2019
DOI: 10.1109/tsg.2018.2859821
|View full text |Cite
|
Sign up to set email alerts
|

Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
86
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 189 publications
(86 citation statements)
references
References 31 publications
0
86
0
Order By: Relevance
“…Many relevant studies have been conducted to improve the energy management and optimize the costs. Various techniques, such as fuzzy logic [63], cooperative game theory [64], genetic algorithms [65], and deep recurrent neural networks [66], have been proposed. Specifically, a reinforcement learning-based strategy (Fuzzy Q-learning) was recently proposed to improve the decision-making process of P2P power trading [67].…”
Section: Discussionmentioning
confidence: 99%
“…Many relevant studies have been conducted to improve the energy management and optimize the costs. Various techniques, such as fuzzy logic [63], cooperative game theory [64], genetic algorithms [65], and deep recurrent neural networks [66], have been proposed. Specifically, a reinforcement learning-based strategy (Fuzzy Q-learning) was recently proposed to improve the decision-making process of P2P power trading [67].…”
Section: Discussionmentioning
confidence: 99%
“…The authors in [96] design an interconnection topology and an RL-based algorithm to optimize the coordination of different energy storage systems (ESSs) in a microgrid. In [97], a novel dynamic energy management system is proposed to deal with microgrids real-time dispatch problems. The developed energy management system can optimize the long-term operational costs of microgrids without long-term forecast or distribution information about uncertainty.…”
Section: Aiot Perception Layer -Smart Gridmentioning
confidence: 99%
“…where P i,min DG and P i,max DG are the minimum and maximum output power of the ith DG, respectively. Given the output power P i DG (t) of the ith DG at time slot t, its operational cost can be calculated by using a conventional quadratic function model [25],…”
Section: Modeling Of Dgsmentioning
confidence: 99%
“…In a real-time electricity market, the electricity prices, or locational marginal price (LMP), are generally uncertain, and have an important impact on the management of MGs. In [25], Zeng et al proposed an approximate dynamic programming (ADP) approach to solve MG energy management considering the uncertainty of load demand, renewable generation, and real-time electricity prices, as well as the power flow constraints. A recurrent neural network (RNN) is designed to make one-step-ahead state estimation and approximate the optimal value function.…”
Section: Introductionmentioning
confidence: 99%