Latvia

This work is aimed at solving the problem of converting diesel power drives to diesel– hydrogen fuels which are more environmentally friendly and less expensive alternatives to diesel fuel. The method of increasing the energy efficiency of diesel fuels has been improved. The thermochemical essence of using methanol as an alternative fuel to increase energy efficiency based on the provisions of thermotechnics is considered. Alternative methanol fuel has been chosen as the initial product for the hydrogen conversion process and its energy value cost and temperature conditions have been taken into account. Calculations showed that the caloric effect from the combustion of the converted mixture of hydrogen H2 and carbon monoxide CO exceeds the effect from the combustion of the same amount of methanol fuel. Engine power and fuel energy were increased due to the thermochemical regeneration of engine exhaust gas heat. An experimental setup was created to study the operation of a converted diesel engine on diesel–hydrogen products. Experimental studies of power and environmental parameters of a diesel engine converted for diesel–hydrogen products were performed. The studies showed that the conversion of diesel engines to operate using diesel– hydrogen products is technically feasible. A reduction in energy consumption was accompanied by an improvement in the environmental performance of the diesel–hydrogen engine working together with a chemical methanol conversion thermoreactor. The formation of carbon monoxide occurred in the range of 52–62%; nitrogen oxides in the exhaust gases decreased by 53–60% according to the crankshaft speed and loading on the experimental engine. In addition soot emissions were reduced by 17% for the engine fueled with the diesel–hydrogen fuel. The conversion of diesel engines for diesel–hydrogen products is very profitable because the price of methanol is on average 10–20% of the cost of petroleum fuel.

When the renewable energy is used the challenge is match the supply of intermittent energy with the demand for energy therefore the energy storage solutions should be used. This paper is dedicated to hydrogen accumulation from wind sources. The case study investigates the conceptual system that uses intermitted renewable energy resources to produce hydrogen (power-to-gas concept) and fuel (power-to-liquid concept). For this specific case study hydrogen is produced from surplus electricity generated by wind power plant trough electrolysis process and fuel is obtained by upgrading biogas to biomethane using hydrogen. System dynamic model is created for this conceptual system. The developed system dynamics model has been used to simulate 2 different scenarios. The results show that in both scenarios the point at which the all electricity needs of Latvia are covered is obtained. Moreover the methodology of system dynamics used in this paper is white-box model that allows to apply the developed model to other case studies and/or to modify model based on the newest data. The developed model can be used for both scientific research and policy makers to better understand the dynamic relation within the system and the response of system to changes in both internal and external factors.

This research delineates a pivotal advancement in the domain of sustainable energy systems with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore the paper elucidates the construction of the forecasting model revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.