[HTML][HTML] Advances of machine learning in multi-energy district communities‒mechanisms, applications and perspectives
Y Zhou - Energy and AI, 2022 - Elsevier
Energy paradigm transition towards the carbon neutrality requires combined and continuous
efforts in cleaner power production, advanced energy storages, flexible district energy …
efforts in cleaner power production, advanced energy storages, flexible district energy …
Cloud-based energy management systems: Terminologies, concepts and definitions
JCM Siluk, PS de Carvalho, V Thomasi… - Energy Research & …, 2023 - Elsevier
The evolution of energy systems has placed end users in a central role in dynamic, flexible
and decentralised cloud-based energy management models. Different terms have been …
and decentralised cloud-based energy management models. Different terms have been …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
[HTML][HTML] Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints
In this paper, we investigate an energy cost minimization problem for prosumers
participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable …
participating in peer-to-peer energy trading. Due to (i) uncertainties caused by renewable …
Security constrained decentralized peer-to-peer transactive energy trading in distribution systems
Peer-to-peer (P2P) transactive energy trading offers a promising solution for facilitating the
efficient and secure operation of a distribution system consisting of multiple prosumers. One …
efficient and secure operation of a distribution system consisting of multiple prosumers. One …
A real-time energy consumption minimization framework for electric vehicles routing optimization based on SARSA reinforcement learning
A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy
requirements is proposed in this work. The proposed strategy employs the state–action …
requirements is proposed in this work. The proposed strategy employs the state–action …
Exploiting battery storages with reinforcement learning: a review for energy professionals
The transition to renewable production and smart grids is driving a massive investment to
battery storages, and reinforcement learning (RL) has recently emerged as a potentially …
battery storages, and reinforcement learning (RL) has recently emerged as a potentially …
Electricity Pricing and Its Role in Modern Smart Energy System Design: A Review
Energy is the foundation for human survival and socio-economic development, and
electricity is a key form of energy. Electricity prices are a key factor affecting the interests of …
electricity is a key form of energy. Electricity prices are a key factor affecting the interests of …
Coalitional demand response management in community energy management systems
With the advent of the Distributed Energy Resources within smart grid systems, traditional
demand response management (DRM) models need to be redesigned to capture …
demand response management (DRM) models need to be redesigned to capture …
Multi-time scale optimal configuration of user-side energy storage considering demand perception
H Wang, F Wang, D Han, W Sun - Renewable Energy, 2024 - Elsevier
The promotion of user-side energy storage is a pivotal initiative aimed at enhancing the
integration capacity of renewable energy sources within modern power systems. However …
integration capacity of renewable energy sources within modern power systems. However …