Real-Time Energy Management in Microgrids: Integrating T-Cell Optimization, Droop Control, and HIL Validation with OPAL-RT

Achraf Boukaibat (), Nissrine Krami, Youssef Rochdi, Yassir El Bakkali, Mohamed Laamim and Abdelilah Rochd
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Achraf Boukaibat: National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco
Nissrine Krami: National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco
Youssef Rochdi: National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco
Yassir El Bakkali: National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco
Mohamed Laamim: Research Platform in Solar and Renewable Energies, Green Energy Park, Benguerir 43150, Morocco
Abdelilah Rochd: Research Platform in Solar and Renewable Energies, Green Energy Park, Benguerir 43150, Morocco

Energies, 2025, vol. 18, issue 15, 1-19

Abstract: Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids.

Keywords: real-time energy management; T-Cell optimization; Hardware-in-the-Loop (HIL); droop control; Multi-Agent System (MAS); OPAL-RT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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