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Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model's robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel "policy-versus-policy" strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF. [ABSTRACT FROM AUTHOR]

As the global energy landscape transitions towards a more sustainable future, hydrogen has emerged as a promising energy carrier due to its potential to decarbonize various sectors. However, the economic competitiveness of hydrogen production by water electrolysis strongly depends on renewable energy source (RES) availability. Thus, it is necessary to overcome the challenges related to the intermittent nature of RESs. This paper presents a comprehensive techno-economic analysis of complementing green hydrogen production with grid electricity. An evaluation model for the levelized cost of hydrogen (LCOH) is proposed, considering both CO2 emissions and the influence of RES fluctuations on electrolyzers. A minimum load restriction is required to avoid crossover gas. Moreover, a new operation strategy is developed for hydrogen production plants to determine optimal bidding in the grid electricity market to minimize the LCOH. We evaluate the feasibility of the proposed approach with a case study based on data from the Kyushu area in Japan. The results show that the proposed method can reduce the LCOH by 11% to 33%, and increase hydrogen productivity by 86% to 140%, without significantly increasing CO2 emission levels. [ABSTRACT FROM AUTHOR]

Ongoing climate change has increased the impact of landslides and related slope disasters on infrastructure and human lives. Microwave satellite remote sensing, particularly interferometric analysis of synthetic aperture radar (InSAR) data, is a powerful tool for routine monitoring of the displacement of slopes on small wavelength scales, independently from solar illumination and cloud coverage. Although various sophisticated techniques for time-series InSAR have been developed, practical application in conjunction with geological analysis to reveal intrinsic and triggering factors of slope displacement is still limited. In this paper, we describe a practical case of time-series InSAR analysis with a special focus on its geological implications in northwestern Kyushu, a high-risk area for slope-related disasters in Japan. The extracted susceptible polygons from the InSAR results show accuracy comparable with that of other recent research on landslide mapping. Intrinsic factor analysis based on a geographic information system reveals that the displacement occurs on relatively gentle slopes instead of steep areas, corresponding to the transient zone of Paleogene-Neogene sedimentary rocks and basalt. Triggering factor analysis based on correlation coefficients reveals a significant link between some displacement areas with mean and/or maximum precipitation for each observation duration. Those findings confirm the importance of carefully accounting for the geological background for landslide susceptibility assessment and policy making, apart from topographic and meteorological conditions. [ABSTRACT FROM AUTHOR]