5 citations · 8 across the 4 of their papers we have counts for
4 papers
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting
Arman Ghasemi, Amin Shojaeighadikolaei, Morteza Hashemi
Renewable energy sources, such as wind and solar power, are increasingly being integrated into smart grid systems. However, when compared to traditional energy resources, the unpre…
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework
Amin Shojaeighadikolaei, Arman Ghasemi, Kailani Jones +4
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In…
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids
Arman Ghasemi, Amin Shojaeighadikolaei, Kailani Jones +3
This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dyna…
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
Amin Shojaeighadikolaei, Arman Ghasemi, Kailani R. Jones +3
Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques c…