Power quality (PQ) becomes a more and more pressing issue for the operation stability of power systems with renewable energy sources. An important aspect of PQ monitoring of distribution networks is to compare the PQ indicators in different operating conditions. This paper evaluates the impact of a microgrid implementation in a real distribution network on power quality indicators at the point of common coupling in an LV network. The study includes a classical assessment of the long-term PQ parameters according to the EN 50160 standard, such as nominal frequency deviations, voltage RMS variations, voltage fluctuations (represented by long-term flicker severity), voltage unbalance and total harmonic distortion. The PQ evaluation is extended in statistical assessment based on cluster analysis. The case study contains 5 weeks of power quality observation results obtained at the assessment point in two different working conditions of the distribution system: before and after implementing the microgrid. The study allows establishing general conclusions regarding a microgrid interconnection in order not to exceed power quality limits and considering the influence of photovoltaic generation on power quality parameters.

Energy communities have recently gained significant attention as local entities that empower neighborhoods to contribute actively to the clean energy transition by adopting solar energy. However, the variability of weather conditions makes PV energy production highly unpredictable, emphasizing the need for accurate prediction and forecasting to ensure efficient operation and balance supply and demand. This study investigates the use of machine learning models to predict PV energy generation from multiple household rooftop photovoltaic (PV) systems within an energy community, with solar irradiance serving as the sole input parameter. Furthermore, various deep learning architectures were also explored to forecast solar radiation and determine the optimal model configuration. The results show that the Random Forest model performed better than the other models tested, achieving the lowest error metrics for PV energy prediction. For solar radiation forecasting, the GRU model demonstrates good performance compared the other models.

Due to the prospect of climate change and the challenges posed by the European Union to the modern power grid, a decentralized system based on distributed energy sources is being created from a centralized system based on utility power. It also involves new ideas on the operation and management of power grids, especially at the level of low-voltage distribution networks, where prosumers play a special role. In addition to the transformation of sources to renewables, the aim is to increase the flexibility of power grids by exploiting the regulatory potential of flexible grid components. The issue of grid flexibility assumes particular importance in the case of microgrids and local grids covered by the energy communities. Many posts describe the realization of the task of flexibility through energy storage, e.g., storing storage resources in electric vehicles or the use of energy transformation through conversion to heat, air compression air, or process cooling. However, there seems to be a lack of exploration of the topic, where the photovoltaic inverter could provide a flexible energy source while maintaining the rigor of power quality. This article presents current developments in low-voltage grids and the prospect of using prosumer installations to provide grid flexibility and stability.