Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019900" target="_blank" >RIV/62690094:18470/22:50019900 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1996-1073/15/23/9032" target="_blank" >https://www.mdpi.com/1996-1073/15/23/9032</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/en15239032" target="_blank" >10.3390/en15239032</a>
Alternative languages
Result language
angličtina
Original language name
Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating
Original language description
It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ENERGIES
ISSN
1996-1073
e-ISSN
1996-1073
Volume of the periodical
15
Issue of the periodical within the volume
23
Country of publishing house
CH - SWITZERLAND
Number of pages
15
Pages from-to
"Article Number: 9032"
UT code for WoS article
000897540200001
EID of the result in the Scopus database
2-s2.0-85143767847