Distributed Fixed-Time Optimal Resource Management for Microgrids - Liu, Li-Ning; Yang, Guang-Hong (IEEE transactions on automation science and engineering, 2023)
This article studies the problem of distributed cooperative resource management for Microgrids. In order to maximize the social welfare, a distributed fixed-time consensus algorithm is first proposed to integrate economic dispatch and demand response, which optimally assigns the energy between generation participants and load participants. Based on the designed algorithm, each participant is able to quickly obtain its optimal operation and only requires partial calculation and communication without a central control coordination, thus it offers high flexibility, strong robustness, and better privacy. Moreover, compared with the existing distributed optimization algorithms, the proposed algorithm simultaneously possesses the advantages of fixed-time consensus, no initialization conditions, and supporting switching topology, etc. Furthermore, it is proved that each participant can achieve consensus in a fixed-time manner and converge to the global optimal point if the parameters of the algorithm satisfy some conditions. Finally, numerical simulations in the 6-Bus and the IEEE 30-Bus systems are provided to validate the effectiveness of the presented algorithm. Note to Practitioners-This article is motivated by the problem of distributed cooperative resource management for Microgrids to make the system economical and safe operation. The existing distributed methods only converge in an infinite time horizon. However, asymptotic convergence is undesirable in practice, especially in multiperiod optimisation problems. To address this issue and integrate more distributed resources in the future, in this article, we first propose a distributed fixed-time consensus method, where each participant can achieve consensus in a fixed time and obtain its optimal operation locally. Moreover, the proposed method is robust to communication failures and adaptive to topology changes. Our future work will aim at considering more practical constraints, such as power flow constraints, ramping rate constraints, etc.