied in value-added trade. Sectoral influence on the national industrial system has been interpreted in this paper to stimulate further research. • The energy embodied in the value-added trade of the international construction industries. • The combination of ecological and economic connections in an analysis of value-added trade. • The influence of specific construction sectors on the network depending on flow capacity. • The exploitation and control relationships that dominate the value-added trade system. • The balance between the value-added ratio and energy intensity in sustainable development. [ABSTRACT FROM AUTHOR]

With the ever-increasing level of bidding freedom bestowed to participants in deregulated electricity markets, bidding strategies have become more diversified and complicated, inevitably giving rise to the growth of market uncertainties. Some researchers have developed tools to predict the bidding behaviors of generation companies (GENCOs) considering uncertainties. However, there still remains a gap in enhancing the performance of probabilistic Bidding Behavior Forecasting (BBF) and understanding the sources of bidding uncertainties in electricity markets. This paper proposes a holistic Bayesian Deep Learning (BDL) framework based on Accurate Variational Inference (AVI) to capture both aleatoric uncertainty and epistemic uncertainty, two important uncertainties in bidding behaviors. The introduced framework also procures higher BBF accuracy and reasonable computation cost compared with existing techniques. Derivatives of GENCOs' bidding uncertainty are ascertained by implementing a new metric to quantify the impact of influence factors. A numerical experiment is conducted using data from National Electricity Market (NEM) in Australia to demonstrate the performance of the proposed framework. • A BDL framework based on AVI is proposed for analyzing bidding uncertainty. • A modified BNN is constructed to perform BBF with higher efficiency. • The epistemic uncertainty and the aleatoric uncertainty in BBF are captured. • The derivatives of bidding uncertainty are ascertained. [ABSTRACT FROM AUTHOR]

Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem. [Display omitted] • A Hybrid deep learning model is proposed for daily electricity demand prediction. • Convolutional Neural Networks and Echo State Networks are integrated into a single forecasting algorithm. • The proposed deep learning-based approach is excellent in electricity demand prediction. • The proposed algorithm has practical use in inter-daily electricity demand prediction. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power forecasting (PVPF) plays an important role in the scheduling and operation of modern power systems. Considering the highly varying and complex features of PVPF, this paper constitutes a novel hybrid deep learning forecasting method. A similar day selection method based on the levy-flight beetle antennae search algorithm is proposed to select historical days similar to the forecast day from the real-time massive data. The Pearson correlation coefficient method is utilized to select the main meteorological factors while wavelet packet decomposition is used to decompose and reconstruct the original PV power into a series of sub-signals. A deep learning model taking the sub-signals of similar days as network inputs is established with a group of gated recurrent units (GRU), where the hyperparameters of each GRU network are effectively optimized. The forecasting result of each sub-signal is integrated to obtain the final forecasting PV power value. The simulation regarding the 5-min ahead PVPF is carried out based on a real-world dataset from Alice Spring, Australia. The simulation comparisons indicate that the proposed hybrid deep learning method outperforms other competitive PVPF methods in terms of both forecasting accuracy and computational efficiency. • Application of SDS-based GRU model to PV power forecasting. • A similar day selection method is developed based on the Levy-flight BAS algorithm. • Application of WPD to decompose the PV signal to extract the frequency feature. • Levy-flight BAS is used to optimize the hyperparameters of GRU. [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]

Ground source heat pump systems (GSHP) for residential building heating, cooling, and hot water are highly energy efficient but capital intensive when sized for peak demands. The use of supplemental sources of energy with GSHP systems enables improved life-cycle economics through the reduction in the size and cost of the GSHP components. This paper investigates the life-cycle economics of hybrid solar-assisted ground source heat pump systems (SAGSHP) using simulations validated from field data. The economics and optimal sizing of SAGSHP systems for heating dominant climates in four locations in Australia and ten locations elsewhere are evaluated in order to explore the suitability and relative merits of SAGSHP systems in a range of heating dominant climates. In locations having high or moderate levels of solar irradiation, high electricity prices, and high or moderate gas prices, SAGSHP systems are shown to have the lowest life cycle cost amongst alternatives, with predicted savings of up to 30%. • A comprehensive investigation of the design and performance of hybrid GHSPs. • A comparison of hybrid GSHPs and conventional systems on cost and CO 2 emissions. • Effects of local climatic and economic conditions are evaluated for 14 global cities. • Hybrid GSHPs have shown to be the most economical system for 10 out of 14 locations. • Local energy price is a key factor that influences the feasibility of hybrid GSHPs. [ABSTRACT FROM AUTHOR]