A new hybrid AI optimal management method for renewable energy communities

Abstract

In this study, we propose a hybrid AI optimal method to improve the efficiency of energy management in a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Network to forecast the future values of the energy features in the community. Then, these forecasts are used by a stochastic Model Predictive Control to optimize the community operations with a proper control strategy of Battery Energy Storage System. The results of the predictions performed on a public dataset with a prediction horizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation, total energy consumption, and common services, respectively. The model predictive control fed with such predictions generates maximum income compared to the competitors. The total income is increased by 18.72% compared to utilizing the same management system without exploiting predictions from a forecasting method.

Keywords

• Artificial Intelligence
• Deep learning
• Renewable Energy Community
• Battery Energy Storage System management
• Model Predictive Control