Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning

Gengsheng He (), Yu Huang, Guori Huang, Xi Liu, Pei Li and Yan Zhang
Additional contact information
Gengsheng He: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Yu Huang: Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Guori Huang: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Xi Liu: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Pei Li: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China
Yan Zhang: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China

Energies, 2024, vol. 17, issue 15, 1-20

Abstract: Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.

Keywords: virtual power plant; distributed energy resources; flexibility; zero-carbon index; self-organized aggregation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/15/3688/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/15/3688/ (text/html)

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:gam:jeners:v:17:y:2024:i:15:p:3688-:d:1443552

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().