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Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks
In this paper, we introduce a novel approach for distributed and cooperative charging strategy using a Multi-Agent Reinforcement Learning (MARL) framework.
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
The increasing trend in adopting electric vehicles (EVs) will significantly impact the residential electricity demand, which results in an increased risk of transformer overload in the distribution grid.
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting
Furthermore, the large-scale integration of distributed energy resources (such as PV systems) creates new challenges for energy management in microgrids.
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework
Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i. e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources.
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids
This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid.
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills.
