In recent years, decentralized renewable energy production has gained increasing importance. Challenges of distributed energy production include fluctuations in weather-dependent energy generation, which may not always meet peak consumption periods and can result in significant overproduction during low-load periods. Managing production and consumption is a fundamental task for efficient renewable energy utilization. The application of lithium-ion or other advanced battery technologies as community energy storage provides a more reliable power supply when operated optimally with advanced energy management and control systems. Digital platforms form the basis of Energy Communities, supporting necessary processes and functionalities, and enabling the integration of smart grids that utilize data from IoT devices, meteorological sources, and energy markets. This paper presents a design of an Energy Community management platform and digital tools that provide a systematic framework for mapping energy consumption trend within energy communities. Adopting a digital platform for an energy community involves the integration of IoT devices, a centralized database, and a software platform equipped with AIbased forecasting tools. Additionally, investigations into various modeling approaches have highlighted the superior performance of hybrid deep learning models, specifically those combining GRU and LSTM architectures, in predicting energy consumption. These models excel in forecasting consumption peaks, which is crucial for optimizing energy distribution and storage within the community and are able to overcome the limitations of classical forecasting methods, which usually do not account for external variables like weather changes, consumer trends, and technological advancements that might affect energy use.

This paper addresses the impact of spatial allocation on the flexibility requirements of wind farms in the context of the mid-term development of wind energy exploitation in Hungary. As a starting point for evaluating flexibility requirements, a wind power simulation model is adopted that converts hourly wind speed time series derived from climate reanalysis into hourly aggregate power output using theoretical power curves. This simulation model allows for evaluating various flexibility metrics for both existing and hypothetical wind farms. As a second step, a Genetic Algorithm (GA) is integrated with the simulation environment to find an optimal subset of hypothetical wind farms with respect to flexibility requirements. The application of GA reveals insights into how spatially optimizing wind farm placement can significantly reduce flexibility requirements. This finding can have practical implications for policy-makers and planners in the renewable energy sector, especially given the evolving regulatory landscape and increasing focus on grid stability.

To overcome the limitations in using historical time series data for the supply adequacy analysis of renewable power systems, a data enhancement process is implemented allowing for a temporal enrichment of the available historical data. The methodology is based on the direct conversion of gridded climate reanalysis time series into aggregate output estimates where the simulated aggregate output is estimated by machine learning and historical power system data are used as training and testing data set. As a case study, the data enrichment methodology was accomplished with Hungarian power system data. From the enriched wind and solar electricity generation time series, availability statistics were derived that can be integrated into analytical probabilistic adequacy risk assessment models to describe the availability of wind and solar energy as aggregate, multi-state units.