The power sector plays a critical role in the functioning of the economy and the security of a country, being closely interconnected with other vital infrastructures, such as gas supply, water supply, transportation, and telecommunications. Ensuring a stable power supply is crucial for the uninterrupted operation of these systems. One way to enhance the resilience of the power system is by integrating local networks with distributed renewable generation into the overall energy infrastructure. The flexibility, stability, controllability, and self-healing capabilities of microgrids make them an effective solution for improving the resilience of the power system. The power grid is susceptible to disturbances and disruptions that can cause large-scale power outages for consumers. Statistical data indicates that approximately 90% of outages occur due to issues in the distribution system, thus research focuses on local microgrids with distributed renewable generation. This study analyzed the role of microgrids with renewable generation in enhancing the resilience of power systems. Additionally, functions of microgrids that contribute to enhancing power system resilience, such as service restoration, network formation strategies, control and stability, as well as preventive measures, were summarized. It was found that local microgrids have significant potential to enhance power system resilience through the implementation of various strategies, from emergency response planning to providing reliable energy supply for quick responses to military, environmental, and human-induced crises. The concept of local distributed energy generation, storage, and control can reduce reliance on long-distance power transmission lines, reduce network vulnerabilities, and simultaneously improve its resilience and reduce recovery time. It has been determined that the most necessary and promising approaches to enhance the resilience of the power system include developing appropriate regulatory frameworks, implementing automatic frequency and power control systems, ensuring resource adequacy (including the reservation of technical components), promoting distributed generation, integrating energy storage systems into the energy grid, and strengthening cyber security. Keywords: resilience, local power systems, MicroGrid, distributed generation, renewable energy sources.

This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, model-based diagnostics, and data-driven techniques, evaluating their strengths and limitations for both EV and ESS applications. This study underscores the necessity of accurate SOH diagnostics to maximize battery reuse, promoting sustainability and circular economy objectives. By providing a comprehensive overview of the battery lifecycle—from manufacturing to recycling—this research offers strategies for effective lifecycle management and cost-effective, environmentally sustainable secondary battery applications. Key findings highlight the potential of second-life EV batteries in ESSs. The integration of the considered diagnostic methods was shown to extend battery lifespan by up to 30%, reduce waste, and optimize resource efficiency, which is crucial for achieving circular economy objectives. This paper’s insights are crucial for advancing sustainable energy systems and informing future research on improving diagnostic methods for evolving battery technologies.

This study presents a mathematical model for forecasting the development of Ukraine’s Integrated Power System (IPS) until 2040, with a specific focus on the expansion of nuclear energy as a cornerstone of the nation’s low-carbon transition. The model is an extension of Frank Bass’s mixed influence diffusion model, incorporating both economic and technological factors. These factors are treated as stochastic variables essential for accurately predicting the evolution of an integrated energy system, particularly in the context of rapid renewable energy sources (RES) growth. The model employs regression techniques using generalized logistic curves, improving forecasting efficiency by aligning modeling parameters with experimental data. The study’s results indicate the potential for optimizing IPS components, including nuclear and thermal power generation, through the model’s application. The model is distinguished by its inclusion of economic and technological impacts, such as state matrices, control actions, and external influence matrices, which enhance the accuracy of simulations and predictions. The validation of the model, based on scenarios of electricity consumption and generation, shows significant alignment with observed trends, confirming the model’s reliability. The findings suggest that this model is an effective tool for developing and refining energy system scenarios, with nuclear energy playing a pivotal role in Ukraine’s sustainable energy future.