Performance evaluation for optimization of energy trading mechanisms using solar DC microgrid

Using low-voltage direct current (LVDC), such as 48 V DC, provides a practical solution for expanding electricity access to remote and underserved areas. It supports small-scale power use for essential applications like lighting and communication, including mobile phone charging and low-power devices. To improve the reliability and efficiency of these systems, this study employs a smart DC microgrid architecture that incorporates automatic load shedding and demand response features to enhance power management and control. This study proposes a smart DC microgrid architecture that enhances the reliability and efficiency of such systems through autonomous power management and peer-to-peer energy trading. The core contribution is the development and experimental validation of a multi-agent system (MAS) that integrates two key functionalities: (1) an automatic load-shedding algorithm for demand response, and (2) a real-time energy trading mechanism. The study implements and evaluates a functional prototype in the JADE and REPAST simulation environments to assess technical and operational performance. The key quantitative results showed that the smart DC microgrid achieved an operational efficiency of 92.6%, with responsive control latency below 150 milliseconds during communication between sensing and relay units. The automatic load-shedding algorithm reduced total demand by 28% during peak hours, boosting stability and preventing overloads. Additionally, the multi-agent system enabled real-time energy trading, allowing non-critical loads to return up to 15% of excess power to the grid. Bandwidth use stayed below 30%, confirming efficient data transmission and scalability. These results confirm that a multi-agent-based smart DC microgrid is a technically viable and efficient approach for decentralized energy access. The study advances research in distributed energy systems by demonstrating the potential of models for autonomous control, adaptive load management, and decentralized energy trading in LVDC systems.