Tomislav Capuder

Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting.

Contrary to microgrids (MGs) for which grid code or legislative support are lacking in the majority of cases, energy communities (ECs) are one of the cornerstones of the energy transition backed up by the EU’s regulatory framework. The main difference is that, unlike MGs, ECs grow and develop organically through citizen involvement and investments in the existing low-voltage (LV) distribution networks. They are not planned and built from scratch as closed distribution systems that are independent of distribution system operator plans as assumed in the existing literature. An additional benefit of ECs could be the ability to transition into island mode, contributing to the resilience of power networks. To this end, this paper proposes a three-stage framework for analyzing the islanding capabilities of ECs. The framework is utilized to comprehensively assess and compare the islanding capabilities of ECs whose organic development is based upon three potential energy vectors: electricity, gas, and hydrogen. Detailed dynamic simulations clearly show that only fully electrified ECs inherently have adequate islanding capabilities without the need for curtailment or additional investments.