Abstract

In this brief, the online energy management problem of a grid-connected microgrid is studied. The considered microgrid is a typical system that consists of renewable energy generations (RG), local co-generations with combined heat and power supply, the electricity and heat energy storage systems (ESS) (e.g., battery and thermal tank), and the centralized power grid (PG), as well as the external natural gas station. This brief aims to minimize the aggregate microgrid’s operation cost by formulating it as a stochastic nonconvex optimization programming, which is challenging to solve optimally due to the coupling feature of energy storage devices. First, by investigating the unique structure of the formulated optimization problem, we relax the constraints and transform the original nonconvex stochastic optimization problem into a convex optimization programming. Furthermore, we tackle the problem by developing a modified Lyapunov optimization approach and design an online energy management algorithm that does not require any statistic information of the random system inputs (e.g., the purchasing price from the PG, the harvesting electricity from the RG, and the charging levels of the ESS, and so on). Moreover, extensive empirical evaluations using real-world traces are performed to study the effectiveness of the proposed algorithm in practice. Our proposed algorithm can reduce 27.5% of the aggregate cost compared with the benchmark approach.

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