Multi-Stage Centralized Energy Management for Interconnected Microgrids: Hybrid Forecasting, Climate-Resilient, and Sustainable Optimization

Mohamed Kouki¹, Nahid Osman², Mona Gafar³, Ragab A. El-Sehiemy^4,5,6,*
1 Department of Scientific Systems, University of Toulouse, University of Technology Tarbes-UTTOP, LGP, Tarbes, 65000, France
2 Department of Physics, College of Science and Humanities Studies, Prince Sattam Bin Abdulaziz University, Kharj, 16278, Saudi Arabia
3 Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Kharj, 16278, Saudi Arabia
4 Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt
5 Department of Electronic Engineering, Higher Institute of Engineering and Technology at Manzala, Manzala, 66412, Egypt
6 Sustainability Competence Centre, Szechenyi Istvan University, Egyetem tér 1., Győr, H9026, Hungary
* Corresponding Author: Ragab A. El-Sehiemy. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.071964

Received 16 August 2025; Accepted 10 November 2025; Published online 15 December 2025

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Abstract

The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO₂ emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address these issues, this paper presents a novel three-stage centralized EMS for interconnected microgrids. The first stage involves comprehensive data analysis to extract meaningful patterns. The second stage introduces a hybrid forecasting framework that integrates stochastic (Prophet) with machine learning (BiLSTM) techniques to improve prediction accuracy under uncertainty. In the third stage, a modified linear programming approach leverages the improved short-term forecasts to optimize energy sharing between microgrids, with the aim of reducing operational costs, minimizing carbon emissions, and improving system stability under climate variability. The proposed EMS is designed to accommodate diverse microgrid configurations while maintaining computational efficiency. Four scenarios are considered to evaluate the proposed energy management strategy. The obtained results demonstrate that the proposed EMS significantly improves both forecasting accuracy and operational performance. The combined methods achieve the best performance among all tested models, with an RMSE of 0.0070, MAE of 0.0043, and R² of 0.9988, corresponding to improvements of ΔRMSE = −0.2122 and ΔR² = +0.7126 relative to Prophet. These substantial gains in predictive accuracy translate into more precise battery scheduling, reduced grid dependency, and optimized power dispatching, thereby significantly enhancing system efficiency, reliability, and sustainability. Overall, the results highlight the effectiveness of integrating hybrid forecasting with optimization-based EMS, providing a viable pathway toward high penetration of renewable energy sources in future power systems.