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Hydrogen is fast becoming a new international "super fuel" to accelerate global climate change ambitions. This paper has two inter-weaving themes. Contextually, it focuses on the potential impact of the EU's new Carbon Border Adjustment Mechanism (CBAM) on fossil fuel-generated as opposed to green hydrogen imports. The CBAM, as a transnational carbon adjustment mechanism, has the potential to impact international trade in energy. It seeks both a level playing field between imports and EU internal markets (subject to ambitious EU climate change policies), and to encourage emissions reduction laggards through its "carbon diplomacy". Countries without a price on carbon will be charged for embodied carbon in their supply chains when they export to the EU. Empirically, we focus on two hydrogen export/import case studies: Australia as a non-EU state with ambitions to export hydrogen, and Germany as an EU Member State reliant on energy imports. Energy security is central to energy trade debates but needs to be conceptualized beyond supply and demand economics to include geopolitics, just transitions and the impacts of border carbon taxes and EU carbon diplomacy. Accordingly, we apply and further develop a seven-dimension energy security-justice framework to the examples of brown, blue and green hydrogen export/import hydrogen operations, with varying carbon-intensity supply chains, in Australia and Germany. Applying the framework, we identify potential impact—risks and opportunities—associated with identified brown, blue and green hydrogen export/import projects in the two countries. This research contributes to the emerging fields of international hydrogen trade, supply chains, and international carbon diplomacy and develops a potentially useful seven-dimension energy security-justice framework for energy researchers and policy analysts. [ABSTRACT FROM AUTHOR]

The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities. [ABSTRACT FROM AUTHOR]