Hierarchical learning optimisation method for the coordination dispatch of the inter-regional power grid considering the quality of service index

Kai lv*, Hao Tang, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Qi Tan, Qianli Zhang

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The economic dispatch of the inter-regional power grid with multiple uncertain sources and loads is focused in this study. As the tie-line can transmit the power between regional grids, a coordination mechanism between the tie-line power schedule and the economic dispatch of regional power grids is built to solve the focused dispatch problem under the uncertain environment. Furthermore, the quality of service (QoS) method is introduced in this study to consider and study the service attribute in the power dispatch process, which is quantified by the responding behaviour of the price-sensitive consumers. Then, a model-free hierarchical optimisation method based on the learning technique is designed. The hierarchical structure consists of two levels and multiple agents, where the agents learn knowledge from the interaction between themselves and the environment. An improved reinforcement learning algorithm is adopted to find the optimal dispatch policy for each agent, which realises an online optimisation with the operation samples rather than the support of an accurate system model. Finally, the simulation results are shown to validate the effectiveness of the designed method. Specifically, the optimisation process and the obtained dispatch policies are analysed, and the impact of the QoS index on the optimisation is introduced.
Original languageEnglish
JournalIET Generation, Transmission & Distribution
Volume14
Issue number18
Pages (from-to)3673-3684
Number of pages12
ISSN1751-8687
DOIs
Publication statusPublished - Sep 2020

Fingerprint Dive into the research topics of 'Hierarchical learning optimisation method for the coordination dispatch of the inter-regional power grid considering the quality of service index'. Together they form a unique fingerprint.

Cite this