LAPSE:2023.26412v1
Published Article
LAPSE:2023.26412v1
Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow
Huazhen Cao, Chong Gao, Xuan He, Yang Li, Tao Yu
April 3, 2023
Abstract
This paper builds an optimal carbon-energy combined-flow (OCECF) model to optimize the carbon emission and energy losses of power grids simultaneously. A novel multi-agent cooperative reduced-dimension Q(λ) (MCR-Q(λ)) is proposed for solving the model. Firstly, on the basis of the traditional single-objective Q(λ) algorithm, the solution space is reduced effectively to shrink the size of Q-value matrices. Then, based on the concept of ant cooperative cooperation, multi-agents are used to update the Q-value matrices iteratively, which can significantly improve the updating rate. The simulation in the IEEE 118-bus system indicates that the proposed technique can decrease the convergence speed by hundreds of times as compared with conventional Q(λ), keeping high global stability, which is very suitable for dynamic OCECF in a large and complex power grid compared with other algorithms.
Keywords
multi-agent cooperation, optimal carbon-energy combined-flow, reduced-dimension Q(λ)
Suggested Citation
Cao H, Gao C, He X, Li Y, Yu T. Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow. (2023). LAPSE:2023.26412v1
Author Affiliations
Cao H: Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China
Gao C: Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China
He X: Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China
Li Y: Power Grid Planning Center of Guangdong Power Grid Co., Ltd., Guangzhou 510640, China
Yu T: School of Electric Power, South China University of Technology, Guangzhou 510640, China [ORCID]
Journal Name
Energies
Volume
13
Issue
18
Article Number
E4778
Year
2020
Publication Date
2020-09-14
ISSN
1996-1073
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PII: en13184778, Publication Type: Journal Article
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LAPSE:2023.26412v1
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