Optimal energy management strategies for energy Internet via deep reinforcement learning approach
By H. Hua, Yuchao Qin, Chuantong Hao, Junwei Cao. Applied Energy, 2019
295 citations recorded by the source corpus.
Abstract This paper investigates the energy management problem in the field of energy Internet (EI) with interdisciplinary techniques. The concept of EI has been proposed for a while. However, there still exist many fundamental and technical issues that have not been fully investigated. In this paper, a new energy regulation issue is considered based on the operational principles of EI. Multiple targets are considered along with constraints. Then, the practical energy management problem is formulated as a constrained optimal control problem. Notably, no explicit mathematical model for power of renewable power generation devices and loads is utilized. Due to the complexity of this problem, conventional methods appear to be inapplicable. To obtain the desired control scheme, a model-free deep reinforcement learning algorithm is applied. A practical solution is obtained, and the feasibility as well as the performance of the proposed method are evaluated with numerical simulations.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/e8e91f5a7b4d82c7e96a976e756a3af8de16b75d
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In Control and Systems Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Applications of reinforcement learning in energy systems (2021, methodology): “The modern deep actor-critic algorithms, such as A3C [99,112] and DDPG [82,100,146,159], have only been used by the works focusing on BMES (SP), dispatch (SP), vehicle energy systems (SP), and IP.”
- Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, unknown): “algorithm [151] for the online optimization of task offloading decision to adapt to the dynamic changes of the vehicular network.”