Enhancing fairness and efficiency in community energy systems: A forecast-driven approach

The integration of renewable energy sources and community energy storage systems offers a transformative solution for enhancing grid resilience and reducing reliance on fossil fuels. However, ensuring fair and efficient energy allocation among prosumers—individuals who both produce and consume energy—remains challenging, particularly in communities with diverse behavioral patterns. This article addresses these challenges by proposing a mechanism that facilitates equitable energy sharing. It combines long short-term memory (LSTM) forecasts of prosumer energy demand and generation profiles with a relative contribution-based mechanism to manage of a shared battery energy storage system. This mechanism dynamically allocates energy based on a prosumer’s contribution index, which reflects their energy input into the system relative to their demand. A proportional acceptance factor is also introduced to ensure equitable participation in energy allocation, especially when storage capacity is limited. Experimental results demonstrate that integration of forecasting significantly enhances prosumer contributions, reduces disparities in energy access, and optimizes battery charging and discharging cycles. The integration of LSTM forecasting improves the overall fairness and efficiency of the energy management system. This study shows that the proposed mechanism fosters optimal collaboration among prosumers, particularly during high-demand periods or supply shortages. By aligning energy access with individual contributions and accounting for the dynamic nature of prosumers’ behavior, the approach supports energy justice and improves the reliability of battery energy storage systems in the community. These findings suggest that such mechanisms could play a pivotal role in future smart grid systems. • Dynamic relative contribution index and proportional acceptance factor ensure equitable access to community energy storage. • Fair energy allocation mechanism integrates LSTM-based forecasts of prosumer demand and generation. • Relative contributions improve collaboration among prosumers, especially under cases of high demand or low supply. • Forecasting supports energy justice principles by enhancing BESS utilization, increasing renewable energy self-consumption, and reducing short-term disparities in energy access.