The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to enhance system performance and resilience. Using comprehensive methodologies, the review examines state-of-the-art algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search Algorithm (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp Swarm Algorithm (LF-SSA), Mixed-Integer Linear Programming (MILP) and tools like HOMER Pro 3.12–3.16 and MATLAB 9.1–9.13, which have been instrumental in optimizing HRESs. Key findings highlight the growing role of advanced, multi-energy storage technologies in stabilizing HRESs and addressing the intermittency of renewable sources. Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. HRES configurations for cost-effectiveness, environmental sustainability, and operational reliability while also emphasizing the transformative potential of emerging technologies such as quantum computing are underscored. This review provides critical insights into the evolving landscape of HRES optimization, offering actionable recommendations for future research and practical applications in achieving global energy sustainability goals.

This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. By analyzing real-world weather scenarios, the approach aims to identify the highest accuracy forecasting model for the short-term 24-h forecast of wind farm power output. A more accurate forecast allows for more efficient resource planning and better distribution of resources on the electricity grids, thus ensuring a greener approach to energy production. The study shows that the proposed Hybrid (XGBoost + LTC) model predicts wind power generation with an nMAE of 0.0856, representing an improvement over standalone XGBoost and LTC models, and outperforming classical approaches such as LSTM and statistical models like ARIMAX in terms of forecasting accuracy.

Hydrogen has emerged as a promising climate-neutral energy carrier able to facilitate the processes of the European Union (EU) energy transition. Green hydrogen production through the electrolysis process has gained increasing interest recently for application in various sectors of the economy. As a result of the increasing renewable energy developments in the EU, hydrogen is seen as one of the most promising solutions for energy storage challenges; therefore, the leading countries in the energy sector are heavily investing in research of the technical obstacles for hydrogen applications and assessment of the current hydrogen market, which in turn leads to the acceleration of the upscaling of hydrogen production. The main objective of this article was to provide a comprehensive overview of various green hydrogen production, transportation and industrial application technologies and challenges in Europe, with a separate analysis of the situation in Lithuania. Various water electrolysis technologies and their production costs are investigated, along with recent developments in storage and transportation solutions. In addition, the performances and limitations of electrochemical processes are presented and analysed, research trends in the field are discussed and possible solutions for performance and cost improvements are overviewed. This paper proposes a discussion of perspectives in terms of future applications and research directions.

Analysis of wind power utilization efficiency around the world and Europe has revealed the gap between feasible and factual power generation. The paper presents an investigation of wind power generation dynamics, penetration levels into the electricity system, and dependence of capacity factors on the hub height and the rotor diameter of wind turbines in the Baltic States. These factors are the main for the evaluation of wind power utilization efficiency. Wind power penetration levels show that possibilities of the energy system to accept more wind power installations in the Baltic States are far away from its limit. Besides, dependence of high wind turbines capacity factors’ on the hub height and the rotor diameter in the range of 20–120 m was revealed.