Multi-agent systems in networked microgrids: Reinforcement learning and strategic pricing mechanisms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50022661" target="_blank" >RIV/62690094:18470/25:50022661 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0960148125013400?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0960148125013400?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.renene.2025.123678" target="_blank" >10.1016/j.renene.2025.123678</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-agent systems in networked microgrids: Reinforcement learning and strategic pricing mechanisms

Popis výsledku v původním jazyce

This study presents a novel, multi-layered approach for optimizing power transactions in networked microgrids using a multi-agent system framework. It incorporates peer-to-peer trading within microgrid clusters and a market based mechanism for inter-microgrid cluster transactions. To facilitate trading efficiency and market coordination, microgrids are first grouped into clusters based on similar load profiles and generation characteristics, enabling efficient intra-microgrid cluster energy balancing before engaging in inter-microgrid cluster trading. Within each microgrid cluster, microgrids operate autonomously, using local optimization to assess power surpluses and shortages, followed by multi-agent reinforcement learning to dynamically determine bid/ask prices. The proposed framework integrates a two-tiered trading mechanism. First, intramicrogrid cluster trading is facilitated through a proportional bargaining pricing model, ensuring fair power distribution among microgrids within the same microgrid cluster. Then, inter-microgrid cluster trading is optimized using a system marginal pricing mechanism, allowing microgrid clusters to efficiently sell surplus and buy shortage while minimizing grid dependency. Simulations using real-world data demonstrate substantial cost reductions and improved market efficiency. The proposed approach achieves a reduction of 43.9% in the annual surplus energy sold to the grid which reduces reliance on the utility grid by 7.1%. Additionally, annual electricity purchase costs from the grid and the cost of selling electricity to the grid are decreased by 7.5% and 44.6%, respectively. These improvements contribute to greater energy self-sufficiency, lower transaction costs, and enhanced economic fairness among microgrids. This framework provides a scalable, effective, and market-driven solution for power trading in networked microgrids by integrating microgrid clustering, local optimization, dynamic bid/ask price learning, and decentralized trading mechanisms. This improves operational resilience and economic viability of future distributed power markets.

Název v anglickém jazyce

Multi-agent systems in networked microgrids: Reinforcement learning and strategic pricing mechanisms

Popis výsledku anglicky

This study presents a novel, multi-layered approach for optimizing power transactions in networked microgrids using a multi-agent system framework. It incorporates peer-to-peer trading within microgrid clusters and a market based mechanism for inter-microgrid cluster transactions. To facilitate trading efficiency and market coordination, microgrids are first grouped into clusters based on similar load profiles and generation characteristics, enabling efficient intra-microgrid cluster energy balancing before engaging in inter-microgrid cluster trading. Within each microgrid cluster, microgrids operate autonomously, using local optimization to assess power surpluses and shortages, followed by multi-agent reinforcement learning to dynamically determine bid/ask prices. The proposed framework integrates a two-tiered trading mechanism. First, intramicrogrid cluster trading is facilitated through a proportional bargaining pricing model, ensuring fair power distribution among microgrids within the same microgrid cluster. Then, inter-microgrid cluster trading is optimized using a system marginal pricing mechanism, allowing microgrid clusters to efficiently sell surplus and buy shortage while minimizing grid dependency. Simulations using real-world data demonstrate substantial cost reductions and improved market efficiency. The proposed approach achieves a reduction of 43.9% in the annual surplus energy sold to the grid which reduces reliance on the utility grid by 7.1%. Additionally, annual electricity purchase costs from the grid and the cost of selling electricity to the grid are decreased by 7.5% and 44.6%, respectively. These improvements contribute to greater energy self-sufficiency, lower transaction costs, and enhanced economic fairness among microgrids. This framework provides a scalable, effective, and market-driven solution for power trading in networked microgrids by integrating microgrid clustering, local optimization, dynamic bid/ask price learning, and decentralized trading mechanisms. This improves operational resilience and economic viability of future distributed power markets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Renewable Energy

ISSN

0960-1481

e-ISSN

1879-0682

Svazek periodika

254

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

"Article Number: 123678"

Kód UT WoS článku

001513686600007

EID výsledku v databázi Scopus

2-s2.0-105008180265