A two-stage decision system based on distributed optimization in energy communities

Today, more efficient energy systems are necessary to address global climate change and the scarcity of fossil fuel supplies. Energy communities and consumer-shared ownership of renewable energy resources are essential cornerstones of the overall success of the energy transition. This article proposes a new management framework for sustainable energy communities to reduce the mismatch between internal power generation and demand, the so-called imbalance. Proposals on this subject have already been made in the literature, primarily based on static optimization models or multi-step procedures that employ machine learning algorithms. Based on contributions from both techniques, an innovative decision-making model with a first forecasting phase and a second optimization phase is provided. It relies on deep neural networks tailored to forecast the generation and consumption profiles. Then, the energy dispatch efficiency is improved by employing a distributed optimization model to address issues in the traditional centralized framework, including high communication requirements, substantial computational burden, and limited scalability. With respect to previous studies by the authors on the same matter, the methodology's effectiveness is improved here in both the prediction and optimization stages, using more robust and accurate neural networks that enable a more fault-tolerant distributed architecture and increased scalability of the overall management system. To test the accuracy and robustness, the proposed procedure was applied to real measured data from the energy cluster at the University of California, San Diego (USA). The performed tests assess a percentage reduction in the imbalance between the original and optimized management systems, ranging from 40% to 50%, with an increase compared to previous published methodologies of more than 10%.