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Abstract-Decentralized inverter control is essential in distributed generation (DG) microgrids for low deployment/operation cost and high reliability. However, decentralized inverter control suffers from a limited system stability mainly because of the lack of communications among different inverters. In this paper, we investigate stability enhancement of the droop based decentralized inverter control in microgrids. Specifically, we propose a power sharing based control strategy which incorporates the information of the total real and reactive power generation of all DG units. The information is acquired by a wireless network (such as a WiFi, ZigBee, and/or cellular communication network) in a decentralized manner. Based on the desired power sharing of each DG unit and the acquired information of total generation, additional control terms are added to the traditional droop controller. We evaluate the performance of the proposed control strategy based on smallsignal stability analysis. As timely communication may not be established for a microgrid with low-cost wireless communication devices, two kinds of analytical models are developed with respect to negligible and nonnegligible communication delays, respectively. Extensive numerical results are presented to demonstrate the system stability under the proposed control strategy with respect to different communication delays.

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

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