Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50022657" target="_blank" >RIV/62690094:18470/24:50022657 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2666546824001125?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666546824001125?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.egyai.2024.100446" target="_blank" >10.1016/j.egyai.2024.100446</a>

Result language

angličtina

Original language name

Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning

Original language description

As the energy landscape evolves towards sustainability, the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid. One significant aspect of this issue is the notable increase in net load variability at the grid edge. Transactive energy, implemented through local energy markets, has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized, indirect demand response on a community level. Model-free control approaches, such as deep reinforcement learning (DRL), show promise for the decentralized automation of participation within this context. Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics, overlooking the crucial goal of reducing community-level net load variability. This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven, autonomous local energy market (ALEX). In this setting, agents do not share information and only prioritize individual bill optimization. The study unveils a clear correlation between bill reduction and reduced net load variability. The impact on net load variability is assessed over various time horizons using metrics such as ramping rate, daily and monthly load factor, as well as daily average and total peak export and import on an open-source dataset. To examine the performance of the proposed DRL method, its agents are benchmarked against a near- optimal dynamic programming method, using a no-control scenario as the baseline. The dynamic programming benchmark reduces average daily import, export, and peak demand by 22.05%, 83.92%, and 24.09%, respectively. The RL agents demonstrate comparable or superior performance, with improvements of 21.93%, 84.46%, and 27.02% on these metrics. This demonstrates that DRL can be effectively employed for such tasks, as they are inherently scalable with near-optimal performance in decentralized grid management.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Energy and AI

ISSN

2666-5468

e-ISSN

2666-5468

Volume of the periodical

18

Issue of the periodical within the volume

December

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

"Article Number: 100446"

UT code for WoS article

001396166300001

EID of the result in the Scopus database

2-s2.0-85209722255