Distributed Optimal Scheduling of VPP based on EST: An ADMM algorithm based on historical data online transfer

In order to realize the precise control of virtual power plant (VPP) over its internal demand-side resource (DR) clusters with dispersed locations and huge numbers, and realize the VPP’s rapid and effective participation in demand response, in the VPP hierarchical control framework, the multi-edge s...

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Vydáno v:Applied energy Ročník 347; s. 121416
Hlavní autoři: Sun, Yufei, Liu, Xinrui, Wang, Rui, Dong, Chaoyu, Sun, Qiuye
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2023
Témata:
ADMM
algorithms
Control priority
energy
multipliers
Online transfer
power plants
Pre-learning
VPP
Pre-learning
Online transfer
Control priority
ADMM
VPP
ISSN:0306-2619
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Shrnutí:In order to realize the precise control of virtual power plant (VPP) over its internal demand-side resource (DR) clusters with dispersed locations and huge numbers, and realize the VPP’s rapid and effective participation in demand response, in the VPP hierarchical control framework, the multi-edge service terminal (MEST) is introduced as the distributed processor of the VPP to decompose and execute the control task. Firstly, the pre-learning method is used to enrich the historical data, and the historical data is clustered based on the spectral clustering. The distributed cooperative control strategy of MEST and the alternating direction multiplier method (ADMM) of historical data online transfer are proposed, which greatly reduces the number of iteration steps. Then, based on the results of the power adjustment task of the MEST, the hierarchical relationship of control priorities is divided, such as the control mode, comfort tolerance value, switch controllable amount and tolerance value, which can reduce the power adjustment cost of VPP and improve user’s comfort. Finally, the effectiveness of the proposed algorithm and control strategy in executing distributed optimization tasks is proved by simulation experiments. The convergence time of the proposed algorithm is only 32.7% of that of the ADMM and the consensus algorithm. •Comprehensive consideration the cost of power adjustment and device start-stop times.•Improve the relevance of online transfer data through pre-learning.•Historical data online transfer is used to improve initial state of ADMM algorithm.•Precise control of VPP resources is realized by dividing device control priorities.•The convergence time of the proposed algorithm is only 32.7% of other two algorithm.
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ISSN:0306-2619
DOI:10.1016/j.apenergy.2023.121416