Future Energy

Towards sustainable energy: a comprehensive review on hydrogen integration in renewable energy systems

Wahid Bin Noor, Tanvir Amin — Thu, 13 Jun 2024 00:00:00 +0000

As the world shifts towards sustainable energy sources, incorporating hydrogen into renewable energy systems emerges as a critical pathway. This thorough analysis delves deeply into the various facets of hydrogen integration, exploring its potential to revolutionize the energy landscape. Drawing upon recent advancements and research findings, the review examines the production, storage, and utilization of hydrogen within renewable energy frameworks. Key topics include electrolysis methods, storage technologies, and diverse applications spanning transportation, residential sectors, and industry. Furthermore, the review examines the obstacles and prospects linked with hydrogen integration, shedding light on policy frameworks, economic implications, and technological innovations driving its adoption. By offering insights into the multifaceted role of hydrogen, this review aims to inform researchers, stakeholders, and policymakers about the transformative potential of integrating green hydrogen into renewable energy systems for a sustainable future.

Unlocking the potential of green hydrogen for a sustainable energy future: a review of production methods and challenges

Mon, 29 Jul 2024 00:00:00 +0000

The buzz around green hydrogen is growing louder as a game-changer in the fight for a clean and sustainable energy future. This article dives into the coolest ways to create this eco-friendly fuel, exploring methods like splitting water with electricity, turning plant matter into gas, and even some cutting-edge techniques in the pipeline. This research dives deep into the latest breakthroughs in electrocatalyst and electrode materials, the secret ingredients that could supercharge hydrogen production, making it cleaner and cheaper. While good old water electrolysis using alkaline and PEM electrolyzers is the current champ, it's still a bit pricey and not as efficient as we'd like. Thankfully, innovative ways to design these "fuel-splitting champions" and integrate them with renewable energy sources are showing promise as solutions. But green hydrogen isn't just some cool science experiment; it's a potential game-changer for cleaning up our transportation, factories, and even the way we power our homes, all to fight climate change. The study also identifies areas where we need more research and ironing out of kinks before widespread use. It emphasizes the importance of keeping the innovation train rolling, smart investments in this technology, and government policies that give it a green light. By pushing green hydrogen forward, we can slash greenhouse gasses, become more energy-independent, and finally build that sustainable energy future we've all been dreaming of!

Deep-learning-based multi-timescale load forecasting in buildings: opportunities and challenges from research to deployment

Tue, 27 Aug 2024 00:00:00 +0000

Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore, forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.

Qatar's energy policy in the field of renewable energy

Rahmat Hajimineh, Ebrahim Rezaei Rad, Mahsa Sadat Hosseini — Sat, 31 Aug 2024 00:00:00 +0000

Qatar has abundant oil and gas reserves and has made extensive investments in renewable energy according to the zero-carbon policy in recent years. Renewable energy is vital for Qatar, and many investments and plans have been made in this country. Therefore, this article examines Qatar's energy policy in renewable energy using a mixed quantitative-qualitative methodology. In this article, an attempt has been made to answer the question, what is Qatar's energy policy in the field of renewable energy? As a hypothesis, Qatar's energy policy has changed from dependence on non-renewable energies to using and developing renewable energies to stabilize its energy security in the coming decades. Also, the theoretical framework of energy security has been used to test the hypothesis. The research findings indicate that Qatar has also adopted this policy due to the significant changes in the orientation of developed and developing countries towards new energies. Like many Persian Gulf countries, Qatar's policies have been aimed at reducing carbon. With extensive investments and long-term planning until 2030, Qatar can become a model country in the Middle East region in terms of renewable energy. This issue and fossil fuels give Qatar a special place that can turn Qatar into a fossil and renewable energy hub.

Advanced neural network and hybrid models for wind power forecasting: a comprehensive global review

Malixole Sambane, Bongumsa Mendu, Bessie Baakanyang Monchusi — Mon, 14 Oct 2024 00:00:00 +0000

Neural Network Algorithms (NNAs), modeled after the workings of biological neurons, are increasingly utilized in areas like data mining and robotics to address complex challenges in artificial intelligence (AI). This research will undertake a systematic review based on advanced neural networks and hybrid models for wind power forecasting. Using the Scopus database, a methodical search, acquisition, and filtering procedure was utilized to find pertinent publication documents; VOSviewer software was utilized to analyze trends. The emphasis on improving prediction accuracy and stability in wind power forecasting through the application of cutting-edge machine learning techniques and hybrid models is a prominent feature that unites the literature. Furthermore, attention is being paid to resolving issues pertaining to the production of wind energy, such as wind power fluctuation management, grid integration problems, wind speed prediction, and turbine health monitoring. A rising trend involves multi-dimensional, multi-step forecasting and incorporating factors like weather data and spatial-temporal features to enhance reliability. This paper contributes by exploring the integration of optimization techniques with neural networks, investigating hybrid models to improve wind power predictions, assessing LSTM-based approaches in forecasting, and suggesting directions for future research.