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Low or even zero carbon dioxide emissions will be an essential requirement for energy supplies in the near future. Besides transport and electricity generation, industry is another large carbon emitter. Hydrogen produced by renewable energy provides a flexible way of utilizing that energy. Hydrogen, as an energy carrier, could be stored in a large capacity compared to electricity. In Sweden, hydrogen will be used to replace coal for steel production. This paper discusses how the need for electricity to produce hydrogen will affect the electricity supply and power flow in the Swedish power grid, and whether it will result in increased emissions in other regions. Data of the Swedish system will be used to study the feasibility of implementing the hydrogen system from the power system viewpoint, and discuss the electricity price and emission issues caused by the hydrogen production in different scenarios. This paper concludes that the Swedish power grid is feasible for accommodating the additional electricity capacity requirement of producing green hydrogen for the steel industry. The obtained results could be references for decision makers, investors, and power system operators. [ABSTRACT FROM AUTHOR]

Geothermal energy exploration, development, and research have been ongoing in Canada for several decades. The country's cold climate and the push to develop renewable energy sources have driven interest in geothermal energy. Despite this drive, regulatory complexities and competition with other relatively inexpensive energy sources with existing infrastructure have hindered development. As such, interest has grown and waned with changes in the energy economy over several decades, leaving many projects at a standstill. As of January 2023, there are currently no operational geothermal power projects in Canada. Many hot spring pool and spa complexes remain active, and Canada is a leading country in the installation of ground source heat pumps (GSHPs; also called geo-exchange systems). However, in the last decade, the interest in deep geothermal systems has renewed, with many new projects starting up across several provinces and territories. Moreover, projects that had shown limited progress for many years—such as Mount Meager in British Columbia—have begun to renew their development efforts. Research is also expanding within prominent research groups and universities. The areas of focus include both building upon previous studies (such as thermal gradients and the heat flow in sedimentary basins) and researching new methods and resources (such as GSHPs, closed-loop systems, integrated geothermal operations, and hybrid systems, including heat storage). The development is supported by federal, provincial, and territorial governments through grants and the development of regulatory frameworks. Although challenges still remain for Canada to develop its geothermal energy resources, several power, thermal, and co-production projects, ongoing research, funding, and regulatory acts are all moving forward to support geothermal development. This paper aims to study Canada's geothermal energy update in 2023 regarding the aspects mentioned above. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model. [ABSTRACT FROM AUTHOR]