DANKIR, SARA (Institut de Robòtica i informàtica Industrial (CSIC-UPC), Carrer), Puig, Vicenç (Universitat Politècnica de Catalunya (UPC)), LASRI, rAFIK (TED:AEEP, FPL, Abdelmalek Essaadi University, Tetouan 93000), MAATAOUI, YASSIR (TED:AEEP, FPL, Abdelmalek Essaadi University, Tetouan 93000), CHEKENBAH, HAMID (TED:AEEP, FPL, Abdelmalek Essaadi University, Tetouan 93000)

Empowering Microgrid Energy Management with AI and Model Predictive Control

Scheduled for presentation during the Regular Session "Sliding mode and predictive control of power converters" (ThuS2T5), Thursday, July 11, 2024, 13:10−13:30, Session room 5

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

This paper presents an innovative approach to microgrid energy management by integrating Model Predictive Control (MPC) with Artificial Intelligence (AI), focusing on the application of Long Short-Term Memory (LSTM) networks for load forecasting. We show that AI-enhanced MPC can significantly improve the efficiency and reliability of microgrid energy management. The fundamental results of the LSTM models highlight the effectiveness of our methodology in improving predictive accuracy and operational performance.