Energy storage systems are an effective solution to manage the intermittency of renewable energies, balance supply, and demand. Numerous studies recommend adopting a shared energy storage system (ESS) as opposed to multiple single ESSs because of their high prices and inefficiency. Thus, this study examines a shared storage system in a grid-connected microgrid. By modifying the power outputs of the energy resources, this work intends to implement an economic dispatch of a shared ESS in order to satisfy the power balance and lower the overall cost of electricity. In this context, an optimization problem was formulated and developed using a mixed-integer linear programming (MILP) model. Furthermore, a pilot project (the Solar Decathlon Africa Village) in the Green & Smart Building Park (GSBP), Benguerir, Morocco, was employed to evaluate and verify the proposed approach. Some comparable scenarios were therefore run in a MATLAB environment. The collected findings demonstrate the efficacy of the developed algorithm in terms of optimizing energy cost reductions and enhancing the integration of renewable resources into the Moroccan energy mix.

This article proposes an Energy Management System (EMS) for smart microgrids with a decentralized multi-agent system (MAS) based on a bio-inspired T-Cell optimization algorithm. The proposed system allows real-time control and dynamic balancing of loads while addressing the challenges of intermittent renewable energy sources like solar and wind. The system operates within the tertiary control layer; the optimal set points are computed by the T-Cell algorithm across energy sources and storage units. The set points are implemented and validated in real-time by the OPAL-RT simulation platform. The system contains a real-time feedback loop, which continuously monitors voltage levels and system performance, allowing the system to readjust in case of anomalies or power imbalances. Contrary to classical methods like Model Predictive Control (MPC) or Particle Swarm Optimization (PSO), the T-Cell algorithm demonstrates greater robustness to uncertainty and better adaptability to dynamic operating conditions. The MAS is implemented over the JADE platform, enabling decentralized coordination, autonomous response to disturbances, and continuous system optimization to ensure stability and reduce reliance on the main grid. The results demonstrate the system’s effectiveness in maintaining the voltages within acceptable limits of regulation (±5%), reducing reliance on the main grid, and optimizing the integration of renewable sources. The real-time closed-loop solution provides a scalable and reliable microgrid energy management solution under real-world constraints.

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.