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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]

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]