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Offshore wind power stands out as a promising renewable energy source, offering substantial potential for achieving low carbon emissions and enhancing energy security. Despite its potential, the expansion of offshore wind power faces considerable constraints in offshore power transmission. Hydrogen production derived from offshore wind power emerges as an efficient solution to overcome these limitations and effectively transport energy. This study systematically devises diverse hydrogen energy supply chains tailored to the demands of the transportation and chemical industries, meticulously assessing the levelized cost of hydrogen (LCOH). Our findings reveal that the most cost-efficient means of transporting hydrogen to the mainland is through pipelines, particularly when the baseline distance is 50 km and the baseline electricity price is 0.05 USD/kWh. Notably, delivering hydrogen directly to the port via pipelines for chemical industries proves considerably more economical than distributing it to hydrogen refueling stations, with a minimal cost of 3.6 USD/kg. Additionally, we assessed the levelized cost of hydrogen (LCOH) for supply chains that transmit electricity to ports via submarine cables before hydrogen production and subsequent distribution to chemical plants. In comparison to offshore hydrogen production routes, these routes exhibit higher costs and reduced competitiveness. Finally, a sensitivity analysis was undertaken to scrutinize the impact of delivery distance and electricity prices on LCOH. The outcomes underscore the acute sensitivity of LCOH to power prices, highlighting the potential for substantial reductions in hydrogen prices through concerted efforts to lower electricity costs. [ABSTRACT FROM AUTHOR]

Summary: With a high proportion of variable renewable energy integration, accurate forecasting approach is of vital importance in ensuring the reliable and economic operation of power system. Therefore, in this article, a novel method for predicting photovoltaic (PV) power generation based on convolutional neural network (CNN) is proposed. Analytical models of PV systems are formulated, thereby providing physical knowledge about the relationship between PV output and critical meteorological features. To explore the nonlinear and time‐varying properties of PV output, CNN is adopted in this article, which matches the patterns of similar days. Case studies based on realistic datasets in Australia demonstrate that the forecasting performance for solar power can be effectively improved by taking advantage of the proposed CNN‐based learning method. [ABSTRACT FROM AUTHOR]