Multi-Agent System Based Energy Management Controller for Microgrids

Date

2018-05

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Abstract

The increasing integration of distributed renewable energy sources and electric vehicles can significantly impact the power generation and energy consumption profile of the grid. To control these distributed sources and loads, an efficient energy management controller is required. However, the stochastic nature of these sources and loads increase the complexity of the energy management (EM) controller. In addition, the EM scheme must address the self-centered nature of the consumers and must be resilient towards both controller and power system faults that can cause power interruption. This thesis proposes a real-time hybrid energy management controller for a microgrid that address these issues. The proposed hybrid controller architecture is developed based on Multi-Agent System (MAS) strategy. The proposed energy management technique is based on coordinated optimization scheme. This optimization scheme is implemented using distributed gradient descent methodology using agent communication. A novel Peer-to-Peer communication strategy termed “Interchangeable Sender-Receiver behavior” (ISR) strategy is implemented for the agent communication. Furthermore, based on this ISR strategy a self-healing scheme is developed with the combination of sentinel agent approach and workload distribution technique to improve the resiliency of the EM controller during controller failure scenarios. In addition to the resiliency, the EM controller is implemented with an acquisitive optimization problem which includes a penalty function for power interruption. This scheme is tested for different controller failure scenarios. Results verify the robustness of the EM controller and the optimization values. Furthermore, this EM controller scheme is extended for EV to grid integration in a workplace microgrid system. The EM scheme is developed by considering economic benefits to both the EV owner and the workplace. Here, the EM scheme provided an optimized schedule for charging and discharging the EV battery. The optimized schedule is derived from individual EV travel pattern forecasting. The proposed scheme is verified with real-world data and in a real-time simulation testbed. The real-time simulation testbed is constructed by interfacing two Typhoon HIL units to Real Time Digital Simulator (RTDS). The controller is interfaced to the real-time simulation testbed using Ethernet (Socket) protocol. The entire controller architecture is tested in real-time and the results are verified.

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Keywords

Electric vehicles (EV), Energy Management (EM), Multi-agent system (MAS)

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