Research on distributed cooperative optimisation control strategy for active distribution network based on combine-then-adapt diffusion algorithm

Considering the problem of cooperative operation of highly intermittent renewable generations (photovoltaic and wind power) and flexible load in active distribution network, a distributed cooperative optimisation operation strategy based on combine-then-adapt diffusion algorithm is proposed in this...

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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) Vol. 2019; no. 16; pp. 1911 - 1917
Main Authors: Wu, Lizhen, Jiang, Libo, Hao, Xiaohong, Zheng, Tianwen
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01-03-2019
Wiley
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Summary:Considering the problem of cooperative operation of highly intermittent renewable generations (photovoltaic and wind power) and flexible load in active distribution network, a distributed cooperative optimisation operation strategy based on combine-then-adapt diffusion algorithm is proposed in this study. Distributed agents of distributed generators are connected by the distributed sparse network through distributed hierarchical control method. As an agent node of networked system, it interchanges information with its neighbours. In order to make the distribution generator meet the equal incremental cost principle, the adaptive adjacency matrix and fusion matrix with the weighted factor are introduced to update the state value of nodes to obtain the same convergence value. In this strategy, the coordinated source and load is applied by the active power optimisation dispatching for active distribution network. Stimulate analysis and experimental results show that the proposed combine-then-adapt diffusion algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Finally, the simulation analysis and experimental results of IEEE14-bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2018.8821