Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting

no code implementations • 27 Feb 2023 • Arman Ghasemi, Amin Shojaeighadikolaei, Morteza Hashemi

Furthermore, the large-scale integration of distributed energy resources (such as PV systems) creates new challenges for energy management in microgrids.

Decision Making energy management +4

Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework

no code implementations • 29 Nov 2022 • Amin Shojaeighadikolaei, Arman Ghasemi, Kailani Jones, Yousif Dafalla, Alexandru G. Bardas, Reza Ahmadi, Morteza Haashemi

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.

energy management Management +2

A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids

no code implementations • 23 Sep 2020 • Arman Ghasemi, Amin Shojaeighadikolaei, Kailani Jones, Morteza Hashemi, Alexandru G. Bardas, Reza Ahmadi

This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid.

reinforcement-learning Reinforcement Learning (RL)

Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning

no code implementations • 23 Sep 2020 • Amin Shojaeighadikolaei, Arman Ghasemi, Kailani R. Jones, Alexandru G. Bardas, Morteza Hashemi, Reza Ahmadi

Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills.

Decision Making reinforcement-learning +1