Dataset of "Modeling and Optimization of Photovoltaic-Based Energy Communities Using a Gradient Descent Algorithm: A Case Study in a Town of Czechia"

This study aimed to design and validate a comprehensive mathematical optimization algorithm incorporating machine learning principles to optimize the structure of community energy systems. The primary objective of the methodology is to support the effective decentralization of the energy sector by enabling the design and operation of community energy projects that are both energy-efficient and economically viable. The proposed algorithm was validated using a representative case study of a small Czech municipality comprising several key public buildings, selected based on their typical electricity consumption characteristics. The input dataset consisted of standardized annual electricity load profiles, provided by the Czech electricity market operator, which were used to substitute for real-time consumption data of individual community members. These standardized profiles are derived from statistically aggregated national consumption measurements. Although real smart metering data were not yet available at the time of analysis, the methodology is designed to be scalable and applicable to various operational models and energy community sizes. Future refinement and validation will be conducted using real-time consumption data following the nationwide rollout of Advanced Metering Management (AMM) in the Czech Republic.