Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties

Mingshan Mo, Xinrui Xiong, Yunlong Wu and Zuyao Yu ()
Additional contact information
Mingshan Mo: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Xinrui Xiong: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yunlong Wu: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zuyao Yu: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2023, vol. 16, issue 22, 1-18

Abstract: A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios.

Keywords: demand response uncertainty; deep reinforcement learning; community-integrated energy system; low-carbon economic dispatch (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/22/7669/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/22/7669/ (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:16:y:2023:i:22:p:7669-:d:1283898

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 ().