[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 …

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 …

Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects

Y Li, C Yu, M Shahidehpour, T Yang… - Proceedings of the …, 2023 - ieeexplore.ieee.org
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 …

[HTML][HTML] Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints

C Samende, J Cao, Z Fan - Applied Energy, 2022 - Elsevier
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 …

Security constrained decentralized peer-to-peer transactive energy trading in distribution systems

L Wang, Q Zhou, Z **ong, Z Zhu… - CSEE journal of …, 2021 - ieeexplore.ieee.org
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 …

A real-time energy consumption minimization framework for electric vehicles routing optimization based on SARSA reinforcement learning

TM Aljohani, O Mohammed - Vehicles, 2022 - mdpi.com
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 …

Exploiting battery storages with reinforcement learning: a review for energy professionals

R Subramanya, SA Sierla, V Vyatkin - IEEE Access, 2022 - ieeexplore.ieee.org
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 …

Electricity Pricing and Its Role in Modern Smart Energy System Design: A Review

J Liu, H Hu, SS Yu, H Trinh - Designs, 2023 - mdpi.com
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 …

Coalitional demand response management in community energy management systems

N Kemp, MS Siraj, EE Tsiropoulou - Energies, 2023 - mdpi.com
With the advent of the Distributed Energy Resources within smart grid systems, traditional
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 …