Knowledge-network-embedded deep reinforcement learning: An innovative way to high-efficiently develop an energy management strategy for the integrated energy system with renewable energy sources and multiple energy storage systems

Bin Jia, Fan Li and Bo Sun

Energy, 2024, vol. 301, issue C

Abstract: To achieve efficient energy management in complex integrated energy systems (IESs) with renewable energy sources (RESs) and multiple energy storage systems (ESSs), the study aims to propose a novel approach. Evolutionary-based methods are difficult to find the optimal scheme, while deep reinforcement learning (DRL)-based methods face problems with low training efficiency. To address the limitations, a knowledge-network-embedded DRL method is proposed, which combines the fast-searching capability of genetic algorithm (GA) with the environment-aware decision-making capability of DRL, with the aim of developing a highly efficient energy management strategy in improving user comfort and reducing operating costs. GA is used to solve the IES optimization model for obtaining a solution set that serves as domain knowledge, and a knowledge network is constructed using deep learning (DL). Then, DRL uses this network as an actor network to explore the optimal strategy. The use of GA helps the DRL to enhance training efficiency and find an optimal strategy. Simulations show significant improvements in cost reduction (5.7 %) and user comfort (4.2 %), along with improved training efficiency (58.3 %) compared to baseline DRLs.

Keywords: Integrated energy systems; Energy management; Deep reinforcement learning; Knowledge-network-embedded; Training efficiency (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422401377X
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:energy:v:301:y:2024:i:c:s036054422401377x

DOI: 10.1016/j.energy.2024.131604

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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