Initialization-free distributed prescribed-time consensus based algorithm for economic dispatch problem over directed network

Ji, Lianghao; Yu, Linhua; Zhang, Cuijuan; Guo, Xianping; Li, Huaqing

doi:10.1016/j.neucom.2023.02.024

Cited by 4 publications

(1 citation statement)

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“…When a single control algorithm cannot meet the control requirements, multiple control methods can also be used. In [39], a distributed economic scheduling algorithm was proposed based on event triggering and fixed time consistency for optimal scheduling between generators in smart grids.…”

Section: Introductionmentioning

confidence: 99%

Economic dispatch of multi-microgrids considering flexible load based on distributed consensus algorithm

Wu,

Li,

et al. 2024

Preprint

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This paper investigates the economic dispatch (ED) problem of multi-microgrids considering the flexible loads based on distributed consensus algorithm. Firstly, the structure topology diagram of distributed generator nodes is designed based on the global interconnection of multi-microgrids, then the flexible load is considered as adjustable load and be added into the fixed loads. Next, a multi-microgrids economic dispatch (MMED) model which contained fixed and flexible loads is constructed with the goal of minimizes the operating cost of the entire system. The proposed MMED model is a two-layer coordinated optimization model. The power transmission cost between each microgrid is used as the multi-microgrids layer framework, and the internal operating costs of a single microgrid is the sub-microgrid layer framework. The distributed consensus algorithm is designed to solve the MMED problem with the considering of constraint condition of distributed generators in each microgrid. Finally, some examples illustrate that the proposed method can not only optimize the operation of each single microgrid, but also maintains the stability of multi-microgrids through the adjustment of flexible load, and reduces the total operating cost of the whole multi-microgrids system.

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Section: Introductionmentioning

confidence: 99%