2024· International Journal of Modern Research in Science & Engineering· Vol 7, pp. 01-17· 0 citations
TL;DR
This paper proposes a Multi-Agent Reinforcement Learning (MARL)-based energy management framework in which intelligent agents coordinate renewable generation, energy storage, controllable loads, and grid interaction that optimizes energy scheduling, improves system reliability, and reduces operational cost and carbon emissions.
Abstract
The increasing integration of renewable energy sources and distributed energy resources has made smart microgrid energy management more complex due to renewable intermittency and dynamic operating conditions. This paper proposes a Multi-Agent Reinforcement Learning (MARL)-based energy management framework in which intelligent agents coordinate renewable generation, energy storage, controllable loads, and grid interaction. Through distributed learning and real-time decision-making, the framework optimizes energy scheduling, improves system reliability, reduces operational cost and carbon emissions, enhances renewable energy utilization, and provides a scalable, autonomous, and resilient solution for next-generation smart microgrids.
Smart grid incorporates the use of renewable energy, distributed generation, energy storage, and demand response for enhancing efficiency and sustainability. However, smart grid operation is challenging because of uncertainty of renewable generation, fluctuating electricity prices, changing consumer demand, and the req...
Sathiyamoorthy M· 2026 International Conferenc...· 0 citations
Experimental results demonstrate that the proposed DRL-based framework provides an effective and scalable solution for intelligent microgrid energy management and outperforms conventional rule-based strategies and model-based optimization approaches in terms of operational cost reduction, energy utilization efficiency,...
Li Chen, Hong-Qiao Li, Zhen-Xing Chen et al.· European Conference on Elect...· 0 citations
The Machine Learning-Based Intelligent Energy Management System (ML-IEMS) proposed in this paper combines a hybrid CNN-LSTM model for short-term load and renewable generation forecasting with a Reinforcement Learning dispatch agent for real-time storage, demand response, and grid exchange scheduling.
N. Suganthi, S. Tamilselvan· ITM Web of Conferences· 0 citations
Simulation results indicate that using RL to optimize BESS operation will improve the efficiency of dispatching energy, increase the percentage of renewable energy used, and decrease operating costs compared to traditional ways of controlling BESS.
Akhtam Uralov, Akmaljon Aliboyev, Nargiza Nazarova et al.· EPJ Web of Conferences· 0 citations
High renewable penetration makes microgrid energy management sensitive to uncertain photovoltaic output, wind fluctuation, load variation, electricity price, and battery degradation. Conventional rule-based and model predictive strategies require manually tuned thresholds or accurate forecasts, which limits their adapt...
Feng Long, Shang-Zhi Sun, Min-Zhang Jiang et al.· International Conference on...· 0 citations
Microgrids must efficiently manage energy under uncertainties in renewable generation and load demand to ensure reliable and cost-effective operation. This paper investigates a microgrid system that involves renewable energy through the photovoltaic system, wind system, battery energy storage, and local load requiremen...
S. Sreekanth, P. Kiran· International Conference on...· 0 citations
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