Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning

04/28/2019
by   Xu Hanchen, et al.
0

In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy that maps a set of available information processed by a recurrent neural network to ESSs' charging/discharging actions. Finally, we verify the effectiveness of our algorithm using real-time electricity prices from PJM.

READ FULL TEXT

page 1

page 2

page 3

research
12/13/2022

Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets

Driven by the global decarbonization effort, the rapid integration of re...
research
03/11/2022

A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets

Prosumer operators are dealing with extensive challenges to participate ...
research
06/13/2023

Multi-market Energy Optimization with Renewables via Reinforcement Learning

This paper introduces a deep reinforcement learning (RL) framework for o...
research
03/26/2019

Energy Storage Management via Deep Q-Networks

Energy storage devices represent environmentally friendly candidates to ...
research
07/09/2020

Distributed Energy Trading and Scheduling among Microgrids via Multiagent Reinforcement Learning

The development of renewable energy generation empowers microgrids to ge...
research
03/28/2022

REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework

In this work a general framework is proposed to support the development ...
research
10/04/2022

Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control

With the recent advances in mobile energy storage technologies, electric...

Please sign up or login with your details

Forgot password? Click here to reset