Design of an Intelligent System for Controlling and Balancing Renewable Energy Flows in an Autonomous Micro-Grid
Keywords:Micro-grid, sensor network, flow balancing, microcontroller, intelligent system, IoT, 5G
Pooling different renewable energy sources (hydrogen, solar, wind, geothermal, etc.) enables developing a standalone energy micro-grid. The energy flows from these various sources are neither constant nor equivalent. Therefore, control and balancing mechanisms should be established for optimal energy utilization through an intelligent system based on interconnected microcontrollers networked with sensors. Our contribution addresses this issue by proposing an original architecture of an intelligent and distributed control system based on a sensor network and a strategy to share the electric power through the micro-grid. In our work we consider a micro-grid powered by sources of wind turbine, pv panels and battery which energy flows are controlled and balanced through our system depending on power demand of the loads. Alternating Current (AC) bus and Direct Current (DC) bus are tied together by an inverter. A set of microcontroller-sensor-actuators (which we named S.A.D for Sensor/Actuator Device) are deployed at strategic points in the micro-grid providing constantly data from power generated and consumed, equipment health and status. A control algorithm developed in relation to a network control strategy is implemented by combining the performance different microcontroller boards. Relying on existing literature works, a review of solution approaches to the challenging problem, of the power flows balancing between the different energy sources and storage batteries embedding appropriate IoT technologies and exploiting energy big-data platforms, is presented
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Assilevi K, Ajavon A, Adjallah K. Design of a virtual private network of intelligent microcontrollers for distributed control of an autonomous micro-grid of renewable energy harvesters. in the ICEASSM’2019 Proc., the 2nd Int. Conf. on Engineering, Applied Sciences and System Modeling (), 2019.
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