Smart Scheduling Strategy for Islanded Microgrid Based on Reinforcement Learning Algorithm

Author(s): Lingxiao Gana*, Tao Yua, Jing Lia, Jie Tangb
a Electric Power College, South China University of Technology, Guangzhou 510640, China
b Shaoguan Power Supply Bureau of Guangdong Power Grid Corporation, Shaoguan 512026, China
International Journal of Smart Grid and Clean Energy, vol. 1, no. 1, September 2012: pp. 122–128
ISSN: 2315-4462
Digital Object Identifier: 10.12720/sgce.1.1.122-128


Abstract: This paper investigates a hierarchical Automatic Generation Control (AGC) strategy for an islanded microgrid, including wind power, solar photovoltaic, micro turbines, small hydropower and energy storage devices. The upper AGC is for central scheduling. The bottom AGC is to optimize the allocation factors, expecting to meet the requirement of energy-saving generation dispatching (ESGD). Three different bottom controllers are presented. Two of them are designed based on reinforcement learning (RL) algorithm. In order to evaluate their control performance, another proportion-based (PROP) controller which has been put into practical application is also presented. Detailed dynamic models of distributed generations and loads are built to simulate the microgrid. System responses to wind turbine tripping and to large load disturbances are tested. The results indicate that the proposed strategy based on RL algorithm can not only achieve reliability and stability of microgrid in islanded mode, but also reduce fossil energy consumption. This approach is a possible candidate for future microgrid control approaches.

Keywords: Distributed generation; islanded microgrid; hierarchical AGC; reinforcement learning algorithm

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