The decarbonization of hard-to-abate industries is crucial for keeping global warming to below 2°C. Green or renewable hydrogen, synthesized through water electrolysis, has emerged as a sustainable alternative for fossil fuels in energy-intensive sectors such as aluminum, cement, chemicals, steel, and transportation. However, the scalability of green hydrogen production faces challenges including infrastructure gaps, energy losses, excessive power consumption, and high costs throughout the value chain. Therefore, this study analyzes the challenges within the green hydrogen value chain, focusing on the development of nascent technologies. Presenting a comprehensive synthesis of contemporary knowledge, this study assesses the potential impacts of green hydrogen on hard-to-abate sectors, emphasizing the expansion of clean energy infrastructure. Through an exploration of emerging renewable hydrogen technologies, the study investigates aspects such as economic feasibility, sustainability assessments, and the achievement of carbon neutrality. Additionally, considerations extend to the potential for large-scale renewable electricity storage and the realization of net-zero goals. The findings of this study suggest that emerging technologies have the potential to significantly increase green hydrogen production, offering affordable solutions for decarbonization. The study affirms that global-scale green hydrogen production could satisfy up to 24% of global energy needs by 2050, resulting in the abatement of 60 gigatons of greenhouse gas (GHG) emissions - equivalent to 6% of total cumulative CO ₂ emission reductions. To comprehensively evaluate the impact of the hydrogen economy on ecosystem decarbonization, this article analyzes the feasibility of three business models that emphasize choices for green hydrogen production and delivery. Finally, the study proposes potential directions for future research on hydrogen valleys, aiming to foster interconnected hydrogen ecosystems.

With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9 % recall, 100% F1, 100% precision, 99.21 % training accuracy, and 99.70% testing accuracy.)

In the advanced metering infrastructure, smart meters are deployed at the consumers’ side to regularly transmit fine-grained electricity consumption readings to the system operator (SO) for billing and real-time load monitoring and energy management. However, fraudulent consumers may compromise their meters to launch electricity-theft cyberattacks by reporting low-consumption readings to reduce their bills. These false readings not only cause financial losses but also degrade the grid’s performance because they are used for energy management and load estimate. The existing solutions in the literature focus only on securing the billing, so they are not designed to detect the attacks in real time, and thus the SO may use false readings for a long period of time in load monitoring and energy management until they are identified. In this paper, we propose a general ensemble-based deep-learning detector that enables the SO to detect false readings in real time. To do that, we first train several deep learning models on samples generated from a sliding window of the readings. Then, we use the best-performing model to train several models on different ratios of false readings and use them in our ensemble-based detector. Extensive experiments are conducted, and the results indicate that comparing to the literature, our detector can detect the false readings after sending a few false readings (around 15) comparing to the existing daily and weekly detection approaches that need 144 and 1,008 readings, respectively.