Real-Time Adaptive Energy Management in Renewable Energy Communities: Reducing the Challenge of Forecasting and Prior Knowledge Dependencies

The integration of renewable energy (RE) sources and the increasing complexity of energy management (EM) systems have resulted in considerable advances in energy usage optimization in smart renewable energy communities (RECs). Existing methods rely on forecasting techniques that necessitate accurate projections of future energy prices, RE generation, user comfort, and behavior. While these strategies are effective in controlled contexts, they have limitations in dynamic, real-time scenarios where system inputs fluctuate unexpectedly. Using prior knowledge or forecasts might result in computational cost in real-time energy optimization. This study proposes a novel approach to real-time adaptive EM in RECs that overcomes the need for prior knowledge and the overhead of forecasting future buildings' EM systems. To tackle the uncertainties in system input dynamics (i.e., RE generation process, battery storage, load arrival processes, demand, and dynamic pricing), this study presents a one-slot-look-ahead queue-based Lyapunov optimization framework. This approach allows for real-time EM systems and reduces user discomfort in smart buildings that are linked and connected to the smart grid. The main goal is to reduce the average running costs (of procurement and operations) by optimizing the real-time scheduling of both electrical/thermal loads and electrical/thermal storage systems, managing their degradation and life cycle, and ensuring indoor user comfort. The optimization challenge is reduced to smaller sub-problems that can be solved one after the other in real time and are asymptotically optimal. They are particularly effective for real-time use in REC settings, especially when the input statistics are either unknown or highly variable. Simulation results under different scenarios and weather conditions, on both a daily and a monthly basis, indicate that the proposed method leads to average reductions in daily and monthly running costs of up to 13.53% and 19.37%, respectively, when benchmarked against other recent research, which reports similar decreases in the same scenarios.