A Hybrid Decentralized Optimization Approach for User-Agnostic Energy Communities

Energy communities are considered a promising model for enhancing local renewable energy management. Centralized Home Energy Management Systems (HEMSs) have demonstrated significant potential in improving energy sharing between prosumers. However, centralized schemes raise privacy concerns, due to potential user profiling, which could hinder their acceptance. To address this, decentralized alternatives that improve energy sharing while preserving privacy are needed, possibly leveraging open-source home automation solutions. This work proposes and compares four different optimization algorithms for implementation in a HEMS. Two of these algorithms are designed for application in energy communities, while the other two serve as benchmarks representing existing technologies. The focus of this research is to preserve user privacy, with one of the algorithms employing a hybrid approach: a genetic algorithm for central optimization combined with local linear optimization performed by prosumers themselves. The results show that, while the centralized approach, ideally with complete a full knowledge of household devices and consumptions, could achieve the greatest cost reduction for the community, the proposed hybrid approach offers a favorable trade-off, reducing costs compared to benchmark models while preserving end-user privacy, making it more acceptable to users, offering potential economic and environmental benefits by enhancing renewables and local energy sharing.