[HTML][HTML] Smart grid for industry using multi-agent reinforcement learning

M Roesch, C Linder, R Zimmermann, A Rudolf… - Applied Sciences, 2020 - mdpi.com
The growing share of renewable power generation leads to increasingly fluctuating and
generally rising electricity prices. This is a challenge for industrial companies. However …

Multimicrogrid load balancing through EV charging networks

X Chen, H Wang, F Wu, Y Wu… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Energy demand and supply vary from area to area, where an unbalanced load may occur
and endanger the system security constraints and cause significant differences in the …

Exploring the Potential of Artificial Intelligence to Enhance Energy Efficiency in Smart Grid Systems: A Detailed Review and Future Directions

A Tan - Journal of Sustainable Urban Futures, 2023 - neuralslate.com
The rapid evolution of energy infrastructure, driven by increasing energy demands and the
integration of renewable energy sources, necessitates the transformation of traditional …

Optimal scheduling of electric vehicle charging with deep reinforcement learning considering end users flexibility

The rapid growth of decentralized energy resources and especially Electric Vehicles (EV),
that are expected to increase sharply over the next decade, will put further stress on existing …

Technical evaluation of plug-in electric vehicles charging load on a real distribution grid

B Hashemi, P Teimourzadeh Baboli - Distributed Computing and Artificial …, 2020 - Springer
The popularity of Plug-in Electric Vehicles (PEVs) in the last few years however is a turning
point toward alleviating the global warming, but the inevitable effects of charging load of …

Economic Dispatch of Consumer Loads Using Machine Learning in Smart Grid Environment

DN Asthana - 2019 - search.proquest.com
This work presents convincing results from research on implementation of prediction and
optimization models based on machine learning for ensuring economic load dispatch. It is …

[HTML][HTML] An artificial intelligence-based electric multiple units using a smart power grid system

Z Liu, Y Gao, B Liu - Energy Reports, 2022 - Elsevier
EMU stands for electric multiple units and refers to a train of self-propelled cars pushed by
electricity. Energy from renewable sources such as solar and wind can be stored in battery …

Intelligent Operation and Control of Microgrids Using Multiple Reinforcement Learning Agents

R Suryadevara - 2022 - etda.libraries.psu.edu
As the advantages of decentralized energy generation over centralized generation are now
widely realized, new distributed energy resources (DER) which include renewables are …

Real-Time metadata-driven routing optimization for electric vehicle energy consumption minimization using deep reinforcement learning and Markov chain model

TM Aljohani, A Ebrahim, O Mohammed - Electric Power Systems Research, 2021 - Elsevier
A real-time, data-driven electric vehicle (EVs) routing optimization to achieve energy
consumption minimization is proposed in this work. The proposed framework utilizes the …

[HTML][HTML] A stochastic deep reinforcement learning agent for grid-friendly electric vehicle charging management

CB Heendeniya, L Nespoli - Energy Informatics, 2022 - Springer
Electrification of the transportation sector provides several advantages in favor of climate
protection and a shared economy. At the same time, the rapid growth of electric vehicles …