There is an unprecedented need to expand the toolbox of solutions to boost the scalability of clean power and energy systems. Amidst this challenge, nuclear energy is increasingly recognized as an important player in the path toward deep decarbonization of the global energy mix. This paper presents a technology review of small modular reactor (SMR) concepts currently under development and deployment. Both conventional and next-generation reactor technologies are evaluated with a focus on potential power system integration benefits, added system values, and provision of system-bearing services. Nevertheless, there are currently some uncertainties in the techno-economic competitiveness of SMRs, whether they can leverage economics of mass production over their inherent lack of economics of scale. To address this challenge, our paper provides some basic cost analysis of SMRs, taking into account their expected learning curves to evaluate the cost of future deployments and give insights into their economic competitiveness and potential role as a disruptive solution in the energy transition.

With a global energy system in rapid transformation from fossil fuels, Green Hydrogen is one of few solutions to hard-to-abate emissions within industry. While most hydrogen projects are in a planning phase, the Ovako hydrogen facility in Hofors, with a scrap-based Electric Arc Furnace process, was inaugurated in September 2023. This project studies wider system benefits of the electrolyser such as power grid support, oxygen byproduct, providing hydrogen to external actors, and district heating. This is analysed both with current capacity and in regards to possible future development. Replacing fossil fuel with hydrogen produced by an atmospheric alkaline electrolyser is an indirect electrification with the potential to decrease Green House Gas emissions. Industry-wide electrification increases the demand for electricity, affecting all existing users. Therefore, system benefits and sector couplings such as enabling ancillary services to the grid, producing low-marginal cost hydrogen for hydrogen-powered trucks, and using waste heat for district heating, are important to ascertain system-wide improvements.

This paper explores the application of Pumped-Storage Hydroelectricity (PSH) within an electricity market characterised by a substantial share of renewable and intermittent electricity production. The purpose of PSH is to enhance the alignment of supply with demand by storing energy at electricity surplus and releasing it during shortage. The Juktan Power Station, once the PSH facility in Sweden with a storage capacity of about 25 GWh and maximum generation of 335 MW, serves as a case study. An in-depth analysis of the properties of the Swedish electricity market and its influencing factors is conducted. Wind speed data is successfully used to increase accuracy of electricity price prediction. Furthermore, a model is developed to calculate potential profitability employing various operation methods. In conclusion, large-scale PSH provide significant benefit to the robustness of the electricity system with a clear increase in value beginning in 2021. However, concerns about grid tariff design, electricity market development and energy storage incentives means that investments are not obviously profitable.

A document by Jonas Noeland . Click on the document to view its contents.

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.