A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning

Lei Dong, Hao Lin, Ji Qiao, Tao Zhang, Shiming Zhang and Tianjiao Pu

Applied Energy, 2024, vol. 373, issue C, No S0306261924012534

Abstract: As a promising approach to managing distributed energy, the use of microgrids has attracted significant attention among those managing continuous connections to distribution networks. However, the barriers of the data sharing among different microgrids, the uncertainty of the distributed renewable sources and loads, and the nonlinear optimization of power flow make traditional model-based optimization methods difficult to be applied. In this paper, a data-driven coordinated active and reactive power optimization method is proposed for distribution networks with multi-microgrids. A multi-agent deep reinforcement learning (MADRL) method is used to protect the data privacy of each microgrids. Moreover, attention mechanism, which pays attention to crucial information, is presented to overcome the problem of slow convergence caused by the dimensionality explosion of the optimized variables. Two types of agents, controlling discrete action and continuous action devices, respectively, are formulated in coordinated optimization, which reduces voltage violations and improves the system operation efficiency. In addition, in order to improve the performance of the online agent model under variable operation conditions, the transfer learning is embedded in the training process of the MADRL. The proposed method is verified on a modified IEEE 33-bus distribution network with nine microgrids.

Keywords: Distribution networks with multiple microgrids; Coordinated active and reactive power optimization; Attention mechanisms; Multi-agent deep reinforcement learning; Transfer learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924012534
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:373:y:2024:i:c:s0306261924012534

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123870

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().