Two-stage dynamic management in energy communities using a decision system based on elastic net regularization

Energy communities are undergoing a groundbreaking transformation. With the increasing adoption of renewable energy sources and energy storage systems, integrating advanced technologies is essential to address challenges such as managing production variability and balancing consumption. Here, artificial intelligence proves indispensable, offering innovative solutions to enhance efficiency and sustainability.

Recurrent neural networks, such as LSTMs, enable precise forecasting of renewable energy production and load consumption. This approach anticipates seasonal and daily variations, improving the operational management of distributed resources. A practical example: in an energy community in Southern Italy, which includes 11 loads and 3 distributed generators such as wind turbines and photovoltaic systems, consumption and production profiles were predicted with an average error margin of less than 10%. Simulations included complex scenarios, such as variations in photovoltaic generation during winter, demonstrating that even under adverse weather conditions, the model quickly adapts while maintaining reliable forecasts.

The integration of the Elastic Net methodology in energy management has introduced unprecedented efficiency levels. Using regularization parameters that balance accuracy and efficiency, the system optimizes the operation of storage systems, generators, and loads. In tests conducted in June and December, cumulative imbalances were reduced from 11.5 MWh to 5.5 MWh and from 12.1 MWh to 10.3 MWh, respectively. This result was achieved through careful load management without compromising user comfort. Additionally, batteries were strategically deployed to minimize sudden variations in the grid. The optimization system limits excessive battery use, reducing the risk of premature wear and ensuring optimal operational lifespan.

The results are tangible: in tests conducted on real data, the system significantly reduced grid imbalances by using storage systems strategically and minimizing reliance on unsustainable energy sources. Moreover, thanks to short-term forecasting, resources such as wind turbines, photovoltaic systems, and electric vehicle charging stations were effectively integrated. This allowed users to actively participate in energy management through economic incentives and reduced operational costs.

This technology not only enhances the resilience of local grids but also serves as a replicable paradigm in both urban and rural contexts. The combination of AI, Elastic Net, and LSTM networks offers a scalable model for the future of smart grids, where sustainability and innovation go hand in hand.